Myoelectric signals (MES) have been used in various applications, in particular, for identification of user intention to potentially control assistive devices for amputees, orthotic devices, and exoskeleton in order to augment capability of the user. MES are also used to estimate force and, hence, torque to actuate the assistive device. The application of MES is not limited to assistive devices, and they also find potential applications in teleoperation of robots, haptic devices, virtual reality, and so on. The myoelectric control-based prosthetic hand aids to restore activities of daily living of amputees in order to improve the self-esteem of the user. All myoelectric control-based prosthetic hands may not have similar operations and exhibit variation in sensing input, deciphering the signals, and actuating prosthetic hand. Researchers are focusing on improving the functionality of prosthetic hand in order to suit the user requirement with the different operating features. The myoelectric control differs in operation to accommodate various external factors. This article reviews the state of the art of myoelectric prosthetic hand, giving description of each control strategy. © 2016 Geethanjali.

Geethanjali P

Department of Control and Automation

School of Electrical Engineering

Vellore Campus

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Myoelectric control of prosthetic hands: state-of-the-art review.

Medical Devices: Evidence and Research

The focus of this paper is the development of a low-cost research platform for a surface electromyogram (EMG)-based prosthetic hand control to evaluate various pattern recognition techniques and to study the real-time implementation. This comprehensible research platform may enlighten the biomedical research in developing countries for analyzing and evaluating the surface EMG signals. Major challenges in this work were as follows: the design and development of an EMG signal conditioning module, a pattern recognition module, and a prosthetic hand at low cost. Besides, EMG pattern recognition techniques were evaluated for identifying six hand motions in offline with signals acquired from ten healthy subjects and two transradial amputees. Features calculated from EMG signals were grouped into six ensembles to apprehend the vitality of the ensemble in classifiers namely simple logistic regression (SLR), J48 algorithm for decision tree, logistic model tree, neural network, linear discriminant analysis, and support vector machine. The classification performance was also evaluated with the prolonged EMG data recorded on a day at every 1 h interval to study the robustness of the classifier. The results show the average classification accuracy, processing time and memory requirement of the SLR was found to be better and robust with time-domain features consisting of statistical as well as autoregression coefficients. The statistical analysis of variance test also showed that computation time and memory space required for SLR were significantly less compared to the other classifiers. The performance of the classifier was tested in online with transradial amputee for actuation of prosthetic hand for two intended motions with a TMS320F28335 controller. This proposed research platform for evaluation of EMG pattern recognition and real-time implementation has been achieved at a cost of 25 000 Indian rupee (INR). © 2014 IEEE.

IEEE/ASME Transactions on Mechatronics

A Low-Cost Real-Time Research Platform for EMG Pattern Recognition-Based Prosthetic Hand

Electromyographic (EMG) signals are abundantly used in the field of rehabilitation engineering in controlling the prosthetic device and significantly essential to find fast and accurate EMG pattern recognition system, to avoid intrusive delay. The main objective of this paper is to study the influence of Principal component analysis (PCA), a transformation technique, in pattern recognition of six hand movements using four channel surface EMG signals from ten healthy subjects. For this reason, time domain (TD) statistical as well as auto regression (AR) coefficients are extracted from the four channel EMG signals. The extracted statistical features as well as AR coefficients are transformed using PCA to 25, 50 and 75 % of corresponding original feature vector space. The classification accuracy of PCA transformed and non-PCA transformed TD statistical features as well as AR coefficients are studied with simple logistic regression (SLR), decision tree (DT) with J48 algorithm, logistic model tree (LMT), k nearest neighbor (kNN) and neural network (NN) classifiers in the identification of six different movements. The Kruskal-Wallis (KW) statistical test shows that there is a significant reduction (P\0.05) in classification accuracy with PCA transformed features compared to non-PCA transformed features. SLR with non-PCA transformed time domain (TD) statistical features performs better in accuracy and computational power compared to other features considered in this study. In addition, the motion control of three drives for six movements of the hand is implemented with SLR using TD statistical features in offline with TMSLF2407 digital signal controller (DSC). © Australasian College of Physical Scientists and Engineers in Medicine 2015.

Australasian Physical & Engineering Sciences in Medicine

Comparative study of PCA in classification of multichannel EMG signals

IEEE Transactions on Industrial Electronics

sEMG-Based Identification of Hand Motion Commands Using Wavelet Neural Network Combined With Discrete Wavelet Transform

IEEE Transactions on Neural Systems and Rehabilitation Engineering

Closed-Loop Control of Myoelectric Prostheses With Electrotactile Feedback: Influence of Stimulation Artifact and Blanking

Neurocomputing

A real-time EMG pattern recognition method for virtual myoelectric hand control

Myoelectric control of prosthetic hands: state-of-the-art review

Journal	Medical Devices: Evidence and Research
Publisher	Informa UK Limited
ISSN	11791470
Open Access	Yes