Activities of Daily Living-Based Rehabilitation System for Arm and Hand Motor Function Retraining After Stroke.

Xinyu Song,Lanlan Liu,Shirdi Shankara Van De Ven,Hong Wang,Peter B Shull,Frank J Wouda

doi:10.1109/tnsre.2022.3156387

Abstract

Most stroke survivors have difficulties completing activities of daily living (ADLs) independently. However, few rehabilitation systems have focused on ADLs-related training for gross and fine motor function together. We propose an ADLs-based serious game rehabilitation system for the training of motor function and coordination of both arm and hand movement where the user performs corresponding ADLs movements to interact with the target in the serious game. A multi-sensor fusion model based on electromyographic (EMG), force myographic (FMG), and inertial sensing was developed to estimate users' natural upper limb movement. Eight healthy subjects and three stroke patients were recruited in an experiment to validate the system's effectiveness. The performance of different sensor and classifier configurations on hand gesture classification against the arm position variations were analyzed, and qualitative patient questionnaires were conducted. Results showed that elbow extension/flexion has a more significant negative influence on EMG-based, FMG-based, and EMG+FMG-based hand gesture recognition than shoulder abduction/adduction does. In addition, there was no significant difference in the negative influence of shoulder abduction/adduction and shoulder flexion/extension on hand gesture recognition. However, there was a significant interaction between sensor configurations and algorithm configurations in both offline and real-time recognition accuracy. The EMG+FMG-combined multi-position classifier model had the best performance against arm position change. In addition, all the stroke patients reported their ADLs-related ability could be restored by using the system. These results demonstrate that the multi-sensor fusion model could estimate hand gestures and gross movement accurately, and the proposed training system has the potential to improve patients' ability to perform ADLs.

Highlights

STROKE is a leading cause of death and long-term disability [1]
The study found that EMG+force myography (FMG)-multiple-position classifier (MPC) is the optimal sensor and algorithm configuration to reduce the negative impact of arm position changes on hand gesture recognition
Our findings demonstrate that arm position can influence the accuracy of EMG-based hand gesture classification, which aligns with previous findings

Summary

Introduction

STROKE is a leading cause of death and long-term disability [1]. Seventy-five percent of stroke survivors suffer from upper limb dysfunction, which limits their performance in daily life [2]. Effective rehabilitation should be long-term, repetitive, and intensive for stroke patients’ neurological restoration [3], [4], [5]. Patients in acute and subacute stages stay in the hospital, receiving conventional rehabilitation such as occupational therapies (OT) with the assistance of therapists, which is labor-intensive and consumes medical resources. Patients in the chronic stage should continue effective upper limb motor function training in accordance with doctors’ advice even after being discharged from the hospital. Patients may rely on the usage of the unaffected side to complete activities of daily living (ADLs) due to the dysfunction of the affected side in daily life [7], which could cause the gradual decline in motor function capacity of the affected side [8]. Sixty-five percent of patients in the chronic stage cannot integrate their affected side into their ADLs [9]

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Results

Conclusion