Towards Improving Myocontrol of Prosthetic Hands: A Study on Automated Instability Detection

Myocontrol is the control of an assistive device via the interpretation of the subject's intent using surface electromyography, and one paradigmatic instance of myocontrol is in upper-limb prosthetics applications. The reliability of this kind of control remains a key issue - effective and stable upper-limb myocontrol is one of the most interesting open problems in the field of human-robot interfaces and rehabilitation. In this work we focused on the myocontrol of a prosthetic hand while grasping: performing grasp actions only when, and exactly for the duration, the user desires, avoiding failures that can lead to frustrating or catastrophic results. One specific step to improve stability in the myocontrol of prosthetic hands is the possibility to automatically detect the occurrence of a failure. For this purpose, the availability of an automatic “oracle” able to accomplish this work enables the possibility of self-adaptation of the myocontrol system - e.g. via on-demand model updates for incremental learning. According to this view, we performed an experiment using a simplified but still realistic grasping protocol involving four able-bodied expert myocontrol users, and we extracted features from a state-of-the-art commercial prosthetic hand to automatically identify instability in the myocontrol. The results show that a standard classifier is able to detect failures with a mean balanced error rate of 15.98% over the subjects that took part in the experiments. Our results can also be potentially applied in non-medical applications such as, e.g., teleoperation using extra-light interfaces.