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 fby 29% compared with optimal, constant excitation.
Original language | English |
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Pages (from-to) | 85-92 |
Number of pages | 8 |
Journal | Journal of Applied Biomechanics |
Volume | 28 |
Issue number | 1 |
DOIs | |
State | Published - Feb 2012 |
Keywords
- Balance
- Functional neuromuscular stimulation
- Posture
- Rehabilitation
- Spinal cord injury