Hand Motion Estimation using Super-Resolution of Multipoint Surface Electromyogram by Deep Learning

This paper proposes a method for hand motion estimation using super-resolution of multipoint surface electromyogram for prosthetic hands. In general, obtaining more EMGs (ElectroMyoGraphy) improves the accuracy of hand motion estimation, but it is costly and hard to use. Therefore, this method improves the accuracy of hand motion estimation by estimating a large number of EMG signals from a small number of EMG signals using super-resolution. This super-resolution is achieved by learning the relationship between few and many myoelectric signals using a deep neural network. Then, hand motions are estimated from the high-resolution signal using a deep neural network. Experiments using actual EMG signals show that the proposed method improves the accuracy of hand motion estimation.