Deakin University
Browse

File(s) under permanent embargo

Upper limb movement profiles during spontaneous motion in acute stroke

Version 2 2024-06-04, 04:22
Version 1 2021-03-25, 08:42
journal contribution
posted on 2024-06-04, 04:22 authored by S Datta, Chandan KarmakarChandan Karmakar, AS Rao, B Yan, M Palaniswami
Abstract Objective. The clinical assessment of upper limb hemiparesis in acute stroke involves repeated manual examination of hand movements during instructed tasks. This process is labour-intensive and prone to human error as well as being strenuous for the patient. Wearable motion sensors can automate the process by measuring characteristics of hand activity. Existing work in this direction either uses multiple sensors or complex instructed movements, or analyzes only the quantity of upper limb motion. These methods are obtrusive and strenuous for acute stroke patients and are also sensitive to noise. In this work, we propose to use only two wrist-worn accelerometer sensors to study the quality of completely spontaneous upper limb motion and investigate correlation with clinical scores for acute stroke care. Approach. The velocity time series estimated from acquired acceleration data during spontaneous motion is decomposed into smaller movement elements. Measures of density, duration and smoothness of these component elements are extracted and their disparity is studied across the two hands. Main results. Spontaneous upper limb motion in acute stroke can be decomposed into movement elements that resemble point-to-point reaching tasks. These elements are smoother and sparser in the normal hand than in the hemiparetic hand, and the amount of smoothness correlates with hemiparetic severity. Features characterizing the disparity of these movement elements between the two hands show statistical significance in differentiating mild-to-moderate and severe hemiparesis. Using data from 67 acute stroke patients, the proposed method can classify the two levels of hemiparetic severity with 85% accuracy. Additionally, compared to activity-based features, the proposed method is robust to the presence of noise in acquired data. Significance. This work demonstrates that the quality of upper limb motion can characterize and identify hemiparesis in stroke survivors. This is clinically significant towards the continuous automated assessment of hemiparesis in acute stroke using minimally intrusive wearable sensors.

History

Journal

Physiological Measurement

Volume

42

Article number

ARTN 045005

Location

England

ISSN

0967-3334

eISSN

1361-6579

Language

English

Publication classification

C1 Refereed article in a scholarly journal

Issue

4

Publisher

IOP Publishing Ltd