Using Deep Learning and Mobile Offloading to Control a 3D-printed Prosthetic Hand

Abstract

Although many children are born with congenital limb malformation, contemporary functional artificial hands are costly and are not meant to be adapted to growing hand. In this work, we develop a low cost, adaptable and personalizable system of an artificial prosthetic hand accompanied with hardware and software modules. Our solution consists of (i) a consumer grade electromyography (EMG) recording hardware, (ii) a mobile companion device empowered by deep learning classification algorithms, (iii) an cloud component for offloading computations, and (iv) mechanical 3D printed arm operated by the embedded hardware. We focus on the flexibility of the designed system making it more affordable than the alternatives. We use 3D printed materials and open-source software thus enabling the community to contribute and improve the system. In this paper, we describe the proposed system and its components and present the experiments we conducted in order to show the feasibility and applicability of our approach. Extended experimentation shows that our proposal is energy efficient and has high accuracy.