Natural control capabilities of robotic hands by hand amputated subjects.

Abstract

People with transradial hand amputations who own a myoelectric prosthesis currently have some control capabilities via sEMG. However, the control systems are still limited and not natural. The Ninapro project is aiming at helping the scientific community to overcome these limits through the creation of publicly available electromyography data sources to develop and test machine learning algorithms. In this paper we describe the movement classification results gained from three subjects with an homogeneous level of amputation, and we compare them with the results of 40 intact subjects. The number of considered subjects can seem small at first sight, but it is not considering the literature of the field (which has to face the difficulty of recruiting trans-radial hand amputated subjects). The classification is performed with four different classifiers and the obtained balanced classification rates are up to 58.6% on 50 movements, which is an excellent result compared to the current literature. Successively, for each subject we find a subset of up to 9 highly independent movements, (defined as movements that can be distinguished with more than 90% accuracy), which is a deeply innovative step in literature. The natural control of a robotic hand in so many movements could lead to an immediate progress in robotic hand prosthetics and it could deeply change the quality of life of amputated subjects.