Improving Multi-Position Training Performance on Reducing Limb Condition Effect in Wrist Myoelectric Control

Abstract

Compared to the forearm, the wrist is suitable for the combination of myoelectric control with the popular wearable devices, enabling human machine interaction in an intuitive and effortless way. The change of limb condition is a common disturbance degrading the performance of wrist myoelectric control in practical applications. Though multi-position training is a simple and effective strategy of mitigating the influence, with the addition of training data, the performance of the traditional method could be plateaued before reaching the perfect. This study proposed a multi-scale one-dimensional convolutional neural network (MSCNN) with the end-to-end learning to improve the data generalization from different limb conditions. The results showed that the proposed method outperformed the traditional method by from 7.2% with single limb condition training to 9.5% with seven limb condition training, where the classification accuracy of the proposed method, i.e., 97.1%, was close to the perfect. The difference was from the performance on the data from the training conditions, which was maintained by MSCNN, but dropped by the traditional method with the addition of the training data. This work improved the robustness of group strategy against limb condition effect. The results could facilitate the development of the wrist-based wearable devices, as well as the applications of the myoelectric control-based human machine interface into more areas.