Physiol Meas. 2021 Mar 18. doi: 10.1088/1361-6579/abf01e. Online ahead of print.
ABSTRACT
OBJECTIVE: 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 errors while also being strenuous for the patient. Wearable motion sensors can automate the process by measuring characteristics of hand activity. Existing work in this direction either use multiple sensors or complex instructed movements, or analyze only the quantity of upper limb motion. These methods are obtrusive and strenuous for acute stroke patients and 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: 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 the hemiparetic hand, and the amount of smoothness correlates with hemiparetic severity. Features characterizing the disparity of these movement elements between 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 continuous automated assessment of hemiparesis in acute stroke using minimally intrusive wearable sensors.
PMID:33735840 | DOI:10.1088/1361-6579/abf01e
