VALIDATION OF THE MINISUN IDEEA DATA RECORDER FOR THE ANALYSIS OF WALKING ON UNEVEN GROUND. Thesis. Submitted to. The School of Engineering of the

Transcription

1 VALIDATION OF THE MINISUN IDEEA DATA RECORDER FOR THE ANALYSIS OF WALKING ON UNEVEN GROUND Thesis Submitted to The School of Engineering of the UNIVERSITY OF DAYTON In Partial Fulfillment of the Requirements for The Degree of Master of Science in Mechanical Engineering By Mark Tyler Rigsby Dayton, OH December, 2011

2 VALIDATION OF THE MINISUN IDEEA DATA RECORDER FOR THE ANALYSIS OF WALKING ON UNEVEN GROUND Name: Rigsby, Mark Tyler APPROVED BY: Kimberly E. Bigelow, Ph.D. Advisory Committee Chairman Assistant Professor Mechanical and Aerospace Engineering George R. Doyle, Jr., Ph.D. Committee Member Professor Mechanical and Aerospace Engineering Harold Merriman, PT, Ph.D. Committee Member Assistant Professor Doctor of Physical Therapy Program Health & Sports Science John G. Weber, Ph.D. Associate Dean School of Engineering Tony E. Saliba, Ph.D. Dean, School of Engineering & Wilke Distinguished Professor ii

3 ABSTRACT VALIDATION OF THE MINISUN IDEEA DATA RECORDED FOR THE ANALYSIS OF WALKING ON UNEVEN GROUND Name: Rigsby, Mark Tyler University of Dayton Advisor: Dr. Kimberly E. Bigelow Recent developments in portable systems used to analyze human walking have made it possible to collect information about gait in the field, as opposed to studying walking under controlled laboratory conditions. One wearable system, the Intelligent Device for Energy Expenditure and Activity System (IDEEA, MiniSun, LLC), has been reported to record gait parameters with accuracy comparable to common laboratory analysis systems when used to analyze gait on even terrain. This study examined the validity of using the MiniSun IDEEA to study human gait when walking upon an uneven surface. The results obtained using the IDEEA were compared to a proven gait analysis method using a triaxial accelerometer placed on an individual s lower back. Thirty two healthy individuals were recruited for this study in two age groups: Sixteen people aged years and sixteen people aged years. Each individual walked six, fifty meter walking trials: three while walking on even ground and three iii

4 while walking on uneven ground. Information on gait speed, step length, cadence, and gait cycle duration was calculated using raw acceleration patterns and recorded by the IDEEA for each walking trial. The results obtained using the accelerometer and IDEEA on even and uneven ground were compared for each age group using paired t-tests with α=0.05. The results showed that that the IDEEA was not influenced by the type of surface walked upon. However, the values recorded by the IDEEA for speed, step length, and cadence on even and uneven surfaces were significantly different than the raw acceleration calculations (p <.05). These results indicate that the IDEEA is not accurate in recording these gait parameters regardless of surface type. The IDEEA showed high levels of agreement with the raw accelerometer calculations for the recorded values of gait cycle duration on both surfaces (p >.05), which may be indicative of the system correctly recording moments of foot contact. Modifications may need to be made by the manufacturer in order to improve its ability to accurately analyze gait. As an extension of this study, the use of the IDEEA with people who had an above the knee amputation was also examined. Two people with an above the knee amputation who used a prosthetic limb with a microprocessor controlled knee joint participated in this experiment. The IDEEA recorded inaccurate results for one subject and malfunctioned for the other. Although additional research could be conducted to examine the system s accuracy, the results of the healthy population in this study indicate a serious concern with the IDEEA s ability to accurately record gait parameters. As a result, the use of this system is not recommended in lieu of other proven methods of analyzing gait. iv

5 Dedicated to my family v

6 ACKNOWLEDGEMENTS There were a number of individuals who shared their knowledge and expertise with me in order to provide support for the completion of this project. I remain grateful for all they did to aid me in my research. I would first and foremost like to thank my advisor, Dr. Kimberly E. Bigelow. Over the past two years, she has shown me how to properly conduct independent research. There were a number of technical difficulties at the outset of this project, and her input along the way was a catalyst to its completion. I will always be appreciative of her continual inspiration to improve upon my own ideas while allowing me the freedom to select my own area of research. I would also like to thank Dr. Wiebke Diestelkamp, who served as a statistical consultant for the results that were obtained in this study. I thank all of the willing volunteers who participated in this study. I would also like to thank all of the members of the University of Dayton Engineering Wellness and Safety Lab who aided in the completion of this project, especially Dan Petit. I would also like to thank all of my friends who provided support during the rigorous process of conducting this study and completing the written thesis. At many points in time, the company of Mike Weisman, Sean Quirk, Andrew Babey, Cory Miller, vi

7 Michael Franz, Nicholas Beffa, Robby Zastudil, and Greg Titgemeyer kept me down to earth. They were always around when it was time for a much-needed break. Words cannot begin to describe the support that my family provided over the course of my graduate career. Mom and Dad were always there to listen to my successes and failures and to keep me focused on what is important in life. My sister, Morgan, always asked how I was doing and provided me with a laugh whenever I needed one. My cousins Chad and Gary, who are like brothers to me, were always encouraging and reminded me to be proud of each accomplishment I made. vii

8 TABLE OF CONTENTS ABSTRACT...iii DEDICATION...v ACKNOWLEDGEMENTS vi LIST OF FIGURES...xiii LIST OF TABLES...xxi CHAPTERS: 1. INTRODUCTION Objectives of the Current Study Overview of the Chapter Driving Need: Do Advancements in Prosthetic Componentry Correlate to an Improvement in Community Ambulation for the Growing Number of Lower Limb Amputees? The Growing Problem of Lower Limb Amputations in the United States The Significance of the Level of Amputation The Basic Design of the Prosthetic Leg for a Transfemoral Amputee Prosthetic Knee Joint...8 viii

9 1.3.4 A Review of the Literature Comparing Passive and Active Prosthetic Knees Human Gait: Normal, Healthy Walking and How It Compares to that of Transfemoral Amputees During Indoor, Level Walking Introduction to Anatomical and Gait Terminology The Gait Cycle: A Cyclic Representation of Human Walking Transfemoral Amputee Gait Additional Activities of Transfemoral Amputee Gait Gait Analysis: Advances in Technology Have Made It Possible for Human Walking to be Quantifiably Examined Outside of the Laboratory Observational Gait Analysis: A Qualitative Approach to Identifying Deviations from Normal Gait by Visual Observation Quantifying Human Gait through Three Dimensional, Marker Based Gait Analysis An Alternative Approach: Gait Analysis using Kinematic Sensors A New Opportunity: Accelerometers Create the Potential to Address Amputee Gait Needs by Monitoring Real-World Ambulation A Review of Accelerometry The Use of Accelerometers to Analyze Gait A Novel Accelerometer Based Gait Analysis System: The Intelligent Device for Energy Expenditure and Activity System (IDEEA )...31 ix

10 A Review of the Literature Examining the IDEEA Motivation of Current Work METHODOLOGY Gait Parameters Analyzed Factors Considered to Influence Gait Personal/Demographic Factors Environmental Factors Experimental Design Subject Recruitment Subject Population Gold Standard: Triaxial Accelerometer Intelligent Device for Energy Expenditure and Physical Activity Experimental Protocol Data Analysis Extracting Gait Data from the MiniSun IDEEA Gait Analysis using the APDM Opal Extracting Raw Acceleration Data from the Opal Transforming the Data from a Tilted Accelerometer to a Horizontal-Vertical Coordinate System Conducting a Gait Analysis using Raw Accelerations Statistical Analysis Investigating the Use of the IDEEA with Above the Knee Amputees Amputee Subject Recruitment...80 x

11 Using the IDEEA with People Who Use a Prosthetic Limb RESULTS Raw Accelerometer Signal Influence of Surface and Age on Walking Parameters Differences between the MiniSun IDEEA and APDM Opal Nuisance Factors The Use of the IDEEA with People Who Ambulate with a Prosthetic Limb DISCUSSION OF RESULTS Raw Accelerometer Signal Influence of Surface and Age on Walking Parameters Differences between the MiniSun IDEEA and APDM Opal Nuisance Factors The Use of the IDEEA with People Who Ambulate with a Prosthetic Limb Limitations of Current Work Direction of Future Work REFERENCES APPENDIX A: SCREENING QUESTIONNARIES A.1 Screening Questionnaire for Control Subjects A.2 Screening Questionnaire for Subjects with an Above the Knee Amputation xi

12 APPENDIX B: SAMPLE MATLAB CODE USED TO ANALYZE RAW ACCELERATIONS xii

13 LIST OF FIGURES Figure Page 1.1 Different types of major lower limb amputations based on the level of amputation. Each amputation is denoted by a solid line crossing the general area of amputation. The location of the femur, fibula, and tibia have also been denoted An example of an above the knee prosthesis. This particular prosthetic limb has prosthetic ankle function built into the prosthetic foot The three planes and axes of motion of the human body. The transverse plane (TP), frontal plane (FP), and sagittal plane (SP) are shown segmenting the body. The anteroposterior axis (AA), mediolateral axis (MA), and longitudinal axis (LA) are shown in relation to the three planes Step length versus stride length. One stride of the right foot and one left step are shown The terminology for the 8 phases of gait developed by the Rancho Los Amigos Gait Analysis Committee A depiction of the basic construction of a single axis accelerometer (traditional spring-mass-damper system)...25 xiii

14 1.7 A depiction of a simple form of a variable capacitor. One plate which is attached to the silicon mass is installed a certain distance away from a plate which is fixed to the accelerometer frame. As the mass moves closer to and farther away from the frame, the variable gap between the two plates changes and results in a change in capacitance. Even when stationary, capacitive accelerometers are able to measure the pull of gravity on a body segment because of the change created in the variable gap The five sensors and data collector shown with respect to a nickel for size comparison The APDM Opal and the elastic belt which is provided by the manufacturer. The positive x- and y-axes are shown in the figure. The positive z-axis is going into the page. When attached to an individual s lower back, the x-axis corresponds to the mediolateral direction, the y-axis corresponds to the vertical direction, and the z-axis corresponds to the anteroposterior axis in a left handed coordinate system A plot of and measured along the z-axis. As identified by the equation of the line fit through the two points, the sensitivity is the slope of the line, and the y-intercept is the zero-offset The correct placement of the chest accelerometer as shown on an individual in the IDEEA manual. The chest sensor was placed approximately 4 cm below the inside of the clavicles...51 xiv

15 2.4 The correct placement of the thigh accelerometers, as shown in the IDEEA manual. The thigh sensors were placed on top of the thigh, approximately half way between the knee and hip joints The proper location of the IDEEA foot sensors, as indicated in the IDEEA manual. Each sensor is placed approximately 2 cm below the head of the 4 th metatarsal, as shown An example of the APDM Opal placed over the sacrum using the elastic belt provided by the manufacturer A graph of the resulting speed and activity types recorded during a walking trial. Speed is measured in m/s, and activity type is measured by the percentage of time which the activity occurred over the time period selected. The activity type listed, from top to bottom, are standing while moving the left leg (stlm), transition from one activity to another (tran), standing (stnd), and walking (wlk). The transition and standing while moving the left leg correspond to the period of time before the second acceleration peak shown in Figure 2.9. The transition period at the end occurs after the final peak shown in Figure An outline of the gait cycle defined by the forward and backward acceleration pattern of the right foot. Moments of heel contact can be identified by large peaks in the pattern...63 xv

16 2.9 The first four forward and backward acceleration peaks during a walking trial. Accelerations of both the right and left feet are shown. The data before the second peak was discarded such that the walking trial started at heel contact of the second step The last five forward and backward acceleration peaks of the walking trial. The data after the fourth-to-last peak were discarded. The last peak shown is the result of the trail leg being brought forward A graph of the measured acceleration relative to the AP direction,, plotted with the measured vertical acceleration,. The AP axis is in the transverse plane. Since the angle between and is 90, it follows that the angle between and the AP axis,, is the same angle between and the vertical axis A graph of the measured acceleration relative to the ML direction,, plotted with the vertical acceleration,. The ML axis is in the transverse plane. Since the angle between and is 90, it follows that the angle between and the ML axis,, is the same angle between and the vertical axis An example of acceleration due to gravity being measured in the AP direction due to an initial offset of the accelerometer. Since the vertical axis of the accelerometer is not aligned with the gravity vector, a component of gravity is recorded in the AP direction...71 xvi

17 2.14 One Complete Gait Cycle in the AP Direction. The moment of heel strike is at the peak value before the drop in acceleration caused by heel strike. The data points in the graph identify these peaks in the figure A graph of the typical accelerations measured in the AP direction at the start of a walking trial. The first and second steps taken have been identified by data points. The beginning of the walking trial is at heel contact of the second step, or the second identified peak in the graph A graph of the typical accelerations measured in the AP direction at the end of a walking trial. The action of bringing the trail leg forward at the end of the trial can be seen by the small increase in acceleration between 37.8 and 38.5 seconds in the graph. The peak denoting the end of the walking trial has been identified by a data point A depiction of the two-factor factorial experiment in this study. Each corner point represents one test combination of age group and type of walking surface. Y: Subject Aged 18-38, O:Subject Aged 45-65, E: Even Ground, U:Uneven Ground A depiction of the two measurements that were taken on the intact foot to locate the MiniSun foot sensor on an amputee subject s prosthetic foot. The horizontal measurement (H intact ) is from the inside of the foot to the innermost side of the foot sensor. The vertical measurement (V intact ) is from the heel to the bottommost side of the foot sensor...84 xvii

18 3.1 A representative acceleration pattern for the age group while walking on an even surface. A five second bout of walking is shown in the figure, during which 11 instances of heel contact occurred A representative acceleration pattern for the age group while walking on an uneven surface. A five second bout of walking is shown in the figure, during which 10 instances of heel contact occurred A representative acceleration pattern for the age group while walking on an even surface. A five second bout of walking is shown in the figure, during which 10 instances of heel contact occurred A representative acceleration pattern for the age group while walking on an uneven surface. A five second bout of walking is shown in the figure, during which 9 instances of heel contact occurred An example of an erratic acceleration pattern. This signal was observed in the age group while the subject walked on an even surface. A five second bout of walking is shown in the figure, during which 11 instances of heel contact occurred. There is a much larger variation in the shape of this subject s acceleration pattern than normally observed in other subjects A graph of walking speed vs. age of study participants. The age of study participants was coded in the ANOVA such that -1.00=45-65 years and 1=18-38 years. The error bars are an indication of the 95% confidence interval in which the results lie. These bars do not overlap in the figure, indicating a significant difference between the average speed for each age group...96 xviii

19 3.7 A graph of cadence vs. surface walked upon. The surface walked upon was coded in the ANOVA such that -1.00=even ground and 1=uneven ground. The error bars are an indication of the 95% confidence interval in which the results lie. These bars do overlap in the figure, indicating that there was no significant difference between the average cadence observed on each surface A graph of gait cycle duration vs. age of study participants. The age of study participants was coded in the ANOVA such that -1.00=45-65 years and 1=18-38 years. The error bars are an indication of the 95% confidence interval in which the results lie. These bars do not overlap in the figure, indicating a significant difference between the average cadence for each age group A graph of gait cycle duration vs. surface walked upon. The surface walked upon was coded in the ANOVA such that -1.00=even ground and 1=uneven ground. The error bars are an indication of the 95% confidence interval in which the results lie. These bars do overlap in the figure, indicating that there was no significant difference between the average duration of the gait cycle observed on each surface A graph of gait cycle duration vs. age of study participants. The age of study participants was coded in the ANOVA such that -1.00=45-65 years and 1=18-38 years. The error bars are an indication of the 95% confidence interval in which the results lie. These bars do not overlap in the figure, indicating that there was a significant difference between the average duration of the gait cycle observed for each age group xix

20 3.11 A representative acceleration pattern of a person with an above the knee amputation recorded in the anteroposterior direction. The small peaks are heel contacts of the prosthetic limb while the larger peaks are those of the nonamputated limb An example of the IDEEA not recording walking correctly during the testing of the second amputee subject. From top to bottom, the activities listed are strm: Standing while moving the right leg; stlm: Standing while moving the left leg; tran: Transition; stnd: standing; godn: Going down stairs; wlk: Walking. The IDEEA misrepresent walking as going down stairs during the walking trial xx

21 LIST OF TABLES Table Page 1.1 Descriptions of Each Type of Lower Limb Amputation Based on the Level of Amputation A Description of Different Types of Commercially Available Microprocessor Controlled Prosthetic Knees A Description of Each of the 8 Phases of Gait Developed by the Rancho Los Amigos Gait Analysis Committee with Characteristics of Normal Gait Shown Subject Characteristics of Non-Amputee Subjects Technical Specifications of the APDM Accelerometer Sensitivity and Zero-Offset Data of the APDM Opal Triaxial Accelerometer Technical Specifications of the IDEEA Characteristics of Subjects with Above the Knee Amputations Peak and RMS Accelerations Recorded in the Anteroposterior Direction (g) ANOVA Results for Peak Acceleration Recorded in the Anteroposterior Direction ANOVA Results for RMS Acceleration Recorded in the Anteroposterior Direction The ANOVA Table for Gait Speed Estimated Using the Opal...95 xxi

22 3.5 The ANOVA Table for Step Length Estimated Using the Opal The ANOVA Table for Cadence Estimated Using the Opal The ANOVA Table for for Gait Cycle Duration Estimated Using the Opal Results of the Paired T-Test for Recorded Gait Parameters: Age on Even Ground Results of the Paired T-Test for Recorded Gait Parameters: Age on Even Ground Results of the Paired T-Test for Recorded Gait Parameters: Age on Uneven Ground Results of the Paired T-Test for Recorded Gait Parameters: Age on Uneven Ground Intra-class Correlation Coefficients for Results Obtained from the MiniSun IDEEA and APDM Opal The ANOVA Table for the Impact of Nuisance Factors on Speed The ANOVA Table for the Impact of Nuisance Factors on Step Length The ANOVA Table for the Impact of Nuisance Factors on Cadence The ANOVA Table for the Impact of Nuisance Factors on Gait Cycle Duration Recorded Gait Parameters for Amputee Subjects Gait Parameters Recorded for Subject 1 Using the IDEEA xxii

23 CHAPTER 1 INTRODUCTION The increasing number of lower limb amputations in the United States has spurred the development of more technologically advanced prosthetic components to better suit active lifestyles. Today, the most innovative prosthetic knees utilize sensors and computer technology in order to return amputees as closely as possible to their premorbid, functioning selves. Though there are many claimed advantages of these cutting edge knees, there are mixed reports on the validity of these claims as to the extent computer aided knees assist lower limb amputees, particularly in walking upon uneven surfaces. 1-3 Overcoming environmental barriers such as irregular terrain is critical for an individual s mobility and quality of life. 4,5 Previous studies have examined this issue, but mainly in a laboratory environment, which has been shown to be an ill representation of an individual s true gait. 6,7 In recent years, wearable sensors have been proposed as a means to quantitatively analyze human walking in real-world situations. 2,6,8-10 There is a necessity to verify that these portable sensors are reliable to analyze human walking in real life scenarios. Subsequently, these sensors may then be used to observe the walking styles of above the knee amputees in these environments by researchers and clinicians. 1

24 1.1 Objectives of the Current Study The purpose of the present study is twofold. First and foremost, the current work was intended to investigate the validity of using the Intelligent Device for Energy Expenditure and Activity System (IDEEA, MiniSun, LLC), a novel wearable system, to record the walking characteristics of people on uneven ground. This system is highly portable and capable of analyzing human walking outside of the laboratory in real world situations. Previous studies had analyzed the reliability of the IDEEA with promising results, but this system had yet to be tested with individuals walking upon uneven ground The present work, therefore, sought to investigate the system s accuracy by comparing the data from the IDEEA to an accepted and proven method of analyzing human walking. In order to encourage data that was truly representative of human ambulation, walking parameters were observed outside of a laboratory. As an additional aim, this study sought to demonstrate feasibility for the use of the IDEEA system with above the knee amputees. Again, the walking characteristics recorded with the IDEEA were compared to a proven method of analyzing human walking. 1.2 Overview of the Chapter This chapter outlines background information that will allow for an increased understanding of the objectives of this study. First, the motivation behind this study to examine the gait of people with a unilateral above the knee amputation outside of the 2

25 laboratory is explained. Then, information on analyzing gait and the use of portable sensors in studying gait is presented. With this knowledge in mind, a short review of the studies which have examined the IDEEA thus far is presented at the close of this chapter. 1.3 Driving Need: Do Advancements in Prosthetic Componentry Correlate to an Improvement in Community Ambulation for the Growing Number of Lower Limb Amputees? The Growing Problem of Lower Limb Amputations in the United States An estimated 1.2 million people were living with an amputation in 1996 according to the National Health Interview Survey (NHIS). 16,17 Though questions that elicit estimates of the amputee population have since been removed from the survey, research conducted to estimate the number of people living with limb loss indicates that the number is growing and expected to reach 3.6 million by ,17 In this population, there are approximately 700,000 individuals currently living with some form of major lower limb amputation (excluding toes). 16 This value is expected to climb to 1.5 million by 2050, but could be much higher due to the increasing rate of obesity and its related trend of diabetes and vascular disease. 16,17 As the rate of lower limb amputations continues to grow, there is a need to focus research in this area. 3

26 1.3.2 The Significance of the Level of Amputation Figure 1.1 illustrates several types of lower limb amputations based on the level of amputation. General descriptions of each type of amputation can be found in Table A lower limb amputation presents different functional limitations depending on the proximity of the amputation to the body s trunk. In the case of transfemoral amputees (TFAs), the length of the residual, or remaining, limb is of great importance. While it is true that a longer limb will result in a greater moment arm, the length of the stump will also determine the options one has for prostheses. 5 A knee disarticulation would preserve the maximum amount of limb length as compared to a transfemoral amputation and provide a weight-bearing surface for the residual limb. 5 Even though this is true, limited Figure 1.1 Different types of major lower limb amputations based on the level of amputation. 18 Each amputation is denoted by a solid line crossing the general area of amputation. The location of the femur, fibula, and tibia have also been denoted. 4

27 Table 1.1 Descriptions of Each Type of Lower Limb Amputation Based on the Level of Amputation Amputation Hemipelvectomy Hip Disarticulation Transfemoral/Above Knee (TFA/AKA) Knee Disarticulation Transtibial/Below Knee (TTA/BKA) Symes Description Removal of part of the pelvis and the lower limb Amputation of the lower limb through the hip joint Amputation through the femur Amputation through the knee joint Amputation through the tibia and fibula Amputation through the ankle joint prosthetic options exist for those with disarticulations. Due to the irregular shape and length of the distal end of the residual limb, prosthetic knee options are limited for those with knee disarticulations. 5,19 In addition, there is an increased risk for tissue failure on the weight bearing surface. 5,19 For these reasons, transfemoral amputations are much more common, despite losing a greater respective portion of the limb. In addition to bone loss, the main limitation of any amputation is the loss of musculature that accompanies the loss of limb segments, with more musculature being lost as the level of amputation increases. People with a transfemoral amputation are at a particular disadvantage because of the complete loss of musculature below the knee, removing all ankle and knee function. 5,19 In order to replace these ambulatory demands, individuals with a transfemoral amputation have to rely on prosthetic componentry. In fact, amputees prefer a prosthetic limb because it helps with recovery time and with 5

28 performing activities of daily living (ADL), which include dressing, eating, and moving around independently. 20 Regardless of the level of amputation, the key to a successful rehabilitation is to restore the individual to their previous state of independently ambulating within and outside of their home. 21 Geertzen furthers this idea in saying, The aim after amputation is to bring the patient to an optimal level of mental, physical, emotional, social, vocational, and economic efficiency. 4 Because this process is so complex following the loss of a limb, a typical rehab team will exist of professionals in multiple fields, including physiatry, surgery, medicine, physical therapy, occupational therapy, nursing, mental health, social work, and prosthetics. 5,22 A major hindrance in the rehabilitation process after a lower limb amputation is the extra energy consumption related to ambulating with a prosthetic limb. 2,23-26 Due to the musculature lost to amputation, the remaining muscles must exert more effort to compensate and in some cases perform different actions than normal. 5,22,27 Navigating environmental challenges, such as uneven terrain, put even greater demands on the effort an amputee must put forth. 1,4,5,28-30 More advanced prosthetic components have been shown to aid in reducing the amount of energy required to ambulate with a prosthesis and help a person traverse environmental challenges. 2,24,25,28, The Basic Design of the Prosthetic Leg for a Transfemoral Amputee Lower limb prostheses for people with an above the knee amputation consist of six basic components: socket, suspension mechanism, pylon, ankle, foot, and knee 6

29 joint. 5,31 An example of such a prosthetic limb is shown in Figure 1.2. The prosthetic socket connects the residual limb to the prosthetic leg and transmits force from the limb to the prosthesis. The suspension mechanism holds the socket firmly to the residual limb in an effort to prevent any unwanted slippage. The pylon serves as the structural replacement for the tibia and fibula, which are the bones between the ankle and knee which are lost in a transfemoral amputation. The prosthetic ankle, foot, and knee are designed to replicate the normal function of the leg during ambulation. 5 In recent years, the prosthetic knee joint has undergone rapid transformation with the introduction of new technology. Figure 1.2 An example of an above the knee prosthesis. 5 This particular prosthetic limb has prosthetic ankle function built into the prosthetic foot. 7

30 Prosthetic Knee Joint Innovations for the prosthetic knee joint have been under rapid development over the past two decades In general, the prosthetic knee joint must aid in support when a load is placed on the prosthetic limb and allow a smooth progression when an individual swings their leg forward to take to take a step. 23 The knee joint must also permit unrestricted motion for actions such as sitting and kneeling. 5 Previously, people with a transfemoral amputation were required to walk with a fully extended, locked knee when the prosthetic limb was in contact with the ground in order to prevent buckling. This attribute is a hindrance because the lack of flexion does not provide any weight acceptance or cushioning when the affected limb strikes the ground. 32 Allowing the knee to flex slightly during contact with the ground also improves overall stability by shortening the amount of time between initial contact of the heel with the ground and the when the foot becomes flat on the ground. 32 This type of knee flexion is beneficial because it normalizes the vertical movement of the body s center of mass for a more normal gait. 32 Today there are a variety of prosthetic knee options available depending on the needs of the amputee. 5,31,35 The most basic type of prosthetic knee is a passive, single axis knee, in which the knee flexes and extends with respect to only one axis and without electronic assistance. Normally some type of friction mechanism is installed in passive knees in order to control the movement of the shank during swing, dampening excessive knee flexion and extension. 35 Mechanical resistance, achieved by a sleeve or bushing, provides a constant resistance to motion and can be easily adjusted. 35 Pneumatic and 8

31 hydraulic resistance mechanisms on the other hand allow for some variable cadence, the rate at which a person takes steps, because the resistance to motion is proportional to the speed at which the user of the prosthesis walks. 35 The newest development in prosthetic knees is the use of microprocessors to aid in the control of prosthetic limbs. Unlike passive knees, these active knees have the ability to continuously correct and control the motion of the knee during gait up to 1000 times per second through hydraulics, pneumatics, and servomotors. 5 This improves the responsiveness of the prosthesis while the limb is swinging, increases overall gait symmetry, and makes the amputee feel confident while ambulating. 31,32,36 Computerassisted knees also allow for variable cadence and absorb some of the energy that would be normally seen by the quadriceps and hamstrings. 31 Recently fabricated microprocessor-controlled knees and their descriptions can be found in Table ,19,34 9

32 Table 1.2 A Description of Different Types of Commercially Available Microprocessor Controlled Prosthetic Knees Type of Knee Intelligent Knee Prosthesis (IP) Endolite Adaptive Prosthesis Otto Bock C-Leg Ossur Rheo Knee Description First commercially available microprocessor knee. Electronically controls the prosthetic limb while it is not in contact with the ground using a pneumatic cylinder. The cylinder valve opening is dependent upon gait speed. Microprocessor takes measurements 62.5 times per second. Hydraulic valves control the limb while it is in contact with the ground. A pneumatic component controls the limb while it is in the air. Also has stumble control and voluntary locking mechanisms. Microprocessor takes measurements at a rate of 50 times per second to electro-mechanically control the knee throughout gait using a hydraulic cylinder. Also equipped with a stumble control mechanism. Microprocessor takes measurements 1000 times per second to control a magnetorheoligal fluid (metal particles suspended in fluid). The resistance of the knee is changed by the fluid passing through magnetic fields in order to change viscosity. Expensive A Review of the Literature Comparing Passive and Active Prosthetic Knees Though research into prosthetic microprocessor knees is becoming more prevalent, there is still limited information which analyzes their benefit against more conventional, non-microprocessor controlled (NMC) knees in assisting amputees while walking over uneven terrain. The findings of previous studies which have compared the performance of the two types of knees on level ground suggest that above the knee 10

33 amputees walk with a more natural, confident gait while using a microprocessor controlled prosthetic knee. 2,36-39 In comparing the C-Leg to a NMC knee (Mauch SNS), Orendurff et al. found a faster self selected walking speed when using the C-Leg for a small cohort of subjects. 37 Segal et al. also examined the C-Leg versus the Mauch SNS in a 3D gait lab environment and found an increase in self-selected walking speed. 38 However, a study by Johansson et al. contradicted this by finding no significant difference in walking speed or step length between the C-Leg and the Mauch SNS in a study that compared the Otto Bock C-Leg, Ossur Rheo Knee, and Mauch SNS. 2 These initial results of comparisons to microprocessor and non-microprocessor knees point to the fact that the potential benefits of microprocessor knees need to be studied further. The main advantage in the performance of microprocessor knees over NMC knees may lie in activities outside of walking on level ground. 1 Users of microprocessor knees report that ambulation is less tiring and it is easier to maneuver around obstacles and upon uneven surfaces. 19,32 Seymour et al. analyzed the performance of transfemoral amputees using the C-Leg against various NMC knees while navigating an obstacle course which was constructed indoors. 26 Consistent with the findings of Orendurff and Segal, the data showed that subjects completed the obstacle course in less time while using the C-Leg than with NMC knees. 26 However, the only three studies to date which have compared the performance of a microprocessor knee to a passive knee on uneven ground outside of a laboratory simply measured the time taken to complete a walking trial. 1,28,39 This does not take into account other variables such as step length and cadence, as are seen in studies which examine the two knees on even ground. 1,2,27,38 Separating the gap of knowledge even further, the 11

34 results of these three studies are not in agreement. Hafner et al. did not find a significant difference in walking speed between microprocessor and non-microprocessor controlled knees. 1 When testing subjects on uneven ground in a similar study, however, Hafner and Smith did find a significant increase in the self selected walking speed of more active amputees when subjects changed from a NMC to a C-Leg. 39 Kahle, Highsmith, and Hubbard also found that subjects who used the C-Leg had a faster self selected walking speed on uneven terrain. 28 In order to obtain a more detailed comparison of the performance of these two knees when used by an individual to walk upon uneven ground, a portable and accurate gait analysis system appropriate for the outdoors must be used. 1.4 Human Gait: Normal, Healthy Walking and How It Compares to that of Transfemoral Amputees During Indoor, Level Walking Prior to examining the gait of an amputee, it is important to first have an understanding of normal, healthy gait. After all, the goal of prosthetic implementation is to return the amputee s ambulation capabilities as close to their pre-amputation state of mobility as possible. 4 Along with the knowledge of how normal, healthy gait is evaluated, an understanding of normal gait allows for an increased understanding of the limitations in amputees as well as the limitations of analyzing amputee, and non-amputee gait. 12

35 1.4.1 Introduction to Anatomical and Gait Terminology In order to understand the mechanics of human gait, it is necessary to have a basic knowledge of anatomical terminology. The human body can be divided into three planes: sagittal, frontal, and transverse, as shown in Figure There are also three axes along which motion occurs in relation to these planes. 40 The sagittal plane divides the body into right and left sides. Motion along the mediolateral (ML) axis occurs perpendicular to the Figure 1.3 The three planes and axes of motion of the human body. 40 The transverse plane (TP), frontal plane (FP), and sagittal plane (SP) are shown segmenting the body. The anteroposterior axis (AA), mediolateral axis (MA), and longitudinal axis (LA) are shown in relation to the three planes. 13

36 sagittal plane. 41 The frontal, or coronal, plane splits the body into front and back, known as anterior and posterior, sections. Motion along the anteroposterior (AP) axis occurs perpendicular to the frontal plane. 41 The transverse plane divides the body into upper and lower, or superior and inferior, portions. Motion along the longitudinal, or vertical, axis occurs perpendicular to the transverse plane. 41 When describing gait, the sagittal plane is often most studied because of the large amount of movement which occurs from this reference frame. 41 Anatomical terminology is also used to describe the movements of body segments. Flexion is bending that decreases the angle between two segments, where as extension is bending that increases the angle. 40 The foot takes part in two types of flexion: plantarflexion and dorsiflexion, which increase and decrease the angle between the foot and the front of the shin respectively. 40 Abduction/adduction is movement away from/toward the midline of the body of the body when viewed from the frontal plane. Inversion/eversion is the inward/outward turning of the hind foot. 40 Proximal/distal refers to a location closer to or farther away from a point on the body. 40 These movements may be quantified to comment on the observed values against what may be expected. There are also terms which describe different attributes of gait and can also be used to determine how healthy gait is. Cadence is the number of steps a person takes in a given time period. Swing and stance duration are the amount of time spent in either swing phase or stance phase for an individual limb. Step width is the horizontal distance between the same point on opposite feet during double limb stance. Step length is the longitudinal distance between the same point on opposite feet during double limb support. Stride length, on the other hand, is the distance traveled by a point on the foot 14

37 during consecutive phases of initial contact. The difference between step length and stride length is more clearly shown in Figure 1.4. Normative data exists for each of these parameters to compare the gait of an individual to expectations of a healthy walking style in order to suggest whether or not abnormalities indicative of an individual s health exist. Figure 1.4 Step length versus stride length. One stride of the right foot and one left step are shown The Gait Cycle: A Cyclic Representation of Human Walking When describing the events that occur during walking, the motion of one leg is considered. This cyclical pattern of repeated steps during gait can be divided into stance and swing phases, representing instances when the foot is in contact with the ground or not. In normal gait, a person will spend approximately 62% of the cycle in stance phase and 38% in swing phase. 42 During these phases, one foot or both feet are on the ground, indicating single limb support (SLS) or double limb support (DLS). In order to further 15

38 examine walking and the constantly changing position of limb segments during gait, the gait cycle is commonly further broken down into a number of different phases that represent critical actions during stance and swing phases. This feat has been accomplished in a number of ways depending on which authority on gait is consulted. Many sources cite the terminology developed by the Rancho Los Amigos Gait Analysis Committee due to its application for a variety of gait deficits. 40,42,43 This particular system for identifying gait events is divided into the eight phases shown in Figure 1.5 and described in Table ,42 Figure 1.5 The terminology for the 8 phases of gait developed by the Rancho Los Amigos Gait Analysis Committee

39 Table 1.3 A Description of Each of the 8 Phases of Gait Developed by the Rancho Los Amigos Gait Analysis Committee with Characteristics of Normal Gait Shown Phase Initial Contact Loading Response Mid Stance Terminal Stance Pre-Swing Initial Swing Mid Swing Terminal Swing Description The heel of the foot contacts the ground. Body weight is accepted by the limb. Limb is in SLS, completely supporting body weight. Continues until body weight is in line with the forefoot. SLS is completed as the opposite limb finishes swing phase. DLS is initiated as the limb releases body weight. Swing phase begins and continues until the foot is vertically aligned with the opposite foot. Limb advances in swing phase until the tibia is completely vertical. Completion of the swing phase in preparation for ground contact. Gait Cycle Interval 0-2% 2-12% 12-31% 31-50% 50-62% 62-75% 75-87% % Anatomical Movements Hip flexed, knee extended, ankle neutral Hip flexed, knee flexed, ankle plantarflexed Hip extends, knee extends, ankle returns to neutral Hip extends, knee fully extended, ankle dorsiflexed Hip extension reduces, knee flexes, ankle plantarflexes Hip flexes, knee flexes, ankle dorsiflexes Hip continues flexion, knee extends, ankle neutral Hip finishes flexion, rapid knee extension, ankle neutral Transfemoral Amputee Gait Often, the quality of amputee gait is evaluated in reference to the deviations that are present from normal, healthy gait and the inability to perform certain normal 17

40 activities. 20, These deviations are of both a kinematic and kinetic nature. Amputation above the knee significantly affects gait kinematics due to the severe loss of musculature and joints. Though specific kinematic changes are relative to the componentry of the prosthesis, common trends exist in transfemoral amputees as compared to healthy individuals. Overall, amputees tend to walk at a slower speed, resulting in a lower cadence. 22 One would assume that TFAs also have a shorter stride length, but due to less precise foot placement the opposite tends to be true. 44 In addition, step width in TFAs tends to be larger, presumably to provide a larger base of support to increase stability. 40 TFAs remain in stance phase longer on their sound limb and in swing phase longer with the prosthetic limb. 22 This is mainly due to the increased amount of time needed to complete knee extension in the prosthetic limb. 22 These traits culminate in an irregular gait, which may be improved by advancements in prosthetic componentry. 1,2,27,36-38 The changes in kinematics are directly correlated to the changes in the kinetics of gait. Along with the longer time spent in stance phase, the intact limb must endure a greater load than that placed upon the prosthetic limb; vertical and anterior/posterior ground reaction forces, the forces exerted upon the body by the ground during locomotion, are increased in the sound limb and decreased in the prosthetic limb when a person walks as compared to normal gait. 46,47 People with an above the knee amputation cannot produce significant ground reaction forces in their affected limb due to the loss of musculature, so the intact limb must make up the difference. 46,47 This leads to irregularities in an amputee s gait. 18

41 1.4.4 Additional Activities of Transfemoral Amputee Gait Strenuous activities outside of normal walking can impose further challenges for transfemoral amputees, such as ambulation on stairs, hills, and uneven ground as well as obstacle avoidance. 1,4,5,28-30 In addition to lack of strength, reduced proprioception, balance, pain, and fear of falling can impact functionality during these types of activities. 48 The lack of ankle plantar/dorsi-flexion in the prosthetic limb also hinders the amputee. Ambulating over obstacles and on uneven ground is especially challenging for amputees. 1,2,36-38 Transfemoral amputees are susceptible to falling due to decreased knee flexion in the prosthetic limb when negotiating an object. 49 A slower gait speed adds to this problem by decreasing knee flexion further. As a result, the amputee must increase the height of the hip joint on the affected side to avoid an object or when walking on uneven ground. 50 The reasons for this are numerous, including atrophy of the muscles controlling the hip as well as the lack of dorsiflexion and knee flexion on the prosthetic side. 50 Though there have been studies performed to analyze the difficulties amputees have navigating obstacles and uneven ground, these studies have all been performed in a laboratory environment using force and balance plates as well as optoelectric motion analysis. 1,28,49 By testing in these controlled environments, the possible benefits that microprocessor knees provide for above the knee amputees are overlooked. 1 Microprocessor knees have previously been shown to improve amputee ambulation in both stair and hill ambulation, but to date the potential benefits of a microprocessor knee 19

42 have not been thoroughly examined with respect to ambulating upon uneven ground. 29,30 There exists a necessity to conduct a gait analysis in real life situations in order to obtain an unbiased assessment of the walking styles of above the knee amputees in challenging walking situations Gait Analysis: Advances in Technology Have Made It Possible for Human Walking to be Quantifiably Examined Outside of the Laboratory When analyzing the gait of an individual with known mobility impairments, the main objective is to identify deviations from normal, healthy gait in an effort to determine the associated causes of these deviations. 35 This is normally done in research by comparing the group of interest with a control group. It can also be used as a before and after tool in order to analyze the effect that therapy or walking aids have had on walking ability. Modern gait analysis is conducted through collecting muscle activity, kinematic, kinetic, spatial, and temporal data. 35 Because there are so many factors acting simultaneously, gait analysis requires significant knowledge of human ambulation and biomechanics in order to deduce useful results. 35 In order to collect data for analysis, there are a number of options used by clinicians and engineers alike, ranging from observation to sophisticated motion capture. When gait analysis is performed with the intent to improve the rehabilitation of an amputee, it is used only to quantify the mobility state of medical disorders and is not used for medical diagnosis. 7 In other words, gait analysis is a useful tool to determine the effects of a mobility impairment, but not necessarily the cause. However, the problem with using traditional gait analysis in the 20

43 rehabilitation of an amputee is that the interpretation of the biomechanical data resulting from a gait analysis is complex, time consuming, and is not understood by most clinicians. 7 For this reason, observational gait analysis is still a widely used tool when evaluating a person s walking ability Observational Gait Analysis: A Qualitative Approach to Identifying Deviations from Normal Gait by Visual Observation One of the main methods used clinically to evaluate gait is observational gait analysis. The position of limb segments, joint angles, and the axes upon which the angles are being monitored are the main focus in this type of analysis. 43 The observer must have significant knowledge of the stages of the gait cycle and the respective anatomical positions of the body to identify a deviation. Then, the observer can determine if an abnormality is present and to what extent. This type of gait analysis is appealing to clinicians because of its low cost, rapidity to administer, and relative ease of use as compared to 3D marker based and other gait analysis methods. 7 However, due to the subjectivity of the testing, observational gait analysis has poor inter- and intrareliability. 7, Quantifying Human Gait through Three Dimensional, Marker Based Gait Analysis At the other end of the complexity of examining walking is 3D gait analysis. As an alternative to observational analysis, this type of analysis involves the use of markers 21

44 and cameras to quantify human movement. This can be accomplished using active markers, such as light-emitting diodes, or reflective, passive markers. The advantage of passive markers over the other two systems is that the markers are wireless and in turn less restrictive. 51 However, this type of analysis is performed within the confines of a rigorously controlled laboratory setting. 51 When a person is brought into a gait laboratory, these markers are placed on bony prominences across areas of interest on the body. The person is then asked to walk a predetermined distance or for a certain amount of time in order to obtain data on a significant number of gait cycles. These trials are assumed to be representative of an individual s normal, everyday gait. 51 Cameras placed around a laboratory are equipped with infra-red-emitting diodes and the markers reflect this light back to cameras with a clear line of sight to markers. A relative 3D position of a marker can be established as long as two cameras are able to pick up the reflection from one marker at any given point in time. 52 In addition to the camera system, force plates are commonly installed in the walkway to measure the different forces a person exerts on the ground as they walk. The force plates can also be used to determine spatio-temporal variables related to foot placement, such as step length, step width, and cadence. 51 One or more force plates may be placed along the walkway for use in collaboration with the data obtained from cameras. However, there is a great amount of expertise required to perform this type of gait analysis. Both a skilled engineer and physical therapist are required to conduct testing. 51 The cameras capture the image of each marker and the software used identifies the position of the markers. After the testing is done, an engineer will have to analyze the 22

45 plethora of data obtained in order to convert it into a fashion which can be understood by a clinician. 51 From the positioning data, the engineer can calculate the velocities and accelerations of limb segments, joint angles, angular velocities, and angular accelerations. From the force plate data, inverse dynamics can be used to calculate joint moments. This process can take up to two weeks in order to produce meaningful results. 51 Though this type of gait analysis boasts high amounts of quantification and objectivity, there are still a number of drawbacks to using 3D markers. Many times, occlusion of a marker can occur if there are too many markers or an insufficient amount of cameras. 53 Also, cameras may mistake one marker for another or pick up false reflections. 52 Due to the nature of the systems, the data obtained in 3D gait analysis must be processed after testing and, even then, can be confusing to clinicians. For example, knowing that the knee is extended to 45 instead of 35 at the beginning of terminal stance is not immediately useful to clinicians. 3D gait analysis is also restricted to short walking trials in laboratory settings, meaning that the trials may not be representative of the true nature of human walking. 51 For these reasons, other gait analysis methods have been utilized to obtain similar data An Alternative Approach: Gait Analysis using Kinematic Sensors Similar to 3D gait analysis, newer kinematic sensors are able to provide quantification and objective measurements of gait in real world settings. 6 These wearable sensors are capable of measuring components of gait which are independent of forces and mass. 6 Including pressure sensors, goniometers, and accelerometers, these sensors can 23

46 collect raw data which relates to the forces and movements that the human body creates as an individual walks. 6 By processing these raw signals using computer programming, the information gathered can provide spatio-temporal data as it relates to human movement. Included in these parameters are gait speed, cadence, step length, stride length, single limb support time, double limb support time, and joint angles. 40 In particular, accelerometers have been gaining popularity in the field of gait analysis because they can be wireless, easy to use, and can acquire data outside of a laboratory setting A New Opportunity: Accelerometers Create the Potential to Address Amputee Gait Needs by Monitoring Real-World Ambulation The use of accelerometers in analyzing human walking has proven to be reliable in research which has been conducted thus far. 8,54-59 Development of microelectromechanical systems (MEMS) technology has made these sensors inexpensive to produce and small in size. In general, accelerometers measure an applied acceleration along one or more axes which are sensitive to motion. 6 The manner in which this acceleration is measured depends on the types of accelerometer being used. The main classes of accelerometers used in measuring human motion are piezoresistive, piezoelectric, and capacitive. Capacitive accelerometers are especially desirable because they are useful in measuring human movement due to their high stability, sensitivity, and resolution

47 1.6.1 A Review of Accelerometry The basic construction of a single axis accelerometer consists of an inner, or proof, mass connected to a frame or casing by means of a damped spring or some equivalent, as shown in Figure When in motion, this mass is displaced in relation to the movement of the accelerometer. Figure 1.6 A depiction of the basic construction of a single axis accelerometer (traditional spring-mass-damper system). The accelerometer functions on three principles of motion within this system to operate, based upon Newton s 2 nd Law. The first states that the force (F mass ) acting on an object is equivalent to its mass (m) multiplied by its acceleration ( ), 25 (1.1)

48 The second is Hooke s Law, which states that a linear spring will put forth a restoring force (F spring ) equal to the relative distance that it has been displaced, x, multiplied by a spring constant, k, (1.2) Finally, the force exerted by a linear damper (F damper ) is equal to the product of the damping constant, c, and the relative speed at which the displacement of the mass is occurring ( ), (1.3) When the accelerometer mass is subjected to a force due to movement, the relative displacement across the spring and relative velocity across the damper, its motion is modeled by the following differential equation: (1.4) When the accelerometer mass is displaced due to movement of the body to which it is attached, the corresponding displacement is converted to a change of electrical capacitance, a measure of the amount of electrical charge stored between two parallel plates in relation to a voltage applied to the plates, in capacitive accelerometers. 6 26

49 Readings can be made in one direction (uniaxial), two directions (biaxial), or three dimensionally (triaxial) depending on the number of sensitive axes of the accelerometer. In general, a capacitor is made of two parallel plates with a voltage applied across them. They are spaced a certain distance apart with a gas or other material separating the plates. When a voltage, V, is applied across the plates, an electrical charge accumulates and is stored in between the two plates. 60 From an electrical standpoint, capacitance is defined as the ratio of the charge stored to the applied voltage, or, (1.5) Physically, the capacitance achieved between two plates is dependent of the surface area of the plates which are aligned with each other as well as the gap, or distance, between the two plates. 60 This is shown by the equation, (1.6) where k is the dielectric constant, which is a property of the material or air between the capacitor plates, is the capacitivity of the free space between the two plates, S this the area of the two plates which overlap, and h is the variable gap, or the changing distance between the two plates. 60 From Equation 1.6, it can be seen that the capacitance is changed by making the gap between the plates larger or smaller, or by making the overlapping area between the two plates larger or smaller. 27

50 In variable capacitive accelerometers, a number of moving plates are attached to a silicon mass held between a number of capacitors. The number and orientation of plates depends upon the number of sensitive axes of the accelerometer. The mass can be held by actual springs or by torsion bars or tethers, which serve as springs. 60 There are a number of different ways to achieve the same effect in capacitive accelerometers. An example of a simple arrangement a uniaxial capacitive accelerometer is shown in Figure 1.7. When the mass moves due to motion of the frame, the change in the variable gap creates an imbalance between opposing plates via a change in capacitance, explained by Equation 1.6. This change is proportional to the acceleration of the silicon mass. In turn, the inclination of the accelerometer which is placed on an individual can be determined in relation to the pull of gravity, which can be used to represent a global vertical axis. If this tilt of the accelerometer is compensated for and the influence of gravity is removed, then an accelerometer attached to a person is capable of measuring accelerations due to the movement of their body. 54,56,57 28

51 Figure 1.7 A depiction of a simple form of a variable capacitor. One plate which is attached to the silicon mass is installed a certain distance away from a plate which is fixed to the accelerometer frame. As the mass moves closer to and farther away from the frame, the variable gap between the two plates changes and results in a change in capacitance. Even when stationary, capacitive accelerometers are able to measure the pull of gravity on a body segment because of the change created in the variable gap The Use of Accelerometers to Analyze Gait Acceleration patterns generated as an individual walks depend upon the location on the body where an accelerometer is placed and the direction(s) in which acceleration is being measured. The magnitude of measured accelerations during human walking reported range from -0.3 g to 0.8g and tend to decrease from the feet to the head due to the natural damping properties of the human body. 54 These accelerations can be analyzed according to a fixed reference frame if the accelerometer is aligned in accordance with the 3 planes 29

52 of motion of the human body. However, the natural curvature of the human body makes this alignment difficult. Even if a perfect alignment was achieved, the accelerometer will not remain in this fixed reference frame due to the natural rotation of the body and limbs during walking. 56 This imposes a methodological problem, because an accelerometer records accelerations due to gravity as well as those caused by the motion of the body during ambulation. 56,57 As a result of the movement of the accelerometer, the static component of gravity may have an impact on the estimates of dynamic accelerations of the body as a person walks. A commonly used procedure to correct for these deviations developed by Moe- Nilssen has been used in a number of studies which utilize accelerometers to explore the motion of the body. 8,55-57,61-63 Moe-Nilssen found that when an accelerometer is placed on the lower back near the body s center of mass, the vertical axis of the accelerometer aligns closely with that of the human body. 56 Additionally, rotation of the accelerometer along the anteroposterior and sagittal axes are minimized at this location. 56 At this position, any initial tilt due to placement or dynamic tilt during walking of the accelerometer can be corrected for using trigonometry. 56 The acceleration patterns of human walking in the vertical, anteroposterior, and mediolateral directions can then be accurately reported and analyzed. Concordant with the cyclic nature of human walking, the acceleration patterns of human movement are repetitive in nature along all three axes of the human body. 54,57 30

53 1.6.3 A Novel Accelerometer Based Gait Analysis System: The Intelligent Device for Energy Expenditure and Activity System (IDEEA ) Currently, there are a number of commercially available accelerometer based systems which are able to be mounted unobtrusively upon different locations on the body to measure accelerations. Many of these systems use proprietary software which automatically analyzes the acceleration signal(s) and calculates parameters about human gait. These systems are ideal for clinical use, because they do not require extensive signal processing in order to determine gait parameters. They also cost less and are easier to use than more sophisticated methods, yet have comparable accuracy. 8,54 One such system is the MiniSun IDEEA, which is comprised of a five biaxial, capacitive accelerometers which transmit data to a small recorder, as shown in Figure 1.8. The system uses a microcomputer equipped with a 32-bit, 33 MHz microprocessor to record data from the array of accelerometers in order to determine if a person is sitting, standing, walking, etc. based on the orientations of the different sensors. 15 When the system determines that the wearer is walking, it records the acceleration signals and processes them through proprietary software to determine gait parameters. 15 These parameters can be viewed using ActView software, which was provided by the manufacturer with the IDEEA. The advantage of the IDEEA system over other accelerometers is that the system does not require extensive signal processing, as is normally required when using accelerometers in gait analysis. Instead, the microprocessor and proprietary software automatically process the accelerometer signal to determine up to 17 gait parameters. This IDEEA has been used previously in a handful 31

54 Figure 1.8 The five sensors and data collector shown with respect to a nickel for size comparison. 15 of other published studies, including use in populations with mobility impairment (degenerative knee arthritis, hemiparesis, Cerebral Palsy) 11-15,64,65 However, because the IDEEA does not allow for access to the raw data, it is especially important to confirm the accuracy of its measurements for it to be recognized as a valid gait analysis tool. Studies have examined the aspects of this accuracy, with mixed results (Zhang et al., Kwon et al., Saremi et al., Gardner et al., Maffiuletti et al., Mackey, Stott, and Walt). 13,15, A Review of the Literature Examining the IDEEA In order for the IDEEA to accurately record gait parameters, the system must be able to accurately identify when an individual is walking. Zhang et al. found that the 32

55 system correctly detected general walking at a rate of 99.7% on average during walking trials on even ground. 15 The authors also did not find any significant differences between the IDEEA and an optic system when comparing recorded walking speeds. In a different study which compared morbidly obese females to a control group, Kwon et al. found a 95% correlation of the walking speed recorded by the IDEEA compared to the set speed of a treadmill. 65 The system detected walking 100% of the time in the authors study. 65 Two other studies examined the accuracy of the device to determine the reliability and validity of the recorded gait parameters in comparison to those obtained using instrumented pressure sensors placed in subjects footwear, known as footswitches. 13,66 Saremi et al. did not find significant differences between the two systems at a number of different walking speeds. 13 The authors did note, however, that the IDEEA functioned best when subjects walked faster than 0.5 m/s and at longer distances (a walkway of only 15 m was used in their study). 13 Gardner et al. validated the IDEEA using a widely accepted method of analyzing the signals of the footswitches. 66 When compared to the gait parameters reported by the IDEEA, the authors found the system to be as accurate as footswitches for calculating gait speed and time related variables, such as gait cycle duration, but there was a slightly higher discrepancy for step length. 66 More recent studies also investigated the repeatability of the system in addition to its accuracy. 64,67 Maffiuletti et al. found that walking velocity, step length, and stride length were all underestimated by the IDEEA as compared to the force plate data in a gait laboratory. 67 Similarly, Mackey, Stott, and Walt found the IDEEA to overestimate step and stride length, but underestimate walking velocity and cadence. 64 In both studies, temporal parameters had higher inter-and intra-session reliability than spatial parameters 33

56 and the number of steps counted. 64,67 However, both studies were conducted in laboratory environments and with short walking trials, which Saremi et al. reported were not ideal for use with the IDEEA. 13,64,67 The fact that the system uses biaxial accelerometers to record the motion of the human body in three planes is recognized as a potential source of error. 11, 64 For example, inversion or eversion of the foot would not be picked up with the current accelerometer orientation, which could influence actual step length. 67 Without further details regarding the algorithms and filters used by the IDEEA software, however, any specific source of error cannot be identified. Further work is needed to examine the use of the IDEEA, especially in typical community settings, such as waking over uneven ground. 64 Also, although the system has been used with people who have hemiparesis, or weakness of one side of the body, the use of this device with a population of above the knee amputees had also not been examined Motivation of Current Work There have been numerous studies conducted on amputee gait which compare the kinematics of able bodied individuals to transfemoral amputees during gait, stair ambulation, hill ambulation, walking on horizontally inclined surfaces, and navigating obstacles. 22,25,29,43,49,68-70 Similar studies have also compared the effects of different prosthetic componentry on amputee gait kinematics, especially in the areas of prosthetic knee options. 1,2, 24,26,28,30,33,36,37,39,42 A similar theme across the board is that microprocessor knees aid amputees in everyday walking more so than passive knees, 34

57 possibly due to their continual analysis of the kinematics of the knee and forces exerted on the prosthetic limb throughout the gait cycle. 1,2,5,19,23,28,29,33,34,36 However, these studies have not focused on amputee gait on uneven ground, especially not outside of a laboratory. 1,2,36-38 Manufacturers claim that microprocessor knees aid in ambulating on uneven surfaces. 71 However, the Veteran s Administration Technology Assessment Program reported in the year 2000 in their short report on computerized lower limb prosthetics that results on their ability to improve ambulation on uneven surfaces were mixed. 3 In order to assess the validity of this claim, it is necessary to evaluate the kinematics of an amputee s gait with computerized and non-computerized knee components in a real-world setting. To do this, a portable, wearable sensor system is necessary that would allow gait to be measured in these types of settings. By validating a novel device, like the IDEEA, to analyze gait over uneven ground, clinicians and researchers alike may be able to easily perform this task without the cumbersome signal processing required when using 3D marker based systems or raw acceleration readings. The aim of this study was first to examine the validity of the MiniSun IDEEA in real world situations. To accomplish this, the IDEEA was used to analyze the gait of a healthy, non-amputee population as they walked over a stretch of even and uneven ground. Acceleration patterns of these individuals were also recorded using a triaxial accelerometer. Then, using proven methods based on accelerometry, the results obtained from analyzing individual acceleration patterns were compared to those obtained from the IDEEA. The IDEEA system and accelerometer were also attached to the healthy and prosthetic limbs of a two transfemoral amputees in order to record information about their gait as they walked along a stretch of even and uneven ground. Both of these 35

58 individuals prosthetic limbs were equipped with a microprocessor knee joint. The results of this study provide information regarding the use of the IDEEA when individuals walked on uneven ground, as well as for use with people who have an above the knee amputation. Specifically, this study was conducted in order to answer three primary research questions: 1. Does the IDEEA correctly analyze gait outdoors when a person walks on uneven terrain? 2. Is the IDEEA accurate in reporting the speed, cadence, step length, and gait cycle duration of a diverse group of healthy individuals walking outdoors in a realworld environment? 3. Is the IDEEA a valid tool for analyzing the gait of people who have undergone an above the knee amputation? 36

59 CHAPTER 2 METHODOLOGY This study investigated the validity of the MiniSun IDEEA for recording the gait parameters of healthy individuals as they walked on uneven ground. Specifically, the main purpose of this study was to compare the results collected by the IDEEA to a proven method of analyzing gait by using raw acceleration curves collected by a triaxial accelerometer. The study s design followed the guidelines recommended by Coleman and Montgomery for designing and analyzing an experiment. 72 This procedure focuses on the idea that it is necessary to first have a clear idea of the purpose of the study, the results to be collected, factors which may influence the results, and then determine how the data is to be analyzed. With the validation of the IDEEA in mind, the first step was to identify the gait parameters which would be analyzed in the study. Next, variables were identified which could have influenced the validity of the IDEEA, other than type of surface walked upon. Using the knowledge of the results to be analyzed and the factors which may influence these results, an experimental design was then formed. As an additional measure, the present study also examined the feasibility of using the IDEEA to analyze amputee gait in a small number of transfemoral amputees. If the 37

60 results indicated that the system was valid for use on uneven surfaces and feasible for use with amputees, then future studies could investigate the use of the IDEEA with transfemoral amputees walking in real-world situations. Information on this part of the study is at the end of this chapter, after the explanation of the main study on nonamputees. 2.1 Gait Parameters Analyzed In order to quantify human walking, it was necessary to select parameters that would provide the most useful information about gait in real world conditions. Spatiotemporal responses were chosen because they represent a global predictor of an individual s ability to ambulate. 73 Also, because this study was conducted with above the knee amputees in mind, it was important to consider typical functional outcomes which relate to amputees. The three most commonly reported parameters among research which looks at the gait of above the knee amputees are self-selected walking speed, cadence, and step length. 73 These parameters are important because they are considered to be directly correlated to one s ability to ambulate independently. 73 In order to measure the variability in an individual s gait from stride to stride, stride times were also recorded. Stride time is also referred to as the duration of the gait cycle, or the time to take two complete steps. 38

61 2.2 Factors Considered to Influence Gait Although the surface subjects walked upon was the focus of this study, there were a number of factors considered which could have potentially influenced the gait characteristics of a healthy individual when walking in real life situations, and in turn influence the results obtained by the IDEEA. These influences can be broken into personal/demographic and environmental factors Personal/Demographic Factors Any study involving transfemoral amputees must consider the demographics of the amputee population in order for the results to be applicable to the amputee population as a whole. The age of subjects was of particular concern as gait speed and step length have been shown to decrease as age increases, possibly to increase stability. 74 According to current estimates, forty two percent of the amputee population is over 65 years of age. 16 However, amputees over the age of 65 are known to be less autonomous in their mobility and would be less likely to encounter uneven ground during everyday activities. Adults over the age of 65 are also known to be at a greater risk of falling, a risk which would be increased by walking on uneven ground. 74 Consequentially, subjects over the age of 65 were not considered for this study. However, it is a known fact that the walking characteristics of healthy individuals change as they age, especially self selected gait speed and step length There was a potential, then, for the validity of the sensors to be influenced by the age of wearer 39

62 because of the differences in walking style. To account for these changes in this study, age was treated as a second primary design factor in this study. Two age groups were used for the non-amputee population in this analysis. Adults aged years were recruited for an older age group, as 38% of all amputees are represented by this age range. 16 Adults aged were chosen for the younger age group. Weight, height, and gender could also all potentially impact the walking style of study participants. However, the goal of this study was to validate the IDEEA sensors for use with people regardless of these three variables. As such, weight, height, and gender were not considered as primary variables in this study. Instead, these three variables were all treated as uncontrolled, nuisance variables and were recorded due to their potential to have an impact on the results of the current work Environmental Factors Three weather related factors were considered in this study: temperature, humidity, and barometric pressure. In the late summer months in Dayton, OH, the local temperature and humidity are known to rise to relatively high levels. High temperatures and humidity negatively affect the thermal regulation of the human body, which can lead to fatigue and change in blood volume through dehydration. 77,78 Changes in barometric pressure have also been shown to be a source of joint pain for older adults and a potential source of phantom limb pain for amputees. 79 In addition to the influence of environmental factors on humans, the raw outputs of the accelerometers used in this study could also be impacted by the environment. In addition to causing small changes in 40

63 material properties, these changes could also impact the viscosity of the air between capacitors and alter the motion of the spring-mass-damper system that the accelerometer uses to operate. 60 However, because barometric pressure, temperature, and humidity could not be controlled by the researcher over the 56 day period that testing occurred, the influence of these factors could not be directly examined. These three variables were therefore considered to be uncontrollable nuisance factors and were recorded as a precautionary measure; it was still necessary to record these variables to analyze their potential impact on the testing results. Reports of the National Oceanic and Atmospheric Administration were reviewed on testing days and temperature, humidity, and barometric pressure information were recorded. 80 In addition to weather related factors, the physical characteristics of the walking surface can also influence gait. Uphill and downhill slopes are known to have a direct impact on step length and prosthetic knee joint angles in amputees at grades as small as 5%. 29 Additionally, horizontal tilt in the terrain could cause deviations in an individual s gait pattern. 50 The slope of the walking surface was therefore a controlled variable in that both the even and uneven terrains which study participants were asked to walk upon were approximately neutral with respect to any inclines, declines, or side-to-side tilt. Only the small variations in the mown lawn were inclined or tilted, thus creating an uneven terrain. This was performed in order to represent real world situations outside of a laboratory that a person might encounter during their everyday life. The physical dimensions of the walking surface were also taken into consideration because the walking distance impacts the individual trials. In order to make 41

64 results more applicable to both amputees and non-amputees in a clinical setting, the original goal was to use either a standard 6- or 2-minute timed walking test, which are commonly used in clinical research. 81,82 The 6 minute walking test was not chosen because of the risk that subjects would not be able to walk for the entire duration of the test due to fatigue. 81 The 2-minute walking test was also excluded because it has not been formally standardized for use with transfemoral amputees. 82 A third option was to have subjects walk a set distance. The distance was to be long enough to ensure that enough steps were taken in order to ensure meaningful results. In a study which examined the walking distances of amputees, Hatfield found that results from walking trials of at least 40 meters could differentiate between amputees who could walk outdoors independently from those who could not. 83 For these reasons, a walking distance of 50 meters in length was chosen. The width of the path to be walked could also have impacted the amount of freedom study participants had to walk laterally. The paved pathway that subjects walked upon was approximately 1.3 meters in width. In agreement, the pathway which study participants walk over uneven terrain was the same width. 2.3 Experimental Design With the knowledge of the parameters to be analyzed and the factors which may influence these parameters, the experiment could then be designed. The purpose of this study became to investigate the validity of the IDEEA in recording the gait speed, cadence, step length, and gait cycle duration of individuals when walking in real-world situations. For this analysis, the gait parameters of subjects were recorded while subjects 42

65 walked 50 meters at their own self-selected, comfortable walking speed: either along a smoothly paved walkway or over ground in a section of mown grass. Each subject completed three trials on each terrain to ensure that the true, steady state gait of the subject was being recorded. By taking the average of the three trials on even and uneven ground, small variations in the subjects gait from trial to trial would not influence the results of the study. The accuracy of the IDEEA was investigated by comparing the results obtained using the IDEEA to those obtained by using a proven gait analysis technique using a triaxial accelerometer. Specifically, this study investigated the differences between these two systems with respect to the four aforementioned design variables based upon two primary factors: type of walking surface and age. In order to measure their influence on the accuracy of the IDEEA, a total of 4 possible combinations were examined: 1. Age 18-38, Even Ground 2. Age 18-38, Uneven Ground 3. Age 45-65, Even Ground 4. Age 45-65, Uneven Ground These conditions were examined to determine whether or not there was a statistically significant difference between the results. If a statistically significant difference was not found between the results on different walking surfaces, then the system would be valid for use on these two surfaces in future studies. In order to investigate the accuracy of the system further, the impact that age had on the difference between the results of the two 43

66 systems was also examined. If a statistically significant difference was found, then characteristics of the particular age group would have to be taken into consideration when using the IDEEA. An interaction between two variables would mean that one factor has a different effect on a response at different levels of another factor. In the case of this study, an interaction between the type of surface walked upon and the age of participants would indicate that the validity of the IDEEA is more or less valid for use with one age group walking upon different surfaces than the other age group. 2.4 Subject Recruitment The number of participants needed for this study was based on power calculations performed using previously published values of the variables analyzed in this study. MiniTab v.14.2 software was used to conduct power and sample size calculations. Calculations were first performed based upon the mean values of the gait characteristics which were analyzed in this study. Data on the standard deviation of gait speed, cadence, step length, and gait cycle duration of people of different age groups were obtained from previous studies which examined gait on both level and irregular surfaces. 76,84 The standard deviation of these variables was an indication of how much variation should be expected from person to person. These calculations revealed that a minimum of 8 subjects would be required for each age group. In order to ensure the power of the results, 16 subjects were recruited for each age group. In order to investigate the accuracy of the IDEEA system, a population of healthy individuals were recruited who fit into one of two age groups: years of age or 45-44

67 65 years of age. Participants for both age groups were primarily recruited from the University of Dayton via and word of mouth.. The population was free of any diseases, disorders, or injuries which could have altered their walking style and in turn altered the results of this study. In addition to the safety of study participants, any additional disorders that influence the way that a person walks could have had an impact on the results of the study. All subjects who expressed interested in participating were administered a screening questionnaire over the phone in order to ensure that they were physically capable of walking over uneven ground and had not been advised against physical activity by a physician. Anyone who was willing to volunteer also had to be capable of completing the test without the use of a walking aid, as this would have influenced results. The questionnaire can be found in Appendix A.1. All qualified individuals were invited to a public park in Moraine, Ohio for testing. At the beginning of the session, subjects were presented with a written consent form. The researcher reviewed this form with all participants, as approved by the University of Dayton Institutional Review Board (IRB) and subjects then decided if they wanted to participate 2.5 Subject Population A total of 32 healthy individuals volunteered for the validation portion of this study. All volunteers who came to a testing session gave written consent for participation. The sixteen individuals recruited for the age group ranged from 18 to 25 years of age with a mean of 21.6 ± 2.3 years, a mean height of ± 8.7 cm, and a mean weight 45

68 of 72.7 ± 11.9 kg. Six subjects were female and ten were male. The age group consisted of sixteen individuals who ranged from years of age with a mean of 53.6 ± 5 years, a mean height of ± 9.7 cm, and a mean weight of 77.9 ± 15.4 kg. Ten subjects were female and six were male. This information can be seen in Table 2.1. Table 2.1 Subject Characteristics of Non-Amputee Subjects Group Age Weight (kg) Height (cm) Gender Years 21.6 ± ± ± Male, 10 Female Years 53.6 ± ± ± Male, 6 Female 2.6 Gold Standard: Triaxial Accelerometer All raw acceleration measurements were taken using an inertial monitor (Opal, APDM Inc., Portland, OR). This inertial monitor consists of three microelectronechanical systems (MEMS): a capacitive triaxial accelerometer, a triaxial gyroscope, and a magnetometer. All three sensors are contained within a wireless sensing unit, shown in Figure 2.1. This monitor was attached to the lower back of study participants using an elastic belt which the manufacturer provided in order to record the acceleration of study participants as they walked. For the purposes of this study, only the triaxial accelerometer results were analyzed. The technical specifications of the accelerometer are shown in Table 2.2 below. The APDM was intended to investigate the validity of the results of the Intelligent Device for Energy Expenditure and Physical Activity in this study. 46

69 Figure 2.1 The APDM Opal and the elastic belt which is provided by the manufacturer. The positive x- and y-axes are shown in the figure. The positive z-axis is going into the page. When attached to an individual s lower back, the x-axis corresponds to the mediolateral direction, the y-axis corresponds to the vertical direction, and the z-axis corresponds to the anteroposterior axis in a left-handed coordinate system. Table 2.2 Technical Specifications of the APDM Accelerometer Dimensions 48.5 x 36 x 12 mm Weight less than 22 grams Range ±6g Noise Density 128 Bandwidth 50 Hz Sample Rate 128 Hz The sensitivity and zero-offset of the system also had to be obtained for the system along each axis in order to analyze the raw output of the accelerometer. The zerooffset is the output of the accelerometer when there is no influence of gravity on the 47

70 accelerometer (0 g). The sensitivity is defined as the output voltage change per unit of input acceleration, measured in voltage output per g (V/g). It is an indication of how much the measured voltage along an axis changes as the input acceleration changes,, (2.1) where is the measured voltage when accelerometer is positioned with positive axis pointing upward, is the measured voltage when accelerometer is positioned with positive axis pointing downward. When and are plotted on a coordinate axis, the sensitivity would be the slope of the line connecting the two points. This line is commonly referred to as the calibration curve. The zero-offset would be the y-intercept of this line, which is the voltage output at 0g. The zero-offset, then, was found by analyzing the plots of all three axes. The plot of and along the z-axis is shown in Figure 2.2. To obtain this information for each axis, the accelerometer was placed on a flat, level surface and the voltage output was recorded for the +1g and -1g orientation. This procedure was conducted three times for each axis and the average voltage output of the trials were recorded, as shown in Table

71 Voltage, v Z-Axis Sensitivity & Zero-Offset (1, ) y = x (-1, ) Voltage Acceleration, g Figure 2.2 A plot of and measured along the z-axis. As identified by the equation of the line fit through the two points, the sensitivity is the slope of the line, and the y-intercept is the zero-offset. Table 2.3 Sensitivity and Zero- Offset Data of the APDM Opal Triaxial Accelerometer Axis Sensitivity Zero-Offset (V) (V) (V/g) (V) X-Axis Y-Axis Z-Axis

72 2.7 Intelligent Device for Energy Expenditure and Physical Activity The Intelligent Device for Energy Expenditure (IDEEA ) (MiniSun, Fresno, CA) is an accelerometer based system used to monitor human walking and was the focus of this study s validation efforts. Table 2.4 lists the available technical specifications of the IDEEA system. Table 2.4 Technical Specifications of the IDEEA Dimensions Weight Range Noise Density Bandwidth Sample Rate Data recorder: 70 x 54 x 17 mm Accelerometers: ~18 x 15 x 3 mm Data recorder: 59 grams (~.13 pounds) Accelerometer: ~2 grams Not Reported Not Reported Not Reported 32 Hz The system used the five accelerometers placed on the chest, thighs, and feet of individuals, which were placed in accordance to the IDEEA manual; the chest sensor was to be placed in the middle of the sternum approximately 4 cm below the innermost side of the clavicles (Figure 2.3), one sensor was to be positioned on the top of each thigh midway between the knee and hip (Figure 2.4), and one sensor was to be placed under each foot approximately 2 cm below the 4 th metatarsal (Figure 2.5). The chest sensor was 50

73 Figure 2.3 The correct placement of the chest accelerometer as shown on an individual in the IDEEA manual. 85 The chest sensor was placed approximately 4 cm below the inside of the clavicles. 85 Figure 2.4 The correct placement of the thigh accelerometers, as shown in the IDEEA manual. 85 The thigh sensors were placed on top of the thigh, approximately half way between the knee and hip joints

74 Figure 2.5 The proper location of the IDEEA foot sensors, as indicated in the IDEEA manual. Each sensor is placed approximately 2 cm below the head of the 4 th metatarsal, as shown. attached to the data recorder by a wire which was run under the shirt of study participants. The thigh and foot sensors for each leg were connected to the data recorder by a continuous wire that ran from the recorder, through the thigh sensor, and ended at the foot sensor. The sensors had to be carefully placed in order to ensure the accuracy of the system. As detailed in the Introduction of this thesis, the IDEEA system uses the orientation of the five accelerometers in order determine if a person is sitting, standing, walking, etc. When the system determines that the wearer is walking, the system uses a microprocessor contained within the data recorder and proprietary software to determine gait parameters. These parameters can be viewed using ActView software, which was provided by the manufacturer with the IDEEA. 52

75 The advantage of the IDEEA system over other accelerometers is that the system did not require extensive signal processing, as is normally required when using accelerometers in gait analysis. Instead, the microprocessor and proprietary software automatically process the accelerometer signal to determine up to 17 gait parameters, including the five which were examined in this study. The IDEEA is capable of being used outside of a laboratory, but to the author s knowledge the validity of its use on uneven surfaces has yet to be established. 2.8 Experimental Protocol The experimental portion of this study took place during the late summer and early fall months of Subjects were each invited to Ora Everetts Park for a single testing session, lasting approximately one hour. On the day of testing, after the consent process, the study was explained to subjects and their height and weight was collected. Not only was the height and weight information used by the proprietary MiniSun software in recording information about and individual s gait, but height and weight were considered to be nuisance factors which could potentially have impacted the results of this study. After the height and weight of each subject were measured, subjects were fit with a gait belt. The purpose of the gait belt was to provide an added measure of safety to test subjects in the event that an individual lost their balance. However, no subjects lost their balance in this study and the gait belt was never held onto by the researcher. Subjects were then fitted with the MiniSun IDEEA system in accordance with the 53

76 manufacturer s instructions. Sensor placement was also held constant for each trial during the course of the research. The sensors were affixed an individual using hypoallergenic medical tape (3M Medipore). The area where the sensors were placed was swabbed with an alcohol pad before testing in order to prevent any body oils or dirt from causing the tape to come loose. Enough tape was used so that the sensor was securely attached and would not come loose during testing. In this way, the small sensor surface was not the only area of contact between the sensor and the skin, but rather a larger piece of tape covering the sensor and a portion of the surrounding skin. The tape was 2 inches wide to accommodate this procedure. Any excess wire between the thigh sensor and the foot sensor was secured to the leg of subjects with hypoallergenic medical tape, as described by the user manual, so that the wire was not slack and did not impede motion. After subjects were fit with the IDEEA sensors, the system had to be calibrated. Since the accelerometers that the system uses are biaxial, they measure acceleration in two directions. In order for the system to not record any initial acceleration due to gravity, the foot and thigh sensors must be within 15 of being parallel with the ground, and the chest sensor must be within 15 of being exactly vertical. Then, the system will account for any initial offset. 66 The calibration conducted automatically by the IDEEA corrects for any offset within this range. This simple calibration process makes the use of the IDEEA very practical for clinical use. The sensors were calibrated before each trial. In order to calibrate the system, subjects were asked to sit in a chair of an adjustable height. The height of the chair was altered for each individual in order to bring the thigh sensors as close as possible to being parallel with the ground. Subjects were then asked to sit upright, with both feet placed 54

77 directly in front of them and flat on the ground. In order to account for the natural inclination of the subjects footwear, calibration was conducted while wearing shoes. If a sensor was determined to be outside of the 15 allowance, the system showed an error message with a code indicating the sensor which was improperly aligned. That particular sensor was then reattached and the system was recalibrated. The IDEEA was then disconnected from the computer and testing was ready to begin. After calibration, subjects were then fit with the ADPM inertial monitor. The ADPM was attached securely to the subjects using an adjustable waist strap which was provided by the manufacturer. The waist strap and APDM were attached to each subject so that the inertial monitor was located over the sacrum, as shown in Figure 2.6. Care was taken to place the accelerometer as close as possible to the midline of the spine so that the Figure 2.6 An example of the APDM Opal placed over the sacrum using the elastic belt provided by the manufacturer. 55

78 accelerometer was placed in the same position with respect to the sagittal plane. The accelerometer was also positioned just above the gait belt so that the use of one did not interfere with the other. In this position, the accelerometer could not be perfectly aligned with respect to the sagittal or frontal planes. However, certain tilt compensation techniques exist to account for these initial accelerations caused by the gravity. These compensation techniques were applied post hoc, and are presented in section Subjects then walked approximately 20 meters from the site at which calibration occurred and the sensors were attached to the testing area with the IDEEA recorder, APDM interial monitor, and gait belt attached. Aside from reaching the testing area, this initial distance walked served three purposes. First, it allowed the subjects to become comfortable walking with the IDEEA sensors under their feet. Although studies have proven that the sensors do not alter natural gait, it was thought that the initial feeling may have seemed slightly awkward to participants. Secondly, this initial walk allowed the investigator to determine if there was an unnecessary tension in the wires before testing due to improper taping. Excess tension in the wires could have changed the tilt of the accelerometer over the course of the study and voided the initial calibration. Last, the IDEEA required for the wearer to take ten steps in order to recognize their individual gait. This step count was exceeded before the subject reached the testing area. The order in which each surface was walked upon during the six walking trials was randomized for each subject, such that some subjects walked in the lawn first, and some on the paved walkway. The fifty meter distance was designated using chalk lines on the pavement and a water-based marking paint in the grass. Additionally, the left and right boundaries of the pathway in the grass were outlined with cones. 56

79 In order to capture steady state gait, it was necessary to discard the first and last two steps of each trial. This is due to the fact that the initial and final steps taken during the course of walking are not representative of a person s actual walking style because they are speeding up when they begin walking and slowing down before they stop. 46,47,84 This is commonly referred to as gait initiation and gait termination. 46,47 However, it was also necessary for the distance walked to be known in order to estimate gait parameters. In previous studies, authors dismissed the first and last few steps of the walking trial or used a manually pressed trigger to indicate the time that steady state gait began, including one study which examined the reliability of the IDEEA. 15,55,59 It was believed that these methods allowed too much room for researcher error and bias, so a different method was proposed. Subjects were asked to approach the starting line of the testing area and then turn around 180 so that their back was facing the walking area and their heels were against the starting line. Subjects were then asked to take two complete steps and then turn around 180 again, so that they were facing the testing area. The researcher stayed close to the subjects during this transition so that the gait belt could be grabbed if a subject lost their balance. This procedure accounted for the first two steps of the trial which were to be discarded, as they were considered part of the gait initiation of the individual and not representative of a steady state gait. Subjects began each trial with both feet next to each other. In order to account for the two steps to be discarded during the gait termination phase, subjects were asked to take two complete steps past the end line, which was marked with chalk. At the end of the second step, subjects were instructed to bring their trailing foot next to the foot with which they took their last step, and to come to a 57

80 complete stop. The researcher demonstrated this procedure to all study participants before the first trial began, and reminded them of the procedure again before the beginning of the second trial. In this manner, the actual distance walked was just over 50 meters, but the actual distance recorded for analysis was approximately 50 meters. The inclusion of three trials per walking surface was also intended to normalize the walking distance closer to a distance of 50 meters, since the recorded distance walked would be different for each trial. At the beginning and end of each trial, subjects first were instructed to stand still for fifteen seconds, which was measured using a stopwatch. This was performed in order to aid the researcher in data analysis. By identifying the static portion of the acceleration readings of the APDM and IDEEA during each 15 second interval, the data of each walking trial could be easily distinguished from data which was not of interest, such as the walk from the calibration area to the testing area. The researcher then instructed subjects to begin by verbally saying, Ready. Set. Go. Subjects then walked the 50 meter trial and came to a complete stop at the end. After standing still for another fifteen seconds, study participants were then guided back to the calibration area where the APDM was detached and the IDEEA was recalibrated. The purpose in detaching the APDM sensor was to ensure that the sensor did not shift when the subject sat down before each trial to recalibrate the IDEEA. By detaching and reattaching the APDM for every trial, and then completely recalibrating the IDEEA, any shifting of the sensors which may have occurred during a trial or between trials would not affect the results of the study. 58

81 While the data from the APDM and IDEEA were being uploaded onto the on-site computer, the subjects were provided a three minute seated break between trials in order to prevent results from being altered due to fatigue. After the rest period was over and the data from the first trial had been uploaded, the sensors were reattached. The IDEEA sensors were then recalibrated. If an IDEEA sensor was determined to be outside of the 15 tolerance, that sensor was then reattached and the system was calibrated again. This procedure was performed for each walking trial. All data from both the MiniSun IDEEA and the APDM Opal was saved to an on-site computer to be accessed and analyzed later. 2.9 Data Analysis In order to examine individual walking trials, the raw data collected using the MiniSun IDEEA and APDM Opal had to be filtered, processed, and analyzed for each individual trial. The MiniSun IDEEA uses proprietary software to process recorded data. The processed data can then be analyzed using ActView software. The raw acceleration data from the APDM Opal was uploaded as three different arrays into Matlab corresponding to the anteroposterior, mediolateral, and vertical accelerations recorded while test subjects walked. A program was created for each individual trial in order to filter, process, and analyze the accelerations. 59

82 2.9.1 Extracting Gait Data from the MiniSun IDEEA Once a walking trial was processed and loaded into ActView, three different features were used to analyze gait. The first was a graph of the walking speed and activity type being performed (Figure 2.7). For each trial, the IDEEA began recording data immediately after the system was disconnected from the computer during testing. The first step, then, was to use information from the activity recognition feature to define the time period during which the walking trial took place among the rest of the time spent walking to and from the testing area. This was performed by locating activities of the experimental procedure of standing for fifteen seconds, walking, and then standing again for fifteen seconds. This activity pattern in shown in Figure 2.8. Once the walking trial was identified, the next step was to pick out the portion of the walking trial when the subject had crossed the start and finish line of the even and uneven paths. As outlined in Section 2.7, two additional steps were taken before and after each walking trial in order to account for gait initiation and termination. The first two steps of each trial were automatically discarded by the IDEEA, as these steps are identified as a transition from standing to walking by the IDEEA or standing while moving a leg, as can be seen in Figure 2.7. In order to discard the last two steps taken, a second graph in ActView of the raw anteroposterior accelerations taken from the foot sensors was used. 60

83 61 Figure 2.7 A graph of the resulting speed and activity types recorded during a walking trial. Speed is measured in m/s, and activity type is measured by the percentage of time which the activity occurred over the time period selected. The activity type listed, from top to bottom, are standing while moving the left leg (stlm), transition from one activity to another (tran), standing (stnd), and walking (wlk). The transition and standing while moving the left leg correspond to the period of time before the second acceleration peak shown in Figure 2.9. The transition period at the end occurs after the final peak shown in Figure time (s)

84 By examining the shape of the acceleration pattern, different stages of the gait cycle can be identified, as outlined in Figure 2.8. Each walking trial began at the instant when the subject s foot was on or slightly over the starting line, which was identified as the moment that the IDEEA began recording data at initial heel contact of the second step. Each trial ended at the instant when the subject s foot hit the finish line, two steps before the subject stopped walking. Since each subject took an additional two steps after the finish line and brought then brought their trailing leg to rest next to the foot that took the last step, three instances of heel contact occurred after a trial. The end of a trial, then, could be identified by locating the fourth-to-last acceleration peak. An example of the beginning and end of a trial are shown in Figure 2.9 and 2.10, respectively. After the start time and end time of the trial were selected for analysis in ActView, the mean gait speed, cadence, step length, and gait cycle duration of a subject was recorded from the Gait Analysis feature of the IDEEA. 62

85 63 Figure 2.8 An outline of the gait cycle defined by the forward and backward acceleration pattern of the right foot. Moments of heel contact can be identified by large peaks in the pattern.

86 64 Figure 2.9 The first four forward and backward acceleration peaks during a walking trial. Accelerations of both the right and left feet are shown. The data before the second peak was discarded such that the walking trial started at heel contact of the second step.

87 65 Figure 2.10 The last five forward and backward acceleration peaks of the walking trial. The data after the fourthto-last peak were discarded. The last peak shown is the result of the trail leg being brought forward.

88 2.9.2 Gait Analysis using the APDM Opal Extracting Raw Acceleration Data from the Opal In contrast to the IDEEA, the raw data from Opal had to be processed and filtered prior to analysis. This process was performed using MatLab Version for Windows. An individual program was created for each individual trial, although the general layout of the code was the same. There are two main differences between the APDM raw output and that of other triaxial accelerometers. The initial raw output of the Opal is in analog to digital conversion (ADC) counts, as opposed to the common accelerometer output in volts. To change the data to a voltage, the raw ADC data recorded in each direction was multiplied by a factor of 3/65536 (volts/bits), as instructed by the manufacturer. Secondly, the coordinate frame of the system has positive vertical axis pointing downward (Y-axis), positive mediolateral axis pointing left (X-axis), and positive mediolateral axis pointing backward (Z-axis). However, the typical right handed coordinate frame in gait analysis has these axes flipped, with positive vertical axis pointing upward, positive mediolateral axis pointing right, and positive mediolateral axis pointing forward. To correct the reference frame, accelerations recorded in all three directions were multiplied by negative one. Once the data was changed to voltage and the reference frame was transformed, a filter was applied to separate the signal of the walking trial from random noise. A fourth order, zero-lag butterworth filter was utilized for this purpose, which is a commonly used procedure with accelerometer outputs in gait studies. 54,86-88 According to Mathie et al., 66

89 accelerometers must be able to measure frequencies between 0-20 Hz when attached at the waist level. 54 With this knowledge, the cutoff frequency of the butterworth filter was set to 20 Hz. This exact filter has also been applied in other studies which analyzed accelerations at the level of the lower back. 59,86,87 The next step was to convert the raw voltage readings from the accelerometer to raw acceleration data in terms of units of gravity, g. The sensitivity and zero-offset data presented in Section 2.6 was used for this purpose. At any moment in time, the applied raw acceleration can be found from the voltage data using the equation,, (2.2) where is the acceleration, is the filtered voltage, is the zero-offset along the axis being examined, and s is the sensitivity along the axis being examined. The resulting acceleration is in the frame of reference of the orientation of the Opal. In order to analyze the acceleration of an individual as they walk, however, acceleration needs to be measured with respect to a global horizontal-vertical coordinate system with respect to the axes of the human body. 56,57 Due to the natural curvature of the human body and the inevitable tilting of the torso during gait, an accelerometer placed over the L3 vertebrae will also tilt regardless of how perfectly the accelerometer is originally aligned upon placement. 56 This means that in addition to accelerations recorded due to a change in velocity, acceleration due to gravity will also be recorded along the AP 67

90 and ML axes. In order to use the accelerometer readings to analyze gait, the influence of gravity was corrected for using the method outlined my Moe-Nilssen Transforming the Data from a Tilted Accelerometer to a Horizontal-Vertical Coordinate System The following process, as outlined by Moe-Nilssen, to correct for the tilt of a triaxial accelerometer uses the measured accelerations in the AP, ML, and vertical axes,,, and respectively, to approximate the true accelerations along the global AP, ML, and vertical axes, denoted by,,, and. 56 If is the angle between the horizontal, or transverse, plane of the accelerometer and, as shown in Figure 2.11, then the estimated acceleration along the true AP axis can be estimated by projecting the measured vertical and AP acceleration onto the transverse plane: (2.3) The vertical acceleration can then be corrected for with respect to : (2.4) This acceleration is denoted as because it only takes into account forward and backward tilt of the accelerometer,. The final estimation of must take into account side to side tilt of the accelerometer, or the angle between the horizontal plane 68

91 Figure 2.11 A graph of the measured acceleration relative to the AP direction,, plotted with the measured vertical acceleration,. The AP axis is in the transverse plane. Since the angle between and is 90, it follows that the angle between and the AP axis,, is the same angle between and the vertical axis and the measure ML acceleration,. This angle is represented by, as shown in Figure Similar to the estimation of, Moe-Nilssen found that the estimated acceleration along the true ML axis can be determined by projecting and to the transverse plane: (2.5) 69

92 Figure 2.12 A graph of the measured acceleration relative to the ML direction,, plotted with the vertical acceleration,. The ML axis is in the transverse plane. Since the angle between and is 90, it follows that the angle between and the ML axis,, is the same angle between and the vertical axis. Finally, the estimated vertical acceleration along the true vertical axis,, can be calculated by correcting for,, and the influence of gravity, or 1g: (2.6) In order to estimate these accelerations, and must be estimated as well. As shown in Figure 2.13, when the accelerometer is at rest, the only measured 70

93 acceleration is due to gravity. The component of gravity measured in the AP direction is defined as the hypotenuse, 1g, multiplied by the sine of. Using these measures of trigonometry, Moe-Nilssen states that the mean of the measured accelerations and will approach the values of and for a large number of measurements. 56 If a component of gravity is not recorded in the AP or ML directions, the mean values of the accelerations recorded in these directions during a walking trial would be zero. 56 Therefore, any non-zero value of and would be a result of an initial offset of the accelerometer. 56 The mean values of and, then, can be used as best estimates of and in Equations It follows then that, Figure 2.13 An example of acceleration due to gravity being measured in the AP direction due to an initial offset of the accelerometer. Since the vertical axis of the accelerometer is not aligned with the gravity vector, a component of gravity is recorded in the AP direction. 71

94 , (2.7) and, (2.8) The resulting final equations for dynamically estimating the AP, ML, and vertical accelerations at any given point in time with respect to a horizontal-vertical coordinate plane are found by substituting these values into Equations : (2.9) (2.10) (2.11) As long as the raw triaxial accelerometer was placed over the L3 vertebrae, the accelerations determined from these equations were independent of sensor placement. 8,55-57,61,63 This means that a plot of the acceleration signals obtained from the Opal could be used to analyze gait as outlined in the following section. 72

95 Conducting a Gait Analysis using Raw Accelerations To analyze gait speed, cadence, step length, and gait cycle duration, it was necessary to determine the instants of time during the walking trial that an individual s heel hit the ground. As performed in previous research, this feat is made possible by examining the acceleration patterns recorded in the anteroposterior direction at the level of the L3 vertebrae. 8,55,59 Figure 2.14 is an example of one complete gait cycle of an individual during a trial. When an individual s foot swings forward during walking, the body s center of mass (CoM) accelerates forward until the instant of initial heel contact. 8,55,59 The force of the heel contacting the ground causes a rapid deceleration of the body s CoM. The moment of heel contact, then, can be found by identifying the peak forward acceleration before the acceleration of the CoM changes from positive to negative. 8,55,59 An algorithm was created using Matlab in order to determine the instants of time when these peak accelerations occurred. This identification also made it possible to determine the beginning and end of each trial. Similar to the IDEEA, the start and end of a walking trial had to be identified among the other data because the Opal begins recording data as soon as it is disconnected from its docking station. As with the IDEEA, this time period was found by locating activities of the experimental procedure of standing for fifteen seconds, walking, and then standing again for fifteen seconds. Since output of the accelerometer is 0g in the AP direction during standing, the walking trial could be identified by locating a series of steps between two fifteen second durations of a 0g output. Once the trial was identified, 73

96 AP Corrected Acceleration (g) One Complete Gait Cycle in the AP Direction 0.4 X: Y: X: 23 Y: time (s) Figure 2.14 One Complete Gait Cycle in the AP Direction. The moment of heel strike is at the peak value before the drop in acceleration caused by heel strike. The data points in the graph identify these peaks in the figure. the first and last two steps taken had to be discarded before further analysis. As shown in Figure 2.15, the first peak is small, but can be identified by the rapid deceleration in the AP direction. The walking trial began at the second peak, or the moment of heel contact of the second step. 74

97 AP Corrected Acceleration (g) 0.6 The Start of a Walking Trial 0.4 X: Y: X: Y: time (s) Figure 2.15 A graph of the typical accelerations measured in the AP direction at the start of a walking trial. The first and second steps taken have been identified by data points. The beginning of the walking trial is at heel contact of the second step, or the second identified peak in the graph. The end of the trial was also identified using this peak recognition methodology. Figure 2.16 shows a typical acceleration pattern at the end of a walking trial. Unlike the IDEEA, bringing the trailing leg to rest next to the foot that took the last step did not register as a significant acceleration in the AP direction. This was due to the fact that acceleration is being measured at the level of the lower back, and bringing the trailing leg forward did not result in the body s CoM accelerating forward significantly. Instead, the 75

98 AP Corrected Acceleration (g) 0.8 The End of a Walking Trial X: Y: time (s) Figure 2.16 A graph of the typical accelerations measured in the AP direction at the end of a walking trial. The action of bringing the trail leg forward at the end of the trial can be seen by the small increase in acceleration between 37.8 and 38.5 seconds in the graph. The peak denoting the end of the walking trial has been identified by a data point. acceleration in the AP direction slowly increased after the last step and then became zero when a subject stopped walking. The end of the trial, as in Figure 2.16, was at the thirdto-last peak caused by heel contact in the AP direction. The number of steps taken during the trial could then be determined by counting the number of peaks which occurred over the duration of the trial. This was conducted by a program created using Matlab v , which is included in Appendix B. 76

99 Once the trial duration and number of steps taken were known, gait speed, cadence, step length, and gait cycle duration could be estimated using Equations ,59 Since the distance walked was approximately 50 meters, good estimates of the four gait parameters of interest are: (m/s) (2.12) (steps/min) (2.13) (m) (2.14) (s), (2.15) where t is the trial duration, is the number of steps taken over the course of the trial, and is the moment of time a peak in the accelerometer signal occurred corresponding to initial foot contact. Once all gait variables were determined using the raw accelerometer data, the gait parameters estimated using the Opal were compared to those recorded by the IDEEA. 77

100 2.9.3 Statistical Analysis Before the results obtained from the raw accelerometer data were compared to the output of the IDEEA, a two-way crossed ANOVA was conducted on both the raw accelerations and data estimated using the Opal. The influence that the terrain walked upon and the age of participants had upon the results were analyzed in a 2 2 factorial design using a two-way crossed ANOVA in Design Expert v software. This analysis was performed in order to compare the results of this study to previous findings only, as these analyses have been conducted in previous studies. Figure 2.17 shows the two-factor factorial design that was used in this study. The gait parameters estimated using the Opal were then compared to the IDEEA results using two statistical analyses. To check for systematic bias, paired t-tests were performed on each individual gait parameter. A minimum difference was set such that the IDEEA output and the Opal should have less than a 5% error. Secondly, intra-class correlations were investigated to determine the consistency of the differences between the two systems. ICC(3,2) was used in this study, indicating a two-way mixed model where there is no interaction between subjects and the method used to record gait variables. A single measure ICC was used, indicating that each gait analysis system was used independently. An absolute agreement was also set such that the same value should be recorded by each system for any given gait parameter of an individual subject. These two analyses were conducted on the data collected for each of the four possible combinations of walking surface and age group of participants. 78

101 Figure 2.17 A depiction of the two-factor factorial experiment in this study. Each corner point represents one test combination of age group and type of walking surface. Y: Subject Aged 18-38, O:Subject Aged 45-65, E: Even Ground, U:Uneven Ground 2.10 Investigating the Use of the IDEEA with Above the Knee Amputees As an extension of this work, this study also examined the use of the IDEEA with people who had suffered a transfemoral amputation. The IDEEA has also previously been used to analyze gait in populations with mobility impairment (degenerative knee arthritis, hemiparesis, cerebral palsy), but its use with amputees was not previously investigated before this study. 13,14,64 If the IDEEA is able to accurately analyze the gait of this population, then the performance of more advanced, microprocessor prosthetic knees can be investigated in real-world situations outside of level walking. It is in these situations 79

102 when the additional functionality of microprocessor knees may prove to aid above the knee amputees more than passive knees. 1,28,29, Amputee Subject Recruitment The target population for the amputee group in this study was healthy adults who had suffered an above the knee amputation and were prescribed either a microprocessor or non-microprocessor knee joint. Differences between the walking styles of the users of these two joints has been shown in previous studies. 1,2,19,26,28,36-39 However, these two type of knees have not been extensively examined when walking on uneven terrain. 1,28,39 Due to the special population being sought, amputee subjects were recruited without restrictions on age, cause of amputation, or specific type of knee joint. The number of amputees included was to depend upon the response obtained from recruiting efforts. Small cohorts of amputees have been used in research, especially in pilot studies, possibly because of the challenges of finding subjects to volunteer. 2,20,26,36,73 Subjects with a transfemoral amputation were recruited from four prosthetic clinics in the Dayton, Ohio area. One company specialized in lower limb prosthetics, while the other three offered a wide array of upper and lower limb prosthetic services and products. All of these locations provided amputees with prosthetic limbs, including the Otto Bock C-Leg and passive prostheses. As opposed to simply selling prostheses, these companies also fitted and trained people in the use of their prostheses. It was believed that amputee patients at these locations would be well trained in the use of their prosthesis and would be ideal subjects for the current study. The prosthetic clinics were 80

103 asked to place a flyer with tear-offs in their lobby area so that interested individuals could contact the researcher directly. As with non-amputees, amputee subjects were invited to a public park in Moraine, Ohio for testing. At the beginning of the session, the details of the study were explained and subjects were then given the chance to read through the informed consent form. Two people with a transfemoral amputation volunteered to participate in this study. One was recruited through efforts at a prosthetic clinic and the other was recruited by word of mouth. The subject demographics of each individual, including prosthetic components, are presented in Table 2.5. The experimental procedure conducted with each transfemoral amputee was the same as with the non-amputees of this study, except for a few changes in the sensor placement when using the IDEEA. Table 2.5 Characteristics of Subjects with Above the Knee Amputations Subject 1 Subject 2 Age Weight (kg) Height (cm) Gender Male Male Cause of Amputation Trauma Trauma Prosthetic Leg Otto Bock C- Leg Ossur Rheo Prosthetic Foot 1C31 Trias+ Ossur Ceterus 81

104 Using the IDEEA with People Who Use a Prosthetic Limb In Section 2.7, the correct placement of the IDEEA sensors on the chest, thighs, and feet was outlined. The attachment of these sensors to a prosthetic limb, however, has not been performed. On the limb to which the prosthesis is attached, the prosthetic socket extended above the area on the thighs where the sensors were attached. As a result, one of the thigh sensors was placed on top of the prosthetic socket instead of on the subject s skin. This did not impose a great procedural change, as the sensor was still placed halfway between the hip and the prosthetic knee joint. However, because there was no established standard for placing the sensors on a prosthetic foot, one had to be developed. In order to be consistent, the original plan was to place the foot sensor proportionally on the prosthetic foot with respect to the intact foot. First, the researcher placed one foot sensor on the intact foot of a person with an above the knee amputation. Next, the length and width of both the intact and prosthetic feet of the amputee were measured. The experimenter then measured the horizontal distance of the sensor from the insole where the big toe attaches to the foot (medial side of the joint where the first metatarsal and proximal phalange meet) and the vertical distance of the sensor from heel (posterior calcaneus). The position of the sensor was then to be proportionally matched on the prosthetic foot by approximating these landmarks. If the lengths of an individual s intact and prosthetic feet are known, the distance from the heel to the bottom of the sensor on the intact foot (V intact ) can be measured to find the correct positioning on the prosthetic limb (V prosthesis ) using: 82

105 (2.16) Using cross-multiplication and simple algebra, it follows that: (2.17) The horizontal distance from the inside of the prosthetic foot to the innermost side of the sensor, H prosthetic, can be found in the same mathematical manner, resulting in:, (2.18) where H intact is the horizontal distance from the inside of the intact foot to the innermost side of the sensor. With both subjects, however, the anatomy of the prosthetic feet was not sufficient to place the sensors at this location. The Otto Bock 1C31 Trias and the Ossur Ceterus both use arched blades to mimic the motion of a normal human foot. As a consequence, the area where the foot sensor would be placed was directly under a hard, weight-bearing portion of this blade. This was true for both subjects feet, even though both had a soft cosmetic covering over the foot. In order to prevent damage to the sensor, the foot sensor was place approximately 2 cm farther toward the heel, closer to the middle of the foot. As a result, Equation 2.17 became: 83

106 (2.19) An example of these measurements is shown in Figure Other than the placement of the IDEEA sensors, the experimental protocol, gait analysis using the IDEEA sensors, and gait analysis using the Opal was the same for subjects with above the knee amputations as it was for non-amputee subjects. The gait speed, cadence, step length, and gait cycle duration obtained by the IDEEA were Figure 2.18 A depiction of the two measurements that were taken on the intact foot to locate the MiniSun foot sensor on an amputee subject s prosthetic foot. The horizontal measurement (H intact ) is from the inside of the foot to the innermost side of the foot 84