School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300131, China.
Engineering Research Center of Intelligent Rehabilitation Device and Detection Technology, Ministry of Education, Tianjin 300131, China.
Sensors (Basel). 2021 Feb 6;21(4):1147. doi: 10.3390/s21041147.
The intelligent prosthesis driven by electromyography (EMG) signal provides a solution for the movement of the disabled. The proper position of EMG sensors can improve the prosthesis's motion recognition ability. To exert the amputee's action-oriented ability and the prosthesis' control ability, the EMG spatial distribution and internal connection of the prosthetic wearer is analyzed in three kinds of movement conditions: appropriate angle, excessive angle, and angle too small. Firstly, the correlation characteristics between the EMG channels are analyzed by mutual information to construct a muscle functional network. Secondly, the network's features of different movement conditions are analyzed by calculating the characteristic of nodes and evaluating the importance of nodes. Finally, the convergent cross-mapping method is applied to construct a directed network, and the critical muscle groups which can reflect the user's movement intention are determined. Experiment shows that this method can accurately determine the EMG location and simplify the distribution of EMG sensors inside the prosthetic socket. The network characteristics of key muscle groups can distinguish different movements effectively and provide a new strategy for decoding the relationship between limb nerve control and body movement.
基于肌电信号的智能假肢为残疾人的运动提供了一种解决方案。肌电传感器的适当位置可以提高假肢的运动识别能力。为了发挥截肢者的动作导向能力和假肢的控制能力,对三种运动状态(适当角度、过大角度和角度过小)下的假肢穿戴者的肌电空间分布和内部连接进行了分析。首先,通过互信息分析肌电通道之间的相关特征,构建肌肉功能网络。其次,通过计算节点特征和评估节点重要性来分析不同运动状态下网络的特征。最后,应用收敛互映射方法构建有向网络,确定能够反映用户运动意图的关键肌肉群。实验表明,该方法可以准确确定肌电位置,并简化假肢插座内肌电传感器的分布。关键肌肉群的网络特征可以有效地区分不同的运动,并为解码肢体神经控制与身体运动之间的关系提供了一种新策略。