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Gestures classification is a widespread solution for prosthetic hand control using electromyographic signals (EMG). It can be defined as the task of estimating the class associated to a time-series data provided by the sensor. Nevertheless, it remains unsatisfactory as the number of motions (classes) is limited. In this article, we investigate the problem of estimating the joint coordinates of the hand from the same input data. The main contributions of the article are: (1) a new dataset for joint coordinates estimation from EMG data, (2) a set of neural networks models for this regression task, and (3) an experimental study to compare the performances of the proposed methods. The database is built with a six channels EMG armband and hand motions measured by computer vision. First results are hopeful and provide both a baseline for new methods and a proof of concept for future prosthetic control solutions.

A New Dataset and Neural Networks Models to Estimate the Joint Coordinates of Human Hand from Electromyographic Signals