120 TEMPLATES IN TOTAL - IEEE Bilkentieee.bilkent.edu.tr/grc2017/docs/sample_pdf.pdf · Exp e rim e...

E x p e r i m e n t a l R e s u l t s

TE

MP

LA

TE

S

Aras Yurtman and Billur BarshanDepartment of Electrical and Electronics Engineering, Bilkent University

yurtman@ee.bilkent.edu.tr, billur@ee.bilkent.edu.tr

● Physical therapy often requires repeating certain exercise movements.

● Patients first perform the required exercises under supervision in a hospital or rehabilitation center. PARTIAL AND SUBJECTIVE FEEDBACK

● Most patients continue their exercises at home. NO FEEDBACK

● The intensity of a physical therapy session is estimated by the number of correct executions.

OBJECTIVE : to detect and evaluate all exercise executions in a physical therapy session by using wearable motion sensors based on template recordings

I n t r o d u c t i o n

A l g o r i t h m

● 5 wearable motion sensors, each containing a tri-axial accelerometer, gyroscope, and magnetometer

● 8 exercise types performed by 5 subjects

● Each exercise is assumed to have 3 execution types: one correct and two erroneous (fast and low-amplitude execution)

● one template for each execution type of each exercise of each subject 120 TEMPLATES IN TOTAL

● To simulate a physical therapy session, for each exercise, each subject performs the exercise 10 times in the correct way, then 10 times with type-1 error, and finally 10 times with type-2 error. Between these 3 blocks, the subject is idle. 1,200 TEST EXECUTIONS IN TOTAL

D a t a s e t

● Standard DTW

● measures the similarity between two signals that are different in time or speed

● matches two signals by transforming their time axes nonlinearly to maximize the similarity

● Multi-Template Multi-Match DTW (MTMM-DTW) has been developed based on DTW to

● detect multiple occurrences of multiple template signalsin a long test signal

● both detect and classify the occurrences

Features of MTMM-DTW:

● The number of templates, occurrences, their positions, and lengths of the template and test signals may be arbitrary.

● The signals may be multi-D.

● A threshold factor can be selected to prevent relatively short matches compared to the matching template.

● The amount of overlap between the matched subsequences can be adjusted.

● Any modification to the DTW algorithm may be used in MTMM-DTW.

EXECUTION TYPES:

● correct

● type-1 error (executed too fast)

● type-2 error (executed in low amplitude)

5subjects

8exercises

3execution

types10

repetitions

1 of 3executions selected✕ ✕ ✕

=

=

120templates

1,200executions

EXPERIMENTS SIMULATING A PHYSICAL THERAPY SESSION

For each exercise, each subject has simulated a therapy session by executing the exercise

● 10 times in the correct way,

● waiting idly,

● 10 times with type-1 error,

● waiting idly, and then

● 10 times with type-2 error.

0 50 100 150 200-20

0

20subject 3, exercise 1, unit 2

time (s)

accele

ration (

m/s

2)

x

y

z

0 50 100 150 200-5

0

5

time (s)

angula

r ra

te (

rad/s

)

x

y

z

0 50 100 150 200-1

0

1

time (s)magnetic f

ield

(re

lative)

x

y

z

0 10 20 30 40 50-20

0

20subject 3, exercise 1 (templates), unit 2

time (s)

accele

ration (

m/s

2)

x

y

z

0 10 20 30 40 50-5

0

5

time (s)

angula

r ra

te (

rad/s

)

x

y

z

0 10 20 30 40 50-1

0

1

time (s)magnetic f

ield

(re

lative)

x

y

z

1 2

3

4

5

LE

G E

XE

RC

ISE

S

6 7

8

AR

M E

XE

RC

ISE

S

TEST

XSENS MTx

SENSOR UNIT

Number of total executions 1,200

Number of executions detected 1,125

Accuracy of exercise classification 93.5%

Accuracy of exercise and execution type classification 88.7%

Misdetection rate (MDs / positives) 8.6%

False alarm rate (FAs / negatives) 4.9%

Sensitivity 91.4%

Specificity 95.1%

1000 2000 3000 4000 50000

5

10

15

samples

dist

ance

Experiment 1 of subject 5

1000 2000 3000 4000 50000

5

10

15

samples

dist

ance

Experiment 2 of subject 5

1000 2000 3000 4000 50000

5

10

15

samples

dist

ance

Experiment 3 of subject 5

1000 2000 3000 4000 50000

5

10

15

samples

dist

ance

Experiment 4 of subject 5

1000 2000 3000 4000 50000

5

10

15

samples

dist

ance

Experiment 5 of subject 5

1000 2000 3000 4000 50000

5

10

15

samples

dist

ance

Experiment 6 of subject 5

1000 2000 3000 4000 50000

5

10

15

samples

dist

ance

Experiment 7 of subject 5

1000 2000 3000 4000 50000

5

10

15

samples

dist

ance

Experiment 8 of subject 5

● We apply the proposed MTMM-DTW algorithm to each test signal with the 24 template signals of the same subjectfor 8 exercise types × 3 execution types.

● Each detected exercise must be at least half the length of the matching template.

● Detections with a normalized DTW distance larger than 10 are omitted.

C o n c l u s i o n

● The proposed system can be used in tele-rehabilitation to provide feedback to the patient exercising remotely and assessing the effectiveness of the exercising session.

● In previous systems, each execution is recorded separately or cropped manually.

● Our system

● automatically detects the individual executions and idle time periods,

● classifies each execution as one of the exercise types,

● evaluates its correctness, and

● identifies the error type if any.

R e f e r e n c e s

[1] A. Yurtman and B. Barshan, “Automated evaluation of physical therapy exercises using multi-template dynamic time warping on wearable sensor signals,” Comp. Meth. and Prog. Biomed., 117(2):189–207, Nov. 2014.

[2] P. Tormene, T. Giorgino, S. Quaglini, and M. Stefanelli, “Matching incomplete time series with dynamic time warping: an algorithm and an application to post-stroke rehabilitation,” Artif. Intell. Med., 45(1):11–34, Jan. 2009.