1 INEGI Instituto de Engenharia Mecânica e Gestão Industrial, Porto, Portugal, FEUP Faculdade de Engenharia,

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1 REGISTRATION BETWEEN DATA FROM VISUAL SENSORS AND FORCE PLATFORM IN GAIT EVENT DETECTION Sousa, Daniela Sofia S., Tavares, João Manuel R. S., Correia, Miguel V., Mendes, Emília, Veloso, António 4, Silva, Vera 4, João, Filipa 4 INEGI Instituto de Engenharia Mecânica e Gestão Industrial, Porto, Portugal, FEUP Faculdade de Engenharia, Universidade do Porto, Portugal INEB Instituto de Engenharia Biomédica, Porto, Portugal, FEUP CRPG Centro de Reabilitação Profissional de Gaia, Arcozelo, Portugal 4 FMH Faculdade de Motricidade Humana, Universidade Técnica de Lisboa, Portugal Abstract A main requirement in clinical gait analysis is the ability to accurately identify gait events; especially, the initial contact of the heel with the floor and the toe off. The knowledge of the major events of the gait cycle is needed, for instance, in biomechanical data normalization and in the calculation of several temporal/distance parameters. The most common technologies for gait event detection are foot switches and force platforms; however, if the same performance could be achieved, it would be preferable to use the information collected by visual sensors to detect the main gait events. This paper proposes a procedure to be used in the detection of not just stance phase events (i.e. initial contact, opposite toe off, heel rise, opposite initial contact), but also of swing phase events (i.e. toe off, feet adjacent, tibia vertical) through the analysis of visual gait data acquired by image cameras. Moreover, in this paper, it is compared the performance of detecting the initial contact and toe off using our visual methodology and the information obtained from force platforms. Introduction In gait analysis, gait cycle designates the time interval between two successive events of walking, usually between to successive heelstrikes of the right foot. Typically, a gait cycle is subdivided into a stance phase, while the both feet are on the ground, and a swing phase, when just one foot in on the ground. According to [], these gait phases can still be divided in loading response, mid-stance, terminal stance, pre-swing and initial swing, mid-swing, and terminal swing, respectively. The identification of the stance and swing phases requires the detection when the foot touches the ground (generally, referred as heelstrike ) and the detection of the moment when the foot leaves the ground (normally, cited as toe off ). For other gait sub-phases is also necessary to detect the heel rise when the heel begins to lift the ground, the feet adjacent when the two feet are side by side and the tibia vertical when the tibia of the swinging leg becomes vertical. In clinical gait analysis, the most common reasons to detect gait events are the normalization of the measured data and the calculation of temporal/distance. Thus, gait events such as the initial contact and toe off, can be used to calculate stride and step lengths, cadence, speed, single and double support time, etc. Consequently, accurate identification of gait events is fundamental in clinical gait analysis; especially, the identification of the toe off and of the initial contact. In this paper is proposed a methodology for gait events identification based on visual data. Special attention is made not just in the detection of stance phase events (initial contact, opposite toe off, heel rise and opposite initial contact), but also to the recognition of swing phases events (toe off, feet adjacent and tibia vertical). Finally, our visual-based algorithm, used in this work for the detection of the initial contact and the toe off, is validated, considering the detection of the start and end of the contact phases, by the usage of the information obtained by force platforms. Methods In this work is considered the normal gait data obtained from visual sensors and from a force platform of seven non-disabled subjects ( men, 4 women, average age of years, average weight of 7 kg and average height of 7 cm). To these individuals were asked to perform four walking trials of 6 m length, barefoot, at a self-selected normal speed, in such a way that they could reliably land their right foot on the force platform used that was placed in the middle of the walk course. Twenty-one retro reflective markers were fixed onto the subjects body, according to the marker-set recommended by Simi Motion (Simi Reality Motion Systems, Unterschleissheim, Germany). For the presented study, the most relevant markers are the forefoot right/left marker, placed directly over the second metatarsal, and the heel right/left marker, on the posterior surface of the calcaneous just above the floor level. The visual paths of those markers were recorded using a four cameras setup, at a sampling frequency of 5 Hz, and the Simi Motion analysis software. On the other hand, the ground reaction force (GRF) was obtained using a force platform (Kistler Instrumente AG, Winterthur, Switzerland) at a sampling frequency of

2 Hz. The synchronization between the cameras and the force platform used was done manually by adjusting the starting frame of each camera in the Simi Motion software. Our event detection methodology was used to automatically estimate the following events: initial contact (IC), heel rise (HR), toe off (TO), feet adjacent (FA) and tibia vertical (TV). The algorithm used consists in filtering the kinematics data in both directions, using a second order Butterworth filter with a cut-off frequency of Hz. For each event detection, the width of the searching window used is defined based on the assumption that the peak of the z-coordinate of heel right marker occurs at the toe off instant (6 % of time cycle) [4], on percentages of the typical gait intervals according to [6] and on the duration of the gait cycle in analysis. Empirically, it was estimated the error associated with the Pappas s assumption [4]: 6.6 % of the duration of the cycle in analysis. So, in the algorithm was used a gap before and after of the pretended event to be detected at least of % of the cycle duration. The main objectives of this search restriction are to adjust the event detection to the corresponding gait cycle and to respect some additional conditions needed for the detection of a particular event. The initial contact is identified in our algorithm by searching for an absolute minimum in the z- coordinates of the heel marker. The chosen width of the searching window was from last TO till the next TO. The heel rise event is determined by finding the time when the heel marker start moving in the sagital and frontal planes. A pair of thresholds are used to characterize the start of the movement in sagital and frontal planes. They are defined as equal to the maximums values in the corresponding non-movement phase of these signals (from opposite TO till % of cycle duration). The width of the search window used is chosen from the opposite TO to TO (Graph ). The first peak of the forefoot vertical coordinates difference from HR to PJ corresponds to the toe off instant. Feet adjacent is the instant when the progression coordinates of the heel markers assume similar values. Thus, the FA search window used in our methodology starts on the opposite IC and ends on IC (Graph ). Finally, tibia vertical event, as the name suggest, occurs when the angle between the tibia and the plane of the movement is around 9º. However, the method used in the Simi Motion software for the calculation of the shank angles gives maximums at tibia vertical instants. A search restriction at this event is approximately at the opposite TO where the tibia is also in a vertical position, thus TV event will be between TO and IC (Graph ). Graph. CI detection (left figure), HR detection (right figure); search windows are defined by circles and the selected instant is marked by an asterisk. z right heel (m) z and y right heel (m) y heel z heel Graph. TO detection (left figure), FA detection (right figure); search windows are defined by circles and the selected instant is marked by an asterisk..5 z forefoot y forefoot y heel right y heel left z and y right forefoot (m).5 y right and left heel (m)

3 Graph. TV detection; search window is defined by circles and the selected instant is marked by an asterisk. 8 Right shank angle (º) In order to verify how accurate the detection of the initial contact and toe off events by the algorithm proposed is, we compare the events detected with the ones obtained from the data of force platforms. Thus, the IC event was determined as the time at which the vertical component of ground reaction force exceeded N and the TO event when the vertical component falls bellow 5 N. The differences between the detections obtained by our visual algorithm and the detections obtained from the data of the force platform were calculated. Also, using the Bland and Altman s method, a descriptive statistical comparison of our technique and the golden standard was established. In addition, to test the success of HR, PJ and TV detection, the averages of the normalised durations of loading response, mid-stance, terminal stance, pre-swing, initial swing, mid-swing and terminal swing, were calculated. All these time intervals were compared with the intervals defined in [, 6]. Thus, for each subject in analysed the cycle time was calculated based on the assumption that the time from IC to TO in data obtained from the force platform corresponds to 6 % of the gait cycle duration. Finally, the previous results were compared using the data obtained from visual sensors and from the force platform used of 7 normal individuals (7 men, average age of 4 years, average weight of 76 kg and average height of 74 cm) acquired in the gait laboratory of the Faculdade de Motricidade Humana da Universidade Técnica de Lisboa, Portugal. That laboratory is equipped with four high-speed cameras setup, at a sampling frequency of Hz and automatically synchronized with the force platform (Kistler Instrumente AG, Winterthur, Switzerland) working at a sampling frequency of Hz, and the Simi Motion analysis software as well. Results The comparison between our visual algorithm and the well-established force platform based method is summarized in Graph 4-7. Our algorithm analyses showed that the setup with high-speed cameras has mainly more accurate and more precise results than the setup with normal speed cameras. All the cases of DA identification are within frames for both setups and the TO detections are within frame for the setup with high-speed cameras and approximately 6 frames for the setup with normal speed cameras. Bland and Altman s analysis for the setup with normal speed cameras give an error of -.8 ±.5 s (mean ± standard deviation) for IC detection, against -.9 ±.5 s for TO detection. Instead, the setup with high-speed cameras has errors of.7 ±.64 for IC detection and.5 ±.5 for TO detection. Bland Altman also suggests the order of the events detection. A negative mean value indicates a previous detection of the gait event by the visual methodology, an opposite situations occurs for positive values. The detection order for both setups do not agree, but as in the setup with high-speed cameras the synchronization is automatically, we would consider it as the best setup to verify the behaviour of our visual algorithm. One justification for that is that the IC and TO events are probably identified sooner by the force platform based method. In Graph 7 it is showed the averages of the normalised durations of the gait cycle intervals for our sample. The last representation used in this graph permits to compare the performance of our algorithm with the expected intervals durations described by Perry in [6]. Table combines all the results in a friendlier format and suggests the behaviour of our visual algorithm. From these results, it can be seen that IC, DA and HR events are detected too late by our visual algorithm.

4 Graph 4. Setup based on normal speed cameras: Plot of the mean differences between the diction using our visual algorithm and the force platform based method, against means of both methods for IC (left images) and TO (right images) detections. (Horizontal lines represent ±*standard deviation (SD).) Graph 5. Setup based on high-speed cameras: Plot of the mean differences between the detection using our visual algorithm and the force platform based method, against means of both methods for IC (left images) and TO (right images) detections. (Horizontal lines represent ±*standard deviation (SD)) Graph 6. Setup based on normal speed cameras: Histogram of absolute time differences between our visual algorithm and the force platform based method (the x axis is divided in ms intervals); Left image, initial contact results; right image, toe off results

5 Graph 7. Setup based on high-speed cameras: Histogram of absolute time differences between our visual algorithm and the force platform based method (the x axis is divided in ms intervals); Left image, initial contact results; right image, toe off results Graph 7. Normalized average gait interval durations for setup based on normal speed cameras. Upper image, our visual algorithm on data from the setup with normal speed cameras; lower image, our visual algorithm on data from the setup based on high-speed cameras; Perry s interval gait characterization ( %, %, %, %, %, 4 % and %). % % 4% 6% 8% % Loading response Mid-stance Terminal stance Pre-swing Initial swing Mid-swing Terminal swing % % 4% 6% 8% % Loading response Mid-stance Terminal stance Pre-swing Initial swing Mid-swing Terminal swing Table. Summary of our visual algorithm s performance (without the IC atypical detection and a TV atypical detection) on data from the setup with high-speed cameras; cells with - represent an inconclusive result (to be continued). Bland and Gait phases Difference % Hypothesis Most probable hypothesis Loading response >.6 Mid-stance > 6. Terminal stance < 9. Pre-swing >. Initial swing >.9 Altman IC detected too soon No TO detected too late Yes TO detected too soon No HR detected too late - HR detected too late - IC detected too soon No TO detected too soon No IC detected too late Yes FA detected too soon - TO detected too late Yes TO detected too late HR detected too late HR detected too late IC detected too late -

6 Table. Summary of our visual algorithm s performance (without the IC atypical detection and a TV atypical detection) on data from the setup with high-speed cameras; cells with - represent an inconclusive result (continuation). Bland and Gait phases Difference % Hypothesis Altman TV detected too late - Mid-swing < 4.8 FA detected too soon - Terminal IC detected too soon No swing >.4 TV detected too late - Most probable hypothesis - - Discussion The proposed algorithm agrees sufficiently well with the force platform based method to be able to replace the need of information based on the ground reaction force. This is particularly noticeable in the setup with high-speed cameras. The difference in the performance of our algorithm on data from the highspeed cameras and on data from the normal cameras can be a good pointer of the error associated with the manual synchronization (between the cameras and the force platform used) that was done in the setup based on normal cameras. Between the two setups considered there was a difference around.45 s in the precision of the results obtained by our algorithm. With the setup based on high-speed cameras was obtained an average difference of.7 ±.64 s (mean ± standard deviation) in IC detections and.5 ±.5 s in TO detection. All the cases are within.5 s (.4 % of the average gait cycle time) for IC detection and.55 s (5. % of the average gait cycle time) for TO detection. The setup based on normal speed cameras permitted an average difference of -.8 ±.5 s (mean ± standard deviation) in IC detection, against -.9 ±.5 s in TO detection. All cases are within. s (9.6 % of the average gait cycle time) for IC detection and.49 s (4 % of the average gait cycle time) for TO detection. Ghoussayni [] reports average differences and absolute average differences within.5 s (. % of the average gait cycle time) for initial contact detection and within.7 s (5.6 % of the average gait cycle time) for toe off detection and concluded that their algorithm was sufficiently reliable and accurate. In [] the average difference is about.6 ±.5 s in IC detection and.9 ±.5 s in TO detection. The gait intervals durations for the setup with normal speed cameras gave results more close to the expected duration of these intervals, that can be justified with the errors associated with the manual synchronization. These errors may not show the visual algorithm s behaviour. According with [5], whenever a gait phase corresponds to % of the cycle time the error should be bellow. s, which corresponds to.4 % of our average gait cycle time. Through the gait intervals detection on data from the setup based on high-speed cameras, this work also suggests some improvements in our visual algorithm. HR, IC and TO detection should be identified in a sooner instant to better agree with the Perry s intervals duration and the instants detected by the force platform based method. An advantage that our visual algorithm presents is that can be applied to other gait data without the need of additional adjustments. In the present study, the gait cycle duration was manually introduced in our algorithm; however, whenever there is more than one gait cycle analyzed, this information can be automatically obtained from the time between two successive maximums of the heel s vertical position. Acknowledgments This work was done in the scope of the projects: ACTIDEF Avaliação Computacional e Tecnológica Integrada do Desempenho e Funcionalidade dos Cidadãos com Incapacidades Músculo-esqueléticas (reference 4/4./C/REG, financially supported by POS-Conhecimento Programa Operacional Sociedade do Conhecimento) and Segmentation, Tracking and Motion Analysis of Deformable (D/D) Objectsusing Physical Principles (reference POSC/EEASRI/5586/4, financially supported by FCT - Fundação para a Ciência e a Tecnologia from Portugal). References []. O'Connor C. M. et al. (6). Gait & Posture In Press, Corrected Proof. []. Ghoussayni S. et al. (4). Gait & Posture,, []. Whittle M. (). Gait analysis an introduction. Oxford Boston, Butterworth-Heinemann. [4]. Pappas I. P. I. et al. (). Neural Systems and Rehabilitation Engineering, IEEE Transactions on, 9, -5. [5]. Mickelborough J. et al. (). Gait & Posture,, -7. [6]. Perry J. (99). Gait analysis: Normal and Pathological Function. Thorofare, New Jersey.