Self-trainable 3D-printed prosthetic hands

Abstract

3D printed prosthetics have narrowed the gap between the tens of thousands of dollars cost of traditional prosthetic designs and amputees’ needs. However, the World Health Organization estimates that only 5-15% of people can receive adequate prosthesis services [2]. To resolve the lack of prosthesis supply and reduce cost issues (for both materials and maintenance), this paper provides an overview of a self-trainable user-customized system architecture for a 3D printed prosthetic hand to minimize the challenge of accessing and maintaining these supporting devices. In this paper, we develop and implement a customized behavior system that can generate any gesture that users desire. The architecture provides upper limb amputees with self-trainable software and can improve their prosthetic performance at almost no financial cost. All kinds of unique gestures that users want are trainable with the RBF network using 3 channel EMG sensor signals with a 94% average success rate. This result demonstrates that applying user-customized training to the behavior of a prosthetic hand can satisfy individual user requirements in real-life activities with high performance.