title: Trunk acceleration for neuroprosthetic control of standing: a pilot study.
abstract: This pilot study investigated the potential of using trunk acceleration feedback control of center of pressure (COP) against postural disturbances with a standing neuroprosthesis following paralysis. Artificial neural networks (ANNs) were trained to use three-dimensional trunk acceleration as input to predict changes in COP for able-bodied subjects undergoing perturbations during bipedal stance. Correlation coefficients between ANN predictions and actual COP ranged from 0.67 to 0.77. An ANN trained across all subject-normalized data was used to drive feedback control of ankle muscle excitation levels for a computer model representing a standing neuroprosthesis user. Feedback control reduced average upper-body loading during perturbation onset and recovery by 42% and peak loading by 29% compared with optimal, constant excitation.
authors.affiliation: Louis Stokes VAMC, Cleveland, OH, USA.
authors.forename: Raviraj
authors.initials: R
authors.lastname: Nataraj
authors.forename: Musa L
authors.initials: ML
authors.lastname: Audu
authors.forename: Robert F
authors.initials: RF
authors.lastname: Kirsch
authors.forename: Ronald J
authors.initials: RJ
authors.lastname: Triolo
mesh_terms: D000054:Acceleration; D000328:Adult; D000842:Ankle / Q000502:physiology; D005260:Female; D006801:Humans; D008297:Male; D009119:Muscle Contraction / Q000502:physiology; D018482:Muscle, Skeletal / Q000502:physiology; D016571:Neural Networks, Computer; D058765:Neurofeedback / Q000502:physiology*; D010865:Pilot Projects; D004856:Postural Balance / Q000502:physiology*; D013119:Spinal Cord Injuries / Q000534:rehabilitation*; D013223:Statistics as Topic; D013997:Time Factors; D016474:Weight-Bearing / Q000502:physiology; D055815:Young Adult