title: Pattern recognition of hand movements with low density sEMG for prosthesis control purposes.
abstract: This paper presents a study related to the identification of different hand gestures from EMG signals from forearm muscles, to be used as human machine interface system in a hand prosthesis. The capture of EMG signals was performed with healthy people during different hand gestures related to the fingers flexion-individual and pairs- and flexion / extension and grasp grisp, organized into four categories. The low-level and low-density of sEMG signals was taking into account. Different characteristics were studied based on time and frequency, and were subsequently combined into pairs with fractal analysis, used for low level schemes. The results showed 95.4% higher than recognitions.
authors.forename: J J
authors.initials: JJ
authors.lastname: Villarejo
authors.forename: A
authors.initials: A
authors.lastname: Frizera
authors.forename: T F
authors.initials: TF
authors.lastname: Bastos
authors.forename: J F
authors.initials: JF
authors.lastname: Sarmiento
mesh_terms: D000328:Adult; D000465:Algorithms; D001186:Artificial Limbs; D004576:Electromyography / Q000379:methods*; D005260:Female; D005542:Forearm / Q000502:physiology*; D005868:Gestures; D006225:Hand / Q000502:physiology*; D006801:Humans; D008297:Male; D018482:Muscle, Skeletal / Q000502:physiology; D016571:Neural Networks, Computer