An Unobtrusive Vision System to Reduce the Cognitive Burden of Hand Prosthesis Control

This paper introduces an inexpensive prosthetic hand control system designed to reduce the cognitive burden on amputees. It is designed around a vision-based object recognition system with an embedded camera that automates grasp selection and switching, and an inexpensive mechanomyography (MMG) sensor for hand opening and closing. A prototype has been developed and implemented to select between two different grasp configurations for the Bebionic V2 hand, developed by RSLSteeper. Pick and place experiments on 6 different objects in 'Power' and 'Pinch' grasps were used to assess feasibility on which to base full system development. Experimentation demonstrated an overall accuracy of 84.4% for grasp selection between pairs of objects. The results showed that it was more difficult to classify larger objects due to their size relative to the camera resolution. The grasping task became more accurate with time, indicating learning capability when estimating the position and trajectory of the hand for correct grasp selection; however further experimentation is required to form a conclusion. The limitation of this involves the use of unnatural reaching trajectories for correct grasp selection. The success in basic experimentation provides the proof of concept required for further system development.