Abstract
During the application of surface electromyograms (EMGs) in human-machine interfaces, direct estimates of multiple hand motion speeds are required to facilitate operations that fully reflect the user's intentions. However, no practical methods are available for this purpose because conventional function approximators cannot learn the complex relationships between the motion speeds and surface EMGs within a practical period of time. By contrast, it has been shown that a selective desensitization neural network (SDNN) can learn complex input-output relationships with low computational costs. In this study, we propose a method for the direct estimation of hand motion speeds from surface EMGs using a SDNN. We estimated the motion speed in practice to assess the efficacy of this proposed method. Our experimental results show that the proposed method can estimate the approximate speeds of six basic motions in real time.
