A novel predictive approach to prosthetic control for digit amputations using grip classification

In this paper, the authors present a novel method of prosthetic control for amputees who are missing a subset of their digits. The core of the system is a personalized set of basic configurations of the human hand to facilitate given tasks, referred to as use-patterns. In the concept, the system first senses the positions of the remaining, healthy digits using resistive flex sensors mounted onto a glove. Then, an algorithm using a Z-score metric compares the flex sensor readings to a large set of training data to determine which of several use-patterns the current situation corresponds to. A control system can then move a bionic digit into the intended position corresponding to the use-pattern detected, completing the grip. MATLAB simulations were run to determine the accuracy of this concept on five exemplary use-patterns. The success rate with one amputated digit was 96.5% and the success rate with two amputated digits was 92.5%. This demonstrates the feasibility of a simple, intuitive, and low-cost prosthetic control system based on this premise that could deliver even higher success rates when personalized with self-learning algorithms.