IEEE Trans Neural Syst Rehabil Eng. 2022;30:1298-1309. doi: 10.1109/TNSRE.2022.3172974. Epub 2022 May 27.
Motor disorder of upper limbs has seriously affected the daily life of the patients with hemiplegia after stroke. We developed a wearable supernumerary robotic limb (SRL) system using a hybrid control approach based on motor imagery (MI) and object detection for upper-limb motion assistance. SRL system included an SRL hardware subsystem and a hybrid control software subsystem. The system obtained the patient's motion intention through MI electroencephalogram (EEG) recognition method based on graph convolutional network (GCN) and gated recurrent unit network (GRU) to control the left and right movements of SRL, and the object detection technology was used together for a quick grasp of target objects to compensate for the disadvantages when using MI EEG alone like fewer control instructions and lower control efficiency. Offline training experiment was designed to obtain subjects' MI recognition models and evaluate the feasibility of the MI EEG recognition method; online control experiment was designed to verify the effectiveness of our wearable SRL system. The results showed that the proposed MI EEG recognition method (GCN+GRU) could effectively improve the MI classification accuracy (90.04% ± 2.36 %) compared with traditional methods; all subjects were able to complete the target object grasping tasks within 23 seconds by controlling the SRL, and the highest average grasping success rate achieved 90.67% in bag grasping task. The SRL system can effectively assist people with upper-limb motor disorder to perform upper-limb tasks in daily life by natural human-robot interaction, and improve their ability of self-help and enhance their confidence of life.
上肢运动障碍严重影响脑卒中后偏瘫患者的日常生活。我们开发了一种基于运动想象（MI）和目标检测的混合控制方法的可穿戴式附加机器人肢体（SRL）系统，用于辅助上肢运动。SRL 系统包括 SRL 硬件子系统和混合控制软件子系统。该系统通过基于图卷积网络（GCN）和门控循环单元网络（GRU）的 MI 脑电图（EEG）识别方法获得患者的运动意图，以控制 SRL 的左右运动，并且一起使用目标检测技术快速抓取目标物体，以弥补单独使用 MI EEG 时控制指令较少和控制效率较低的缺点。设计了离线训练实验以获得受试者的 MI 识别模型，并评估 MI EEG 识别方法的可行性；设计了在线控制实验以验证我们可穿戴 SRL 系统的有效性。结果表明，与传统方法相比，所提出的 MI EEG 识别方法（GCN+GRU）可以有效地提高 MI 分类准确性（90.04%±2.36%）；所有受试者都能够通过控制 SRL 在 23 秒内完成目标物体抓取任务，在袋子抓取任务中最高平均抓取成功率达到 90.67%。SRL 系统可以通过自然的人机交互有效地帮助上肢运动障碍患者完成日常生活中的上肢任务，提高他们的自理能力，增强他们的生活信心。
