Terrain Identification for Prosthetic Knees Based on Electromyographic Signal Features

The features of electromyographic （EMG） signals were investigated while people walking on different terrains, including up and down slopes, up and down stairs, and during level walking at different speeds, The features were used to develop a terrain identification method. The technology can be used to develop an intelligent transfemoral prosthetic limb with terrain identification capability, The EMG signals from 8 hip muscles of 13 healthy persons were recorded as they walked on the different terrains. The signals from the sound side of a transfemoral amputee were also recorded. The features of these signals were obtained using data processing techniques with an identification process developed for the identification of the terrain type. The procedure was simplified by using only the signals from three muscles. The identification process worked well in an intelligent prosthetic knee in a laboratory setting.

A Hybrid Model Based on ResNet and GCN for sEMG-Based Gesture Recognition

The surface electromyography(sEMG)is one of the basic processing techniques to the gesture recognition because of its inherent advantages of easy collection and non-invasion.However,limited by feature extraction and classifier selection,the adaptability and accuracy of the conventional machine learning still need to promote with the increase of the input dimension and the number of output classifications.Moreover,due to the different characteristics of sEMG data and image data,the conventional convolutional neural network(CNN)have yet to fit sEMG signals.In this paper,a novel hybrid model combining CNN with the graph convolutional network(GCN)was constructed to improve the performance of the gesture recognition.Based on the characteristics of sEMG signal,GCN was introduced into the model through a joint voting network to extract the muscle synergy feature of the sEMG signal.Such strategy optimizes the structure and convolution kernel parameters of the residual network(ResNet)with the classification accuracy on the NinaPro DBl up to 90.07%.The experimental results and comparisons confirm the superiority of the proposed hybrid model for gesture recognition from the sEMG signals.

Microelectronic neural bridging of toad nerves to restore leg function

The present study used a microelectronic neural bridge comprised of electrode arrays for neural signal detection, functional electrical stimulation, and a microelectronic circuit including signal amplifying, processing, and functional electrical stimulation to bridge two separate nerves, and to restore the lost function of one nerve. The left leg of one spinal toad was subjected to external mechanical stimulation and functional electrical stimulation driving. The function of the left leg of one spinal toad was regenerated to the corresponding leg of another spinal toad using a microelectronic neural bridge. Oscilloscope tracings showed that the electromyographic signals from controlled spinal toads were generated by neural signals that controlled the spinal toad, and there was a delay between signals. This study demonstrates that microelectronic neural bridging can be used to restore neural function between different injured nerves.