title: Hand motion estimation by EMG signals using linear multiple regression models.
abstract: The purpose of this research is to construct an intelligent upper limb prosthesis control system that uses electromyogram (EMG) signals. The signal processing of EMG signals is performed using a linear multiple regression model that can learn parameters in a short time. Using this model, joint angles are predicted, and the motion pattern discrimination is conducted. Discriminated motions were grip, open, and chuck of a hand. Predicted joint angles were multi-finger angles corresponding to these three motions. In several experiments we proved the usefulness of processing EMG signals with a linear multiple regression model.
authors.affiliation: Department of Mechanical Engineering, Doshisha University, Kyotanabe, Kyoto, Tokyo. dtf0328@mail4.doshisha.ac.jp
authors.forename: Toru
authors.initials: T
authors.lastname: Kitamura
authors.forename: Nobutaka
authors.initials: N
authors.lastname: Tsujiuchi
authors.forename: Takayuki
authors.initials: T
authors.lastname: Koizumi
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D004576:Electromyography / Q000379:methods*; D006225:Hand / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D009119:Muscle Contraction / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity