In this paper, a prosthetic robot hand was designed and fabricated and experimental evaluation of the realization of basic gripping motions was performed. As a first step, a robot finger was designed with same structural configuration of the human hand and the movement of the finger was evaluated via kinematic analysis. Electromyogram (EMG) signals for hand motions were measured using commercial wearable EMG sensors and classification of hand motions was achieved by applying the artificial neural network (ANN) algorithm. After training and testing for three kinds of gripping motions via ANN, it was observed that high classification accuracy can be obtained. A prototype of the proposed robot hand is manufactured through 3D printing and servomotors are included for position control of fingers. It was demonstrated that effective realization of gripping motions of the proposed prosthetic robot hand can be achieved by using EMG measurement and machine learning-based classification under a real-time environment.

This paper proposes a myoelectric hand prosthesis with an easy control strategy to apply more conveniently with just two EMG sensors. The myoelectric hand prosthesis is composed of a multi-DOF finger mechanism, a controller, and an intuitive control algorithm. The developed hand prosthesis has 6-DOFs and can perform eight hand motions using the intuitive control algorithm. The proposed intuitive control algorithm classifies four grip motions and four gesture motions; we used the thumb position of the hand prosthesis and three EMG signals (Co-contraction, flexion, and extension) generated from the two EMG sensors. From the experimental results, we demonstrated that the proposed myoelectric hand prosthesis is applicable to amputees as a hand prosthesis.