Motion Artifact Suppression for Insulated EMG to Control Myoelectric Prostheses Myoelectric prostheses help amputees to regain independence and a higher quality of life. These prostheses are controlled by electromyography, which measures an electrical signal at the skin surface during muscle contractions. In this contribution, the electromyography is measured with innovative flexible insulated sensors, which separate the skin and the sensor area by a dielectric layer. Electromyography sensors, and biosignal sensors in general, are striving for higher robustness against motion artifacts, which are a major obstacle in realworld environment. The motion artifact suppression algorithms presented in this article, prevent the activation of the prosthesis drive during artifacts, thereby achieving a substantial performance boost. These algorithms classify the signal into muscle contractions and artifacts. Therefore, new time domain features, such as Mean Crossing Rate are introduced and wellestablished time domain features (e.g., ZeroCrossing Rate, Slope Sign Change) are modified and implemented. Various artificial intelligence models, which require low calculation resources for an application in a wearable device, were investigated. These models are neural networks, recurrent neural networks, decision trees and logistic regressions. Although these models are designed for a lowpower realtime embedded system, high accuracies in discriminating artifacts to contractions of up to 99.9% are achieved. The models were implemented and trained for fast response leading to a high performance in realworld environment. For highest accuracies, recurrent neural networks are suggested and for minimum runtime ( 0.99xe2x80x931.15    xcexbc   s), decision trees are preferred.

Recognition of Finger Motions Based on Surface Electromyographic Signals and Artificial Neural Network Surface electromyographic (sEMG) signals can be used in medicine, rehabilitation, robotics and industrial fields. This paper proposes a recognition method of finger motions for dexterous control of prosthetic hands. In this work, we record the corresponding sEMG signals from five subjects, and then set up the model by data preprocessing, feature extraction and classification. The results show that high recognition accuracy can be achieved by using time domain feature extraction and artificial neural network. It means that finger motions can be decoded accurately. To acquire the tradeoff between the number of channels and the recognition accuracy, we apply channel number reduction and it is found that the number of channels is at least 7 with recognition accuracy of 90.52%.