DFNNbased Gesture Recognition with the Shift and Damage of the HDsEMG Electrodes The surface electromyography (sEMG) signals can be used to identify the activity intention. Recently, Highdensity (HD) electrodes have been widely used to record sEMG signals for myoelectric control. However, the problem of the electrodes shift or damage in dons and doff often happens, it will have a greatly bad influence on the accuracy and stability of gesture recognition. In this paper, we propose a deep fast neural network (DFNN) to attenuate these issues by utilizing the spatialtemporal features of sEMG signals. The performance of the proposed model is compared with the locality sensitivity analysis (LSDA) and long shortterm memory (LSTM), and they are all tested to classify nine gestures in these situations including the electrodes shift (10mm), shift (10mm) and damage (6). Without pretraining, the proposed DFNN model can high average 93.35%, and the error between individuals is less than 0.045 when the electrodes shift and damage.

Deep Neural Network Approach for Hand, Wrist, Grasping and Functional Movements Classification using Lowcost sEMG Sensors This paper presents a Deep Neural Network approach for the classification of 41 hand, wrist, grasping and functional movements based on a public dataset of lowcost sEMG sensors called Ninapro DB5. The recent advancements in sensor technology, mechatronics, signal processing techniques and edge computing hardware equipped with GPU make dexterous prosthetic hands with noninvasive sEMG sensors and control capabilities of machine learning possible. However, its high cost means the technology is not accessible to most people. Therefore, the objective of this paper is to investigate the control and capabilities of a lowcost setup of sEMG sensors for a prosthetic hand. The acquisition setup includes two Thalmic Myo armbands for the total of 16 channels with the sampling rate of 200 Hz. Our approach achieved an overall accuracy of 91% with a macro recall of 77% for the classification of 41 movements, outperformed other algorithms such as SVM, Random forest, and XGBoost. These results suggest that a development of practical prosthetic hand could be possible with lowcost sEMG sensors.