Orthogonal Fuzzy Neighborhood Discriminant Analysis for Multifunction Myoelectric Hand Control

• Published in: IEEE transactions on biomedical engineering Vol. 57; no. 6; pp. 1410 - 1419
• Main Authors: Khushaba, Rami N., Al-Ani, Ahmed, Al-Jumaily, Adel
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Action Potentials - physiology; Algorithms; Australia; Biological and medical sciences; Discriminant Analysis; Diseases of the osteoarticular system. Orthopedic treatment; Electrodiagnosis. Electric activity recording; Electromyography; Electromyography - methods; Feature extraction; Fuzzy control; fuzzy discriminant analysis (FDA); Fuzzy Logic; Hand - physiology; Humans; Information technology; Investigative techniques, diagnostic techniques (general aspects); Medical sciences; Muscle Contraction - physiology; Muscle, Skeletal - physiology; Muscles; myoelectric control; Nervous system; Pattern recognition; Pattern Recognition, Automated - methods; Prosthetics; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Reproducibility of Results; Sensitivity and Specificity; Signal processing; Studies; Human; Discriminant analysis; Prosthesis; myoelectric control; Pattern recognition; fuzzy discriminant analysis (FDA); Muscle contraction; Hand; Signal processing; Neuromuscular control system; Feature extraction; Electromyography; Biomedical engineering
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2009.2039480

Developing accurate and powerful electromyogram (EMG) driven prostheses controllers that can provide the amputees with effective control on their artificial limbs, has been the focus of a great deal of research in the past few years. One of the major challenges in such research is extracting an informative subset of features that can best discriminate between the different forearm movements. In this paper, a new dimensionality reduction method, referred to as orthogonal fuzzy neighborhood discriminant analysis (OFNDA), is proposed as a response to such a challenge. Unlike existing attempts in fuzzy linear discriminant analysis, the objective of the proposed OFNDA is to minimize the distance between samples that belong to the same class and maximize the distance between the centers of different classes, while taking into account the contribution of the samples to the different classes. The proposed OFNDA is validated on EMG datasets collected from seven subjects performing a range of 5 to 10 classes of forearm movements. Practical results indicate the significance of OFNDA in comparison to many other feature projection methods (including locality preserving and uncorrelated variants of discriminant analysis) with accuracies ranging from 97.66% to 87.84% for 5 to 10 classes of movements, respectively, using only two EMG electrodes.