A Gaussian mixture model based classification scheme for myoelectric control of powered upper limb prostheses

• Published in: IEEE transactions on biomedical engineering Vol. 52; no. 11; pp. 1801 - 1811
• Main Authors: Yonghong Huang, Englehart, K.B., Hudgins, B., Chan, A.D.C.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.11.2005; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial Intelligence; Classification; Electromyography - methods; EMG; Gaussian mixture model; Joint Prosthesis; Linear discriminant analysis; Models, Biological; Models, Statistical; Movement - physiology; Multi-layer neural network; Multilayer perceptrons; Muscle Contraction - physiology; myoelectric signals; Neural networks; Normal Distribution; pattern recognition; Pattern Recognition, Automated - methods; Power system modeling; prosthesis; Prosthesis Design - methods; Prosthetics; Robustness; Spatial databases; Therapy, Computer-Assisted - methods; Time domain analysis; Upper Extremity - physiology; Voting
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2005.856295

This paper introduces and evaluates the use of Gaussian mixture models (GMMs) for multiple limb motion classification using continuous myoelectric signals. The focus of this work is to optimize the configuration of this classification scheme. To that end, a complete experimental evaluation of this system is conducted on a 12 subject database. The experiments examine the GMMs algorithmic issues including the model order selection and variance limiting, the segmentation of the data, and various feature sets including time-domain features and autoregressive features. The benefits of postprocessing the results using a majority vote rule are demonstrated. The performance of the GMM is compared to three commonly used classifiers: a linear discriminant analysis, a linear perceptron network, and a multilayer perceptron neural network. The GMM-based limb motion classification system demonstrates exceptional classification accuracy and results in a robust method of motion classification with low computational load.