Abstract
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.
Highlights
The number of disabled persons is increasing every year due to accidents such as work-related injuries, diseases, traffic accidents, and natural disasters [1]
Lower limb prosthesis is a complex human–machine co-drive system, which requires that the binding force at the human–machine contact position should not be too strong
The EMG signals are recorded by 33 EMG electrodes, the entropy of each channel is calculated, the mutual information between any two channels is analyzed, and the weighted adjacency matrix of the muscle function network is obtained
Summary
The number of disabled persons is increasing every year due to accidents such as work-related injuries, diseases, traffic accidents, and natural disasters [1]. EMG signal acquisition from fifty‐seven five of themeasurement upper limbs through duringchannel specific movements, and the to optimal location was the selected from the subset with the motion recognition accuracy the minimum feature extraction and classification of highest large amounts of data. The location muscle group can act independently and be interconnected to form a complex network selection method of EMG collection based on anatomy is not suitable for residual limb. It is necessary to select the position of EMG signal acquisition of residual the theory of complex networks [26,27,28,29] has developed rapidly. Its local and global fore, the complex characteristics network theory can be applied analyze between the residual limb muscle can clearly describe the to relationship different elements and the groups.
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