title: Identification of motion from multi-channel EMG signals for control of prosthetic hand.
abstract: The authors in this paper propose an effective and efficient pattern recognition technique from four channel electromyogram (EMG) signals for control of multifunction prosthetic hand. Time domain features such as mean absolute value, number of zero crossings, number of slope sign changes and waveform length are considered for pattern recognition. The patterns are classified using simple logistic regression (SLR) technique and decision tree (DT) using J48 algorithm. In this study six specific hand and wrist motions are identified from the EMG signals obtained from ten different able-bodied. By considering relevant dominant features for pattern recognition, the processing time as well as memory space of the SLR and DT classifiers is found to be less in comparison with neural network (NN), k-nearest neighbour model 1 (kNN-Model-1), k-nearest neighbour model 2 (kNN-Model-2) and linear discriminant analysis. The classification accuracy of SLR classifier is found to be 91 ± 1.9%.
authors.affiliation: School of Electrical Engineering, VIT University, Vellore 632 014, Tamil Nadu, India. pganjali78@hotmail.com
authors.forename: P
authors.initials: P
authors.lastname: Geethanjali
authors.forename: K K
authors.initials: KK
authors.lastname: Ray
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000465:Algorithms; D001185:Artificial Intelligence; D001186:Artificial Limbs; D016002:Discriminant Analysis; D004576:Electromyography / Q000379:methods*; D006225:Hand / Q000502:physiology; D006801:Humans; D016015:Logistic Models; D009068:Movement / Q000502:physiology; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D011474:Prosthesis Design / Q000379:methods