A robot-based hybrid lower limb system for Assist-As-Needed rehabilitation of stroke patients: Technical evaluation and clinical feasibility

康复 功能性电刺激 物理医学与康复 肌电图 冲程(发动机) 计算机科学 机器人 医学 模拟 物理疗法 人工智能 刺激 工程类 机械工程 内科学
作者
Kasper Leerskov,Cindy Rikhof,Erika G. Spaich,Strahinja Došen,Gerdienke B. Prange-Lasonder,Erik C. Prinsen,Johan S. Rietman,Lotte N. S. Andreasen Struijk
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:179: 108839-108839
标识
DOI:10.1016/j.compbiomed.2024.108839
摘要

Although early rehabilitation is important following a stroke, severely affected patients have limited options for intensive rehabilitation as they are often bedridden. To create a system for early rehabilitation of lower extremities in these patients, we combined the robotic manipulator ROBERT® with electromyography (EMG)-triggered functional electrical stimulation (FES) and developed a novel user-driven Assist-As-Needed (AAN) control. The method is based on a state machine able to detect user movement capability, assessed by the presence of an EMG-trigger and the movement velocity, and provide different levels of assistance as required by the patient (no support, FES only, and simultaneous FES and mechanical assistance). To technically validate the system, we tested 10 able-bodied participants who were instructed to perform specific behaviors to test the system states while conducting knee extension and ankle dorsal flexion exercises. The system was also tested on two stroke patients to establish its clinical feasibility. The technical validation showed that the state machine correctly detected the participants' behavior and activated the target AAN state in more than 96% of the exercise repetitions. The clinical feasibility test showed that the system successfully recognized the patients' movement capacity and activated assistive states according to their needs providing the minimal level of support required to exercise successfully. The system was technically validated and preliminarily proved clinically feasible. The present study shows that the novel system can be used to deliver exercises with a high number of repetitions while engaging the participants' residual capabilities through the AAN strategy.
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