title: Adaptive neuro-fuzzy logic analysis based on myoelectric signals for multifunction prosthesis control.
abstract: The myoelectric signal is a sign of control of the human body that contains the information of the user's intent to contract a muscle and, therefore, make a move. Studies shows that the Amputees are able to generate standardized myoelectric signals repeatedly before of the intention to perform a certain movement. This paper presents a study that investigates the use of forearm surface electromyography (sEMG) signals for classification of five distinguish movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an adaptive neuro-fuzzy inference system (ANFIS) to process signal features to recognize performed movements. The average accuracy reached for the classification of five motion classes was 86-98% for three subjects.
authors.affiliation: Department of Electrical Engineering, Laboratory IEE – PPGEE, Federal University of Rio Grande do Sul, Av Osvaldo Aranha, 103 – 206, 90035190 PortoAlegre, RS, Brazil. gwfavieiro@inf.ufrgs.br
authors.forename: Gabriela W
authors.initials: GW
authors.lastname: Favieiro
authors.forename: Alexandre
authors.initials: A
authors.lastname: Balbinot
mesh_terms: D004566:Electrodes; D004576:Electromyography / Q000379:methods*; D055724:Electrophysiological Phenomena; D017143:Fuzzy Logic; D006801:Humans; D009068:Movement / Q000502:physiology; D009119:Muscle Contraction / Q000502:physiology; D009132:Muscles / Q000502:physiology*; D009424:Nervous System Physiological Phenomena; D019736:Prostheses and Implants; D012815:Signal Processing, Computer-Assisted