A mechatronics platform to study prosthetic hand control using EMG signals.
In this paper, a low-cost mechatronics platform for the design and development of robotic hands as well as a surface electromyogram (EMG) pattern recognition system is proposed. This paper also explores various EMG classification techniques using a low-cost electronics system in prosthetic hand applications. The proposed platform involves the development of a four channel EMG signal acquisition system; pattern recognition of acquired EMG signals; and development of a digital controller for a robotic hand. Four-channel surface EMG signals, acquired from ten healthy subjects for six different movements of the hand, were used to analyse pattern recognition in prosthetic hand control. Various time domain features were extracted and grouped into five ensembles to compare the influence of features in feature-selective classifiers (SLR) with widely considered non-feature-selective classifiers, such as neural networks (NN), linear discriminant analysis (LDA) and support vector machines (SVM) applied with different kernels. The results divulged that the average classification accuracy of the SVM, with a linear kernel function, outperforms other classifiers with feature ensembles, Hudgin's feature set and auto regression (AR) coefficients. However, the slight improvement in classification accuracy of SVM incurs more processing time and memory space in the low-level controller. The Kruskal-Wallis (KW) test also shows that there is no significant difference in the classification performance of SLR with Hudgin's feature set to that of SVM with Hudgin's features along with AR coefficients. In addition, the KW test shows that SLR was found to be better in respect to computation time and memory space, which is vital in a low-level controller. Similar to SVM, with a linear kernel function, other non-feature selective LDA and NN classifiers also show a slight improvement in performance using twice the features but with the drawback of increased memory space requirement and time. This prototype facilitated the study of various issues of pattern recognition and identified an efficient classifier, along with a feature ensemble, in the implementation of EMG controlled prosthetic hands in a laboratory setting at low-cost. This platform may help to motivate and facilitate prosthetic hand research in developing countries.
School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, 632 014, India. pganjali78@hotmail.com.
P
P
Geethanjali
D000465:Algorithms; D004576:Electromyography / Q000379:methods*; D005385:Fingers / Q000502:physiology; D006225:Hand / Q000503:physiopathology*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D019736:Prostheses and Implants; D012371:Robotics; D012815:Signal Processing, Computer-Assisted; D013223:Statistics as Topic
EMG; Feature ensemble; Linear discriminant analysis (LDA); Neural network (NN); Prosthetic hand; Simple logistic regression (SLR); Support vector machine (SVM); Time domain features