title: Surface electromyogram signals classification based on bispectrum.
abstract: This paper bispectrum is used to classify human arm movements and control a robotic arm based on upper limb's surface electromyogram signals (sEMG). We use bispectrum based on third-order cumulant to parameterize sEMG signals and classify elbow flexion and extension, forearm pronation and supination, and rest states by an artificial neural network (ANN). Finally, a robotic manipulator is controlled based on classification and parameters extracted from the signals. All this process is made in real-time using QNX ® operative system.
authors.affiliation: Instituto de Automática, Facultad de Ingeniería, Universidad Nacional de San Juan. Argentina. eorosco@inaut.unsj.edu.ar
authors.forename: Eugenio
authors.initials: E
authors.lastname: Orosco
authors.forename: Natalia
authors.initials: N
authors.lastname: Lopez
authors.forename: Carlos
authors.initials: C
authors.lastname: Soria
authors.forename: Fernando
authors.initials: F
authors.lastname: di Sciascio
mesh_terms: D000328:Adult; D000465:Algorithms; D000672:Amputation Stumps / Q000503:physiopathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004551:Elbow Joint / Q000503:physiopathology; D004576:Electromyography / Q000379:methods*; D006801:Humans; D008297:Male; D009119:Muscle Contraction; D018482:Muscle, Skeletal / Q000503:physiopathology*; D010363:Pattern Recognition, Automated / Q000379:methods*