The Neurorehabilitation Robotics and Engineering Group, Center for Rehabilitation Robotics, Department of Health Science and Technology, Aalborg University, Gistrup, 9260, Denmark.
Roessingh Research and Development, Enschede, 7522AH, The Netherlands; Faculty of Engineering Technology, Department of Biomechanical Engineering, University of Twente, Enschede, 7500AE, The Netherlands.
Comput Biol Med. 2024 Sep;179:108839. doi: 10.1016/j.compbiomed.2024.108839. Epub 2024 Jul 12.
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.
尽管中风后早期康复很重要,但严重受损的患者由于经常卧床不起,因此选择强化康复治疗的机会有限。为了为这些患者创造一种早期下肢康复系统,我们将机器人操纵器 ROBERT®与肌电图(EMG)触发功能性电刺激(FES)相结合,并开发了一种新的用户驱动按需辅助(AAN)控制。该方法基于一个状态机,能够通过存在 EMG 触发和运动速度来检测用户的运动能力,并根据患者的需求提供不同水平的辅助(无支撑、仅 FES 和同时 FES 和机械辅助)。
为了对系统进行技术验证,我们测试了 10 名健康参与者,他们被指示执行特定的行为,以测试系统状态,同时进行膝关节伸展和踝关节背屈运动。该系统还在两名中风患者身上进行了测试,以确定其临床可行性。
技术验证表明,状态机正确检测到参与者的行为,并在超过 96%的运动重复中激活了目标 AAN 状态。临床可行性测试表明,该系统成功识别了患者的运动能力,并根据其需求激活辅助状态,提供成功锻炼所需的最低水平的支持。
该系统经过技术验证,并初步证明了其临床可行性。本研究表明,该新型系统可以通过 AAN 策略进行多次重复运动,同时利用参与者的剩余能力。