Metric learning for novel motion rejection in high-density myoelectric pattern recognition

Most traditional myoelectric pattern recognition systems can only identify limited patterns and are prone to be disturbed by unknown motion tasks. This paper is aimed to develop a robust myoelectric control method towards rejecting novel/unknown patterns. In the proposed method, we first convert high-density surface electromyogram (HD-sEMG) signals into a series of feature images. Next, a metric-learning guided convolutional neural network (CNN) is utilized to extract discriminative representations of the images. Compared to separable representations from common CNN, discriminative property characterizes representations in both the compact intra-class variations and separable inter-class differences. Subsequently, we train multiple autoencoders (AEs) to reject representations from any novel pattern that appeared significantly different from target patterns. The performance of the proposed method was evaluated using HD-sEMG signals recorded by two pieces of flexible 6×8 high-density electrode array placed over forearm extensors and flexors of nine subjects during performing seven target motion tasks and six complicated novel motion tasks. The proposed method can identify and reject novel patterns with high accuracy of 94.28%, which is significantly better than a widely adopted traditional method of 77.49% (p < 0.05). This work demonstrated the validity of applying metric learning in alleviating novel motion interference, which is inevitable in myoelectric control. This work will enhance the robustness of myoelectric control systems.