BP Neural Network Based Onboard Training for Realtime Locomotion Mode Recognition in Robotic Transtibial Prostheses Locomotion mode recognition based on the offline trained model brings difficulties in integration and application to wearable robots. In this paper, we put forward an onboard training based on back propagation (BP) neural network and developed the realtime locomotion mode recognition research in robotic transtibial prosthesis. Three transtibial amputees participated in the study to finish the designed six experimental tasks (standing, level ground walking, stair ascending and descending, ramp ascending and descending) with robotic transtibial prostheses. Data of six locomotion modes were collected under normal speed condition as training data set to train model on board. Based on the onboard trained models, realtime recognition experiments were developed under three different speeds conditions. The total recognition accuracies were 91.54%, 96.72% and 95.35% corresponding to slow, normal and fast speeds, respectively. The results showed some adaptation of recognition for the six locomotion modes at different speeds. The onboard training strategy was feasible and effective with satisfactory performance.

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.