Experimental Study of Real-Time Classification of 17 Voluntary Movements for Multi-Degree Myoelectric Prosthetic Hand

The myoelectric prosthetic hand is a powerful tool developed to help people with upper limb loss restore the functions of a biological hand. Recognizing multiple hand motions from only a few electromyography (EMG) sensors is one of the requirements for the development of prosthetic hands with high level of usability. This task is highly challenging because both classification rate and misclassification rate worsen with additional hand motions. This paper presents a signal processing technique that uses spectral features and an artificial neural network to classify 17 voluntary movements from EMG signals. The main highlight will be on the use of a small set of low-cost EMG sensor for classification of a reasonably large number of hand movements. The aim of this work is to extend the capabilities to recognize and produce multiple movements beyond what is currently feasible. This work will also show and discuss about how tailoring the number of hand motions for a specific task can help develop a more reliable prosthetic hand system. Online classification experiments have been conducted on seven male and five female participants to evaluate the validity of the proposed method. The proposed algorithm achieves an overall correct classification rate of up to 83%, thus, demonstrating the potential to classify 17 movements from 6 EMG sensors. Furthermore, classifying 9 motions using this method could achieve an accuracy of up to 92%. These results show that if the prosthetic hand is intended for a specific task, limiting the number of motions can significantly increase the performance and usability.