Wang Qiang, Chen Chunjie, Mu Xinxing, Wang Haibin, Wang Zhuo, Xu Sheng, Guo Weilun, Wu Xinyu, Li Weimin
Shandong Zhongke Advanced Technology Co., Ltd., Jinan 250100, China.
Guangdong Provincial Key Lab of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Biomimetics (Basel). 2024 Feb 21;9(3):129. doi: 10.3390/biomimetics9030129.
Heavy lifting operations frequently lead to upper limb muscle fatigue and injury. In order to reduce muscle fatigue, auxiliary force for upper limbs can be provided. This paper presents the development and evaluation of a wearable upper limb exoskeleton (ULE) robot system. A flexible cable transmits auxiliary torque and is connected to the upper limb by bypassing the shoulder. Based on the K-nearest neighbors (KNN) algorithm and integrated fuzzy PID control strategy, the ULE identifies the handling posture and provides accurate active auxiliary force automatically. Overall, it has the quality of being light and easy to wear. In unassisted mode, the wearer's upper limbs minimally affect the range of movement. The KNN algorithm uses multi-dimensional motion information collected by the sensor, and the test accuracy is 94.59%. Brachioradialis muscle (BM), triceps brachii (TB), and biceps brachii (BB) electromyogram (EMG) signals were evaluated by 5 kg, 10 kg, and 15 kg weight conditions for five subjects, respectively, during lifting, holding, and squatting. Compared with the ULE without assistance and with assistance, the average peak values of EMG signals of BM, TB, and BB were reduced by 19-30% during the whole handling process, which verified that the developed ULE could provide practical assistance under different load conditions.
繁重的举重作业经常会导致上肢肌肉疲劳和损伤。为了减轻肌肉疲劳，可以为上肢提供辅助力。本文介绍了一种可穿戴上肢外骨骼（ULE）机器人系统的开发与评估。一根柔性电缆传递辅助扭矩，并通过绕过肩部与上肢相连。基于K近邻（KNN）算法和集成模糊PID控制策略，ULE能够识别搬运姿势并自动提供精确的主动辅助力。总体而言，它具有轻便易穿戴的特点。在无辅助模式下，穿戴者的上肢对运动范围的影响最小。KNN算法使用传感器收集的多维运动信息，测试准确率为94.59%。分别在5kg、10kg和15kg重量条件下，对五名受试者在举重、持重和下蹲过程中的肱桡肌（BM）、肱三头肌（TB）和肱二头肌（BB）肌电图（EMG）信号进行了评估。与无辅助和有辅助的ULE相比，在整个搬运过程中，BM、TB和BB的EMG信号平均峰值降低了19%-30%，这验证了所开发的ULE在不同负载条件下能够提供实际的辅助作用。
