Abstract
Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins’ set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands.
Highlights
Myoelectric pattern recognition (MPR) controlled prosthesis ideally mimics the functionality of a natural limb using signals from residual muscles left over after amputation or congenital defect
Among the classifiers under investigation, k-nearest neighbor (KNN) and multilayer perceptron (MLP) offered the best performance for time domain features
linear discriminant analysis (LDA) and MLP showed higher accuracy than other classifiers when used in combination with wavelet packet features (LogRMS and Normalized logarithmic energy (NLE)), but at the cost of training and testing time
Summary
Myoelectric pattern recognition (MPR) controlled prosthesis ideally mimics the functionality of a natural limb using signals from residual muscles left over after amputation or congenital defect. The ability of MPR systems to decode motor volition is dependent on how well each stage of the system performs. MPR processing stages are typically divided into pre-processing, feature extraction, and classification [1]. Pre-processing is used to remove unwanted signal components from the raw electromyogram (EMG) like motion artifacts or power line interference. EMG signals are windowed and signal features are calculated over each window. Signal features can be grouped into three categories, time domain (TD) [1], frequency domain (FD), and time-frequency domain (TFD) [2], each describing different components of the signal. If the resulting feature space is sufficiently high, dimensionality reduction techniques like principal component analysis (PCA) may be employed to improve classification accuracy and efficiency [3]. The resulting feature vectors are fed into a classifier for training and decoding motor volition
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