title: Modulation of grasping force in prosthetic hands using neural network-based predictive control.
abstract: This chapter describes the implementation of a neural network-based predictive control system for driving a prosthetic hand. Nonlinearities associated with the electromechanical aspects of prosthetic devices present great challenges for precise control of this type of device. Model-based controllers may overcome this issue. Moreover, given the complexity of these kinds of electromechanical systems, neural network-based modeling arises as a good fit for modeling the fingers' dynamics. The results of simulations mimicking potential situations encountered during activities of daily living demonstrate the feasibility of this technique.
authors.affiliation: Electronics Core-Medical Device Solutions, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA.
authors.forename: Cristian F
authors.initials: CF
authors.lastname: Pasluosta
authors.forename: Alan W L
authors.initials: AW
authors.lastname: Chiu
mesh_terms: D000203:Activities of Daily Living; D001186:Artificial Limbs; D006225:Hand / Q000503:physiopathology; D018737:Hand Strength; D006801:Humans; D055595:Mechanical Phenomena; D008954:Models, Biological; D016571:Neural Networks, Computer; D011474:Prosthesis Design / Q000379:methods*; D012815:Signal Processing, Computer-Assisted