Journal
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume 28, Issue 3, Pages 601-611Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2020.2966950
Keywords
Stroke; wearable sensors; Fugl-Meyer assessment; upper-limb impairment; remote monitoring
Categories
Funding
- NSF Smart and Connected Health Program [1755687]
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1755687] Funding Source: National Science Foundation
Ask authors/readers for more resources
Upper-limb paresis is the most common motor impairment post stroke. Current solutions to automate the assessment of upper-limb impairment impose a number of critical burdens on patients and their caregivers that preclude frequent assessment. In this work, we propose an approach to estimate upper-limb impairment in stroke survivors using two wearable inertial sensors, on the wrist and the sternum, and a minimally-burdensome motor task. Twenty-three stroke survivors with no, mild, or moderate upper-limb impairment performed two repetitions of one-to-two minute-long continuous, random (i.e., patternless), voluntary upper-limb movements spanning the entire range of motion. The three-dimensional time-series of upper-limb movements were segmented into a series of one-dimensional submovements by employing a unique movement decomposition technique. An unsupervised clustering algorithm and a supervised regression model were used to estimate Fugl-Meyer Assessment (FMA) scores based on features extracted from these submovements. Our regression model estimated FMA scores with a normalized root mean square error of 18.2% (r(2)=0.70) and needed as little as one minute of movement data to yield reasonable estimation performance. These results support the possibility of frequently monitoring stroke survivors' rehabilitation outcomes, ultimately enabling the development of individually-tailored rehabilitation programs.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available