Design, implementation and evaluation for a high precision prosthetic hand using MyoBand and Random Forest algorithm

Abstract

A prosthesis is an equipment provided to people who lost one or some parts of their limbs to help them having almost normal behaviors in daily or hard activities. The convenience and intelligence of devices should create easiness and flexibility for users. Artificial devices require interdisciplinary collaboration from neurosurgeons, surgical surgeons, physiotherapists and equipment development. Computer engineering plays a crucial role in the design step, supporting manufacturing, training and recognition to match the desirability of customers. Moreover, users need a wide range of different options such as an aesthetic functional material, a myoelectric mechanism, a body-powered appliance or an activity specified device. Thus, the flexible configuration, the proper features and the cost are some important factors that drive user’s selection to the prosthesis. In this article, we describe an effective and powerful solution for analyzing, designing hardware and implementing software to train and recognize hand gestures for prosthetic arms. Moreover, we provide evaluation data of the method compared with similar approaches to support our design and implementation. This is fairly a complete system, making it a convenient solution for hand-cutoff people to control prosthetic hands using their electromyography signals. Statistical results with evaluations show that the device can respond correspondingly and the method creates promisingly recognition data after correct training processes. The prosthetic hardware implementation has also been simulated using a Light-emitting diode (LED) hand model with a high accuracy result.