Research of the System Using Finger-worn Sensors and Data for Stroke Survivors

1. Towards the Design of a Ring Sensor-based mHealth System
to Achieve Optimal Motor Function in Stroke Survivors
YOOJUNG KIM, Seoul National University, Republic of Korea
HEE-TAE JUNG, University of Massachusetts Amherst, United States
JOONWOO PARK, Smilegreen Child Development Center, Republic of Korea
YANGSOO KIM, Heeyeon Rehabilitation Hospital, Republic of Korea
NATHAN RAMASARMA, ArcSecond Inc., United States
PAOLO BONATO, Harvard Medical School, Spaulding Rehabilitation Hospital, United States
EUN KYOUNG CHOE , University of Maryland, College Park, United States
SUNGHOON IVAN LEE , University of Massachusetts Amherst, United States

2. Number of stroke survivors in the U.S.?

3. 800K
It’s more than the population of Seattle
Stroke survivors

4. 800K 750K
It’s more than the population of Seattle
Seattle population
Stroke survivors

5. the less-a
ff
ected limb the a
ff
ected limb
hemiparesis

6. Clinic Home
7
14
Outpatient rehab therapy is necessary
7
7 7
21

7. Functional improvement does not last
unless they keep using their a
ff
ected limb
Clinic
Functional
Improvement

8. Functional improvement does not last
unless they keep using their a
ff
ected limb
Clinic
Functional
Improvement
Home
Performance
in daily living

9. Functional improvement does not last
unless they keep using their a
ff
ected limb
Home
Clinic
Functional
Improvement
Performance
in daily living
learned
non-use
forget to
use
pain

10. How to solve this problem
How?
?
Functional
Improvement
Performance
in daily living

11. Ring sensors
Three-axis Accelerometer
Ultra-Low Power
32-bit System-on-Chip
Local Memory (SD Card)
And 170 mAh Battery

12. Qunatitative
Methods
Qualitative
Methods
Clinical e
ffi
cacy of
fi
nger-worn sensors
Design requirements for
the self-monitoring system

13. Qunatitative
Methods
Clinical e
ffi
cacy of
fi
nger-worn sensors
Finger-worn &
Wrist-worn sensors
25 Stroke survivors
6 Motor tasks

14. Our metric shows a strong agreement
with the existing clinical measure!

15. Finger-wornsensorscancapturemore
clinicallyrelevantinfo.thanwrist-wornsensors!
Sensor Location
Maximum Correlation
MAL-AoU MAL-QoM FMA FAS
Finger 0.83** 0.75** 0.72** 0.80**
Wrist 0.76** 0.73** 0.70** 0.69**
* p < 0.05, ** p < 0.01.

16. Qualitative
Methods
Design requirements for
the self-monitoring system
Semi-structured
Interviews
25 Stroke survivors
7 Occupational
Therapists

17. Goal Setting and Achievement
Steps to Achieve Goals
Reminders and Cues
Involving Formal & Informal Caregivers
Many things to consider

18. mHealth System that encourage the a
ff
ected limb use
with data-driven and patient-centered feedback
Clinical e
ffi
cacy of
fi
nger-worn sensors
Design requirements
for the mHealth system

19. Thank You!
Any questions?