Applications of Inertial Measurement Units in Monitoring Rehabilitation Progress of Arm in Stroke Survivors

Transcription

1 Applications of Inertial Measurement Units in Monitoring Rehabilitation Progress of Arm in Stroke Survivors Committee: Dr. Anura P. Jayasumana Dr. Matthew P. Malcolm Dr. Sudeep Pasricha Dr. Yashwant K. Malaiya Saket S. Doshi Thesis Defense August 16, 2011

2 Motivation ~0.7 M/year experience partial paralysis after stroke Health care cost $73.7 billions Constraint Induced Movement Therapy (CIMT) works CIMT lacks a method to monitor rehabilitation progress remotely American Stroke Association 2

3 Contributions Affected Arm Usage Histogram Metric Motion Tracking 3

4 Constraint Induced Movement Therapy (CIMT) learned non-use Promote affected arm use Constrain unaffected arm Current tracking method Source Touch sensor and house diary 4

5 Constraint Induced Movement Therapy (CIMT) Duration of constraint worn Arm being rehabilitated 5

6 Constraint Induced Movement Therapy (CIMT) Duration of arm movement Arm being rehabilitated 6

7 Constraint Induced Movement Therapy (CIMT) CIMT is administered in 2 classes 1. Activities of Daily Living Perform activities of daily life (comb hair, wipe, clean, button up) for minutes 2. Shaping Perform a particular activity multiple times 7

8 Contributions Affected Arm Usage Histogram Metric Motion Tracking Quantify Count Activities 8

9 Sensor 3D Accelerometer Range 1.5g to 6g Bluetooth Sampling 10Hz Lightweight Gross motor skills 9

10 Detecting Affected Arm Use 10

11 Arm Usage Detection Results X Usage Y Usage Z Usage Usage % % % % Arm usage along 3 sensing axes of accelerometer offers more information than the currently used method Most of the patients undergoing CIMT do not exhibit smooth movements Algorithm will not work with smooth movements not exhibiting sudden changes in accelerometer output 11

12 Counting Activities dead 12

13 Contributions Affected Arm Usage Histogram Metric Motion Tracking Quantify Count Activities Tracking Progress Ranking 13

14 Box and Block Test (BBT) Source NeuroRehabilitation Research Laboratory The objective test developed at NeuroRehabilitation Research Laboratory at CSU Designed to measure functional changes in reach, grasp, and release Affected arm s performance is judged against unaffected arm s 14

15 Box and Block Test (BBT) Source NeuroRehabilitation Research Laboratory Live observations Associated with one motion (task) Time constrained Bulky setup 15

16 Histogram Based Objective Metric Not constrained by time Must complete functional tasks No manual intervention Histogram normalized over all movements Multiple tasks Calculate a scalar quantity (distance) Histogram of distance traversed over a time duration Compare it against unaffected arm s histogram 16

17 NORMALIZED FREQUENCY Effect of Window Width AFFECTED ARM DAY WISE COMPARISON day1 day2 day3 day4 day5 day6 day7 day8 Unaffected Avg DISTANCE (cm) 17

18 NORMALIZED FREQUENCY Temporal Resolution Individual Day Histogram day1 day2 day3 day4 day5 day6 day7 day8 Unaffected Avg DISTANCE (cm) 18

19 NORMALIZED FREQUENCY Temporal Resolution 2 Day Average Histogram st 2D avg. 2nd 2D avg. 3rd 2D avg 4th 2D avg DISTANCE (cm) 19

20 Sensor Trial Setup 5 subjects varying functional ability 4 activities Activity Motor Skills Dimensionality Description activity 1 Gross 1D (Y) Slide in 10 gunny bags activity 2 Fine 3D Fill in egg crate with cotton balls activity 3 Gross 2D (X and Y) Lifting an empty plastic cup activity 4 Gross 1D (X or Z) Sliding out curtain rings from shower curtain bar 20

21 Histogram Based Ranking 21

22 Histogram Based Ranking Participant Box and Block Test Rank (# of blocks) Activities activity 1 activity 2 activity 3 activity 4 Average (All activities) Subject 2 1 (45) Subject 4 2 (19) Subject 3 3 (17) Subject 5 4 (11) Subject 1 5 (03)

23 Histogram Based Ranking Participant Box and Block Test Rank Activities activity 1 activity 2 activity 3 activity 4 Average (All activities) Average (Non 3D activities) Subject 2 1 (45) Subject 4 2 (19) Subject 3 3 (17) Subject 5 4 (11)

24 Automating Histogram Based Ranking Visually observing histogram Observational errors Affected arm Unaffected arm Frequency decreases as distance increases Higher distance is relevant Weighting function 24

25 Automating Histogram Based Ranking Participant Box and Block Test Rank Activities activity 1 activity 2 activity 3 activity 4 Average (Non 3D activities) Subject Subject Subject Subject

26 Contributions Affected Arm Usage Histogram Metric Motion Tracking Quantify Count Activities Tracking Progress Ranking Analyze Errors Algorithm 26

27 Motion Tracking for CIMT Arm movement rehabilitation Necessary information for therapist to assess progress Current systems (robotics, optical) require Specialized hardware/software Trained staff to operate these systems High investment Not portable for deployment at patient s home Obstruct/impede affected arm movements M. Johnson, K. Wisneski, J. Anderson, D. Nathan, R. O. Smith, Development of ADLER: The Activities of Daily Living Exercise Robot, First Annual Conference on Biomedical Robotics (BioRob 2006) by IEEE Robotics and Automation Society (IEEE-RAS) Engineering Medicine and Biology Society (EMBS), Pisa, Italy, February,

28 Sensor Atomic 6DOF 3D Accelerometer & three 1D gyroscopes Sampling at 100Hz Accelerometer range 1.5g - 6g Source Accelerometer resolution g/(adc count) ~4cm/s 2 28

29 State of Motion Tracking with Inertial Sensors Source J. Torres, B. O Flynn, P. Angove, F. Murphy, C. O Mathuna, Motion tracking algorithms for inertial measurement, ACM Conference

30 State of Motion Tracking with Inertial Sensors Motion Inertial Sensors Rotational Gyroscope Linear Accelerometer 30

31 Experiments in Sensor Calibration θ Samples Average (ADC Count) Standard deviation Actual acc (m/s 2 ) ADC Count for 1g Resolution (m/s 2 ) X g sin(θ) θ g Y Z g cos(θ) 31

32 Kalman Filter s k Position at time step k v k Velocity at time step k a k Acceleration at time step k Q k Covariance of state estimation R k Covariance of measurement equation 32

33 Human Arm Linear Motion Simulator Minimum Jerk Theory Almost bell shaped velocity profile Generate velocity profile duration of movement and peak amplitude as parameters Derive acceleration and distance from velocity Tamar Flash, Models of Human Arm Trajectory Control, IEEE Engineering in Medicine and Biology Society 10 th Annual International Conference,

34 Filter Performance Analysis Optimal parameters for Kalman filter (empirically determined) Q k = α = 1 Still some error in filter output as compared with the ideal signal Error accumulation distorts results over time 34

35 Accumulation Error 35

36 Canceling Orientation 36

37 Canceling Orientation 37

38 Canceling Orientation 38

39 1D Motion 39

40 1D Motion 40

41 1D Motion After Bias Correction 41

42 2D Motion - Diagonal Error in X = 2.8cm (8%) Error in Y = 2.7cm (8%) 42

43 2D Motion L Shape ~15% Error 43

44 Cross-axis Sensitivity 44

45 Cross-axis Sensitivity Caused by 2 factors Inherent microstructure Inaccuracies in fabrication, package orientation and misalignment 45

46 2D Motion Tracking as Dependent 1D Motions 46

47 2D Motion Tracking as Dependent 1D Motions 47

48 2D Motion Tracking as Dependent 1D Motions 48

49 2D Motion Tracking as Dependent 1D Motions 49

50 Applications Source NeuroRehabilitation Research Laboratory Source NeuroRehabilitation Research Laboratory 50

51 Conclusions 1. Accelerometer responds to human arm motions enabling accurate detection of affected arm motions 2. Objective test for tracking rehabilitation progress Minimal user interaction Works with 1D and 2D motions Good candidate for remote deployment 51

52 Conclusions 3. Inertial sensors can be used to develop a selfcontained motion tracking solution to track rehabilitation progress Inertial sensors performance has been improving while their cost is reducing. Thus, offer an economical choice for telemonitoring of health Some error sources currently limit the performance of motion tracking to simplified 2D motions 52

53 Future Work Model Cross-axis sensitivity Develop 3D linear motion tracking Use gyroscope to allow orientation change Do it incrementally, initially 2D and then 3D 53

54 Future Work Stroke Patient Therapist 54

55 Related Publication Poster Saket Doshi, Anura Jayasumana, Crystal Massie, Matthew Malcolm, Tracking Rehabilitation Progress by Quantifying Arm Movement, CSU Research Colloquium on Optimizing Healthy Ageing, Fort Collins, CO. Oct 29-30,

56 Thank You Questions / Comments 56