title: EMG-based learning approach for estimating wrist motion.
abstract: This paper proposes an EMG based learning approach for estimating the displacement along the 2-axes (abduction/adduction and flexion/extension) of the human wrist in real-time. The algorithm extracts features from the EMG electrodes on the upper and forearm and uses Support Vector Regression to estimate the intended displacement of the wrist. Using data recorded with the arm outstretched in various locations in space, we train the algorithm so as to allow robust prediction even when the subject moves his/her arm across several positions in space. The proposed approach was tested on five healthy subjects and showed that a R(2) index of 63.6% is obtained for generalization across different arm positions and wrist joint angles.
authors.forename: S
authors.initials: S
authors.lastname: El-Khoury
authors.forename: I
authors.initials: I
authors.lastname: Batzianoulis
authors.forename: C W
authors.initials: CW
authors.lastname: Antuvan
authors.forename: S
authors.initials: S
authors.lastname: Contu
authors.forename: L
authors.initials: L
authors.lastname: Masia
authors.forename: S
authors.initials: S
authors.lastname: Micera
authors.forename: A
authors.initials: A
authors.lastname: Billard
mesh_terms: D000328:Adult; D000465:Algorithms; D001132:Arm / Q000502:physiology; D004576:Electromyography / Q000379:methods*; D005542:Forearm; D006801:Humans; D000069550:Machine Learning; D009068:Movement; D016059:Range of Motion, Articular; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D014955:Wrist Joint / Q000502:physiology*; D055815:Young Adult