title: EMG-force modeling using parallel cascade identification.
abstract: Measuring force production in muscles is important for many applications such as gait analysis, medical rehabilitation, and human-machine interaction. Substantial research has focused on finding signal processing and modeling techniques which give accurate estimates of muscle force from the surface-recorded electromyogram (EMG). The proposed methods often do not capture both the nonlinearities and dynamic components of the EMG-force relation. In this study, parallel cascade identification (PCI) is used as a dynamic estimation tool to map surface EMG recordings from upper-arm muscles to the induced force at the wrist. PCI mapping involves generating a parallel connection of a series of linear dynamic and nonlinear static blocks. The PCI model parameters were initialized to obtain the best force prediction. A comparison between PCI and a previously published Hill-based orthogonalization scheme, that captures physiological behaviour of the muscles, has shown 44% improvement in force prediction by PCI (averaged over all subjects in relative-mean-square sense). The improved performance is attributed to the structural capability of PCI to capture nonlinear dynamic effects in the generated force.
authors.affiliation: Department of Electrical and Computer Engineering, Queen's University, Kingston, ON, Canada.
authors.forename: Javad
authors.initials: J
authors.lastname: Hashemi
authors.forename: Evelyn
authors.initials: E
authors.lastname: Morin
authors.forename: Parvin
authors.initials: P
authors.lastname: Mousavi
authors.forename: Katherine
authors.initials: K
authors.lastname: Mountjoy
authors.forename: Keyvan
authors.initials: K
authors.lastname: Hashtrudi-Zaad
mesh_terms: D000465:Algorithms; D000818:Animals; D003198:Computer Simulation; D004576:Electromyography / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D009119:Muscle Contraction / Q000502:physiology*; D053580:Muscle Strength / Q000502:physiology*; D018482:Muscle, Skeletal / Q000502:physiology*