An Affordable 3D-printed Open-Loop Prosthetic Hand Prototype with Neural Network Learning EMG-Based Manipulation for Amputees

The University of Aizu, School of Computer Science and Engineering, Adaptive Systems Laboratory, Japan

^a) Corresponding author: m5251138@u-aizu.ac.jp
^b) Electronic mail: d8211104@u-aizu.ac.jp
^c) Electronic mail: benab@u-aizu.ac.jp

Abstract

Despite the advancement of prosthetic hands, many of the conventional products are difficult to control and have limited capabilities. Even though these limitations are being pushed by many state-of-the-art commercial prosthetic hand products, they are often expensive due to the high cost of production. Therefore, in the Adaptive Neuroprosthesis Arm (NeuroSys) project, we aim to develop a low-cost prosthetic hand with high functionalities that let users perform various gestures and accurate grasp. This paper mainly focuses on the sEMG signal recognition and control for a prototype 3D printed prosthetic hand model. In this work, we have considered the prosthetic hand to operate from a non-intrusive sensor, surface Electromyographic signal (sEMG). The signal used to control the prosthetic hand is received from a low-cost, 8-channel sEMG sensor, Myo armband. The sensor is placed around a person’s upper forearm under the elbow, and the signal is sent wirelessly to a computer. After the signal is received, a neural network is used to recognize and classify the intention of the signals. The network model is designed for specific individuals to increase the controllability of the prosthetic hand. Also, to mimic the real-world usage, evaluation on two different sessions is conducted. With the use of Recurrent Neural Networks (RNNs) family, sEMG data recognition can reach around 85% of accuracy. While Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTM) have similar results, simple RNN unit produces very low accuracy. Also, the more session the sample data is taken, the more robust the recognition system can be. Using the Myo armband sensor, sEMG signal data during a steady state with force or no force can affect the accuracy performance of the decoding hand gestures. In terms of real-world usage, however the constant force must be applied, otherwise, the system fails to classify the gestures. Also, the variation of sensor placement can affect the deep learning model. Although, there is a trade-off between accuracy and delay, optimal window size can be explored. Using the mentioned method, a prototype of an affordable 3D printed prosthetic hand controlled using sEMG is realized, although it is still far from real-world usage.