Wearable-Based Kinematic Analysis of Upper-Limb Movements During Daily Activities Could Provide Insights into Stroke Survivors’ Motor Ability

Author:

Lee Sunghoon Ivan1ORCID,Liu Yunda1,Vergara-Díaz Gloria2,Pugliese Benito Lorenzo23,Black-Schaffer Randie2,Stoykov Mary Ellen45ORCID,Bonato Paolo2

Affiliation:

1. College of Information and Computer Sciences, University of Massachusetts Amherst, Amherst, MA, USA

2. Department of Physical Medicine and Rehabilitation, Harvard Medical School at Spaulding Rehabilitation Hospital, Boston, MA, USA

3. Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy

4. Arm & Hands Lab, Shirley Ryan AbilityLab, Chicago, IL, USA

5. Department of Physical Medicine and Rehabilitation, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA

Abstract

Background Frequent and objective monitoring of motor recovery progression holds significant importance in stroke rehabilitation. Despite extensive studies on wearable solutions in this context, the focus has been predominantly on evaluating limb activity. This study aims to address this limitation by delving into a novel measure of wrist kinematics more intricately related to patients’ motor capacity. Objective To explore a new wearable-based approach for objectively and reliably assessing upper-limb motor ability in stroke survivors using a single inertial sensor placed on the stroke-affected wrist. Methods Seventeen stroke survivors performed a series of daily activities within a simulated home setting while wearing a six-axis inertial measurement unit on the wrist affected by stroke. Inertial data during point-to-point upper-limb movements were decomposed into movement segments, from which various kinematic variables were derived. A data-driven approach was then employed to identify a kinematic variable demonstrating robust internal reliability, construct validity, and convergent validity. Results We have identified a key kinematic variable, namely the 90th percentile of movement segment distance during point-to-point movements. This variable exhibited robust reliability (intra-class correlation coefficient of .93) and strong correlations with established clinical measures of motor capacity (Pearson’s correlation coefficients of .81 with the Fugl-Meyer Assessment for Upper-Extremity; .77 with the Functional Ability component of the Wolf Motor Function Test; and −.68 with the Performance Time component of the Wolf Motor Function Test). Conclusions The findings underscore the potential for continuous, objective, and convenient monitoring of stroke survivors’ motor progression throughout rehabilitation.

Funder

National Institute of Biomedical Imaging and Bioengineering

Publisher

SAGE Publications

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