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

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 30; pp. 621 - 631
• Main Authors: Song, Xinyu, Van De Ven, Shirdi Shankara, Liu, Lanlan, Wouda, Frank J., Wang, Hong, Shull, Peter B.
• Format: Journal Article
• Language: English
• Published: United States IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activities of Daily Living; ADLs; Algorithms; Arm; Classification algorithms; Classifiers; Computer & video games; Configurations; Educational software; Elbow; Elbow (anatomy); Electromyography; EMG; Estimation; FMG; Games; Gesture recognition; Hand; Hand (anatomy); Humans; IMU; Inertial sensing devices; Motor ability; Movement; Multisensor fusion; natural movement estimation; Qualitative analysis; Rehabilitation; Sensors; serious game; Shoulder; Stroke; Stroke (medical condition); Stroke Rehabilitation - methods; Training; Upper Extremity; Upper limb rehabilitation; Wrist
• DOI: 10.1109/TNSRE.2022.3156387
• ISSN: 1534-4320; 1558-0210

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.