Methods for movement monitoring and daily-life physical activity classification

Size: px

Start display at page:

Download "Methods for movement monitoring and daily-life physical activity classification"

Adam Burns
5 years ago
Views:

1 Milan, August 25th 2015 Methods for movement monitoring and daily-life physical activity classification Speaker: Anisoara Paraschiv-Ionescu Ecole Polytechnique Federale de Lausanne

2 Applications Clinical Sport Robotics Human-computer interface etc

3 HEALTH concept a resource for everyday life health is viewed holistically as an interacting system with mental, emotional and physical components ( health triangle )

4 HEALTHY status Activities of daily living Physical Activities in various contexts

Physical Activity characteristics Type and

5 Physical Activity characteristics Type and event sitting, standing walking, sit-stand, fall Duration activity/sedentary periods, sit-stand duration Frequency how often Intensity acceleration, velocity cadence Pattern Temporal sequences Context indoor/outdoor

Quantified PA features allow to: Assess and maintain healthy

energy expenditure Assess and prevent risk in many disease

Shorter walking periods Less frequent walking periods Lower

postures Avoidance of physical activity Less variability in

6 Quantified PA features allow to: Assess and maintain healthy status: Outcomes for functional performance Association with energy expenditure Assess and prevent risk in many disease Frail elderly, Stroke, Cardiac, Obesity, Parkinson disease Shorter walking periods Less frequent walking periods Lower gait speed Longer rest periods Slower transfer between postures Avoidance of physical activity Less variability in activity Assess efficiency of treatment/rehabilitation aiming to restore health

7 Outline Introduction Basic approaches for PA classification Monitoring technologies Analytical techniques Advanced concepts for PA assessment PA patterns/time-series Inferred PA context PA barcode & Complexity concept Conclusions & Perspectives

8 Basic approaches for PA assessment

Monitoring technology today Consumer devices Pedometer, Smartphone,

(sensors & electronics for data recoding) Multimodal sensing (inertial &

parameters: step count, sit, stand, lie, walk, gait, energy expenditure,

9 Monitoring technology today Consumer devices Pedometer, Smartphone, Fitness tracker High rate of decline after one year* Functionality? Validity? Usability? Smartphone Yamaxx DigiWalker Research oriented devices Data logger (sensors & electronics for data recoding) Multimodal sensing (inertial & barometric pressure) Access to raw data Algorithms for multiple parameters: step count, sit, stand, lie, walk, gait, energy expenditure, fall Validation Nike Fuelband Fitbit One Jawbone UP *

10 Consideration for PA assessment using wearable devices Single Multiple Body Location & number Algorithms: PA classification Movement analysis Practical considerations: Subject s Comfort Number of parameters Accuracy

parameters Maximal accuracy Algorithms: PA classification Movement

11 Consideration for PA assessment using wearable devices Single Multiple Body Location & number Minimal/single location Maximal number of parameters Maximal accuracy Algorithms: PA classification Movement analysis Practical considerations: Subject s Comfort Number of parameters Accuracy

12 Outcomes: PA quantity & quality PA Classification Movement Analysis Raw sensor data Turning, Fall, upstairs Postural transfer duration smoothness acceleration Gait Speed Cadence Stride length Gait cycle time Double support etc Outcomes

Fuzzy-logic - Decision trees Event-driven Preprocessing raw data: - alignment - filtering - pattern enhancement Windowing: Variable size: -

13 PA classification: Main approaches Epoch-based Preprocessing raw data: - alignment - filtering Windowing: Fixed size Feature extraction: Statistical: - time-domain - frequency-domain - wavelet Feature selection Classification schemes: - Machine learning - Hidden Markov Models - Fuzzy-logic - Decision trees Event-driven Preprocessing raw data: - alignment - filtering - pattern enhancement Windowing: Variable size: - Event-based Feature extraction: Heuristic: - Biomechanically inspired Classification schemes: - threshold based - binary decision trees - Fuzzy-logic

14 Epoch-based approach: processing steps Preprocessing & Windowing Feature extraction & selection Classification w1 w2 w3 w4 w1 w2 w3 w4 w5 Classifiers performance depend on: - sensor orientation, signal-to-noise ratio: work with acc. norm, calibration, filtering - segmentation techniques: sliding non-overlapping, sliding overlapping, etc. - window size, percent of overlapping Refs: [1] Banos et al., Sensors, 2014; [2] Khusainov et al. Sensors, 2013; [3] Manini & Sabatini, Sensors, 2010; [4] Preece et al., Physiol. Meas., 30, 2009

15 Feature extraction & selection Refs: [1] Banos et al., Sensors, 2014; [2] Khusainov et al. Sensors, 2013; [3] Manini & Sabatini, Sensors, 2010; [4] Preece et al., Physiol. Meas., 30, 2009

16 Feature extraction & selection Refs: [1] D. Figo et al. Personal and Ubiquitous Computing, 2010; [2] Khusainov et al. Sensors, 2013; [3] Manini & Sabatini, Sensors, 2010

Classification Methodological considerations:

17 Classification Methodological considerations: - Classifier types: Decision trees Machine learning Fuzzy logic HMM - Cross-validation techniques - Validation data - Population Refs: [1] Lara & Labrador: IEEE Comm. Surveys, 2013 [2] Gyllensten & Bonomi, IEEE Trans Biomed Eng, 2011

18 Epoch-based approach: algorithm considerations Advantages: Ease to implement Possibility for real-time processing Limitations: Performance sensitive to Feature set Validation data Window type/size

19 Event-driven approach Postural transition as PA event Trunk tilt acc=1 acc=0 B. Najafi, et al,.,ieee Trans. Biomed. Eng., 2003 Paraschiv-Ionescu et al, Gait & Posture, 2003

20 20 Si-St/St-Si detection using single IMU on trunk Postural transition Trunk tilt ( ) [deg] Healthy Parkinson Time, s Si-St/St-Si Sensitivity of 83%-94% ˆg aˆ trunk Time (s) tmaxa { ( ˆ trunk )} Logistic Regression ˆg ˆ ( ) Max a trunk ˆ ( ) Min a trunk a trunk ˆ ( ) Min g tmina { ( ˆ trunk )} Salarian et al, IEEE Trans Biomed Eng, 2007

21 Barometric pressure (BP) sensor for detection of Si-St/St-Si transitions Hypothesis: Measurement of trunk elevation can significantly improve Si- St/St-Si recognition in mobility-impaired patient population Limitations of BP sensors: Sensitivity to environmental conditions (temperature, weather, air conditioning), noisy output Solution: Patterns enhancement (sinus fitting model) Massé et al, EPFL Thesis, 2014

Si-St/St-Si detection & classification using

{ ( ˆ trunk )} ˆ ( ) Max a trunk a ˆtrunk ˆg

22 Si-St/St-Si detection & classification using single multimodal sensor on trunk IMU & Barometric pressure Physilog (GaitUp, CH) Multimodal set of features ˆg aˆ trunk tmaxa { ( ˆ trunk )} ˆ ( ) Max a trunk a ˆtrunk ˆg ˆ ( ) Min a trunk ˆ ( ) Min g tmina { ( ˆ trunk )}

classifier Transition detection P tr Biomechanics/ Behavior Inspired rules Outcomes

23 PA classification & Movement Analysis Event driven, single sensor algorithm Gait Detection Walking periods Trunk Sensor Lying detection Lying periods Fuzzy classifier Transition detection P tr Biomechanics/ Behavior Inspired rules Outcomes P SiSt Refs: [1] Salarian et al. (2007), IEEE-TBME, [2] Massé et al. Journal of Neuroeng. and Rehab

24 Si-St/St-Si detection & classification using: - single IMU on thigh - 2 IMUs on trunk & thigh Trunk tilt acc=1 acc=0 aft _ LPF ( mg) aft _ DWT( g) sitting sitting aft _ LPF( g) standing aft _ LPF( g) standing Time (s) Paraschiv-Ionescu et al, Gait & Posture, 2003 Time (s)

25 Event-driven approach: algorithm considerations Advantages: performance robust across different populations allows to characterize specific movements such as postural transitions, gait (includes implicitly Movement analysis ) Limitations: complexity no adapted for real-time processing

26 Outcomes: PA & movement features changes with aging and disease Transfer sit-stand duration sit-stand smoothness number of transfers Activity duration gait velocity/cadence Pattern? time

27 Advanced concepts for PA assessment PA patterns/time-series Inferred PA context PA barcode & Complexity concept

28 PA patterns/time-series 28 Definition: Temporal sequence of PA parameters Hypothesis: time-dimension may reveal aspects of behavior and allows to devise new PA parameters Refs: [1] Paraschiv-Ionescu et al., Physical Review E, [2] Paraschiv-Ionescu et al., Plos One, 2012 [3] Paraschiv-Ionescu et al., Scientific Reports 2013 (

29 Inferred PA context Time + Questionnaire Heuristic hypothesis Outdoor Indoor Moving outdoor in elderly people: assessment based on accelerometer data

30 Study design Monitoring 120 subjects: 100 community dwelling persons (mean age 79ys) 20 older persons in a rehabilitation center 24h monitoring, IMU device attached on chest Reference data from subject s report: Did you leave your house during the measurement? If so, from when to when?

31 Algorithm Reference data 3D trunk acceleration (a x, a y, a z ) true indoor true outdoor a a 2 x a Detect & characterize each walking episode 2 y a 2 z Plot cumulative distribution true indoor Exploratory set max number steps for indoor walking (Max_indoor) duration (d) number of steps (#stp) timing Test set #stp > Max_indoor No estimated indoor Yes estimated outdoor Merge episodes that succeed at < 5 min A paraschiv-ionescu et al, ISPGR Congress, 2012 time spent outdoor outdoor activities per day

32 Algorithm 1) detect and characterize each walking episode number of steps, occurrence time time-series true indoor subject true outdoor subject

33 Algorithm 2) plot the CPD of number of steps using pooled exploratory dataset to determine the maximum number of steps for true indoor cumulative probability distribution (CPD) Empirical CPD: 'true indoor' nb steps for each walking period

34 Results: typical examples true indoor estimated indoor true outdoor estimated outdoor

35 Results: overall performances 100 community dwelling + 20 rehabilitation setting true indoor = 34 (n=10,exploratory, n=24,test) true outdoor = 86 (n=15,exploratory, n=71,test) estimated indoor estimated outdoor true indoor true outdoor accuracy 93% sensitivity 84% specificity 97% positive predictivevalue( PPV ) 91% negative predictivevalue( NPV ) 94%

36 PA barcode and Complexity Time Time From Physiological to PA complexity: The concept of complexity loss with age & disease *L.A. Lipsitz & Goldberger, JAMA, 1992

37 Mapping PA dimensions into Barcode Time Paraschiv-Ionescu et al. (2012), PLoS ONE

38 PA barcodes: meaningful information - Diversity of states - Diversity and dynamics of states Complexity - Diversity and intensity dynamics of states A. Paraschiv-Ionescu et al., PLoS ONE, 2012

39 Quantifying Complexity Entropic measures: Shannon Entropy Lempel-Ziv complexity Weighted permutation entropy Each measure captures a different aspect of barcode complexity

40 The concept of complexity in medical research

41 Path from Healthy to Age & Disease/Frailty Components Level Behavioral Level Functional Level L.A. Lipsitz & Goldberger, JAMA, 1992

42 Why quantifying PA complexity? Standard metric Complexity metric % time walking Lempel-Ziv age age empirical evidence of Lipsitz s theoretical concept of physiological complexity loss with aging PA complexity could be predictive signature of transition to frailty *L.A. Lipsitz & Goldberger, JAMA, 1992

Conclusion & Perspectives Physical activity can be objectively estimated during daily conditions using minimal body worn sensors Postural Transitions/Allocation/Patterns provide clinically useful

43 Conclusion & Perspectives Physical activity can be objectively estimated during daily conditions using minimal body worn sensors Postural Transitions/Allocation/Patterns provide clinically useful metrics to characterize healthy status Activity trackers in the context of mobile healthcare: 43 Conclusions and perspectives Great potential, useful in many clinical fields Need improvement in functionality (few outcomes from big data!) Need clinically interpretable outcomes Should be reliable (Technical and clinical) Should de usable: smart clothing, patch, subject specific Need connectivity (Wearable/Smartphone/Smart environment) Sheng Xu et al. Science, 2014

Ambulatory monitoring of gait quality with wearable inertial sensors

Ambulatory monitoring of gait quality with wearable inertial sensors Dr. Philippe Terrier, PhD June 2016 Summary 1. Why? Reasons for measuring gait in real life conditions 2. What? Real-life assessment