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Optimal sampling of recruitment curves for functional electrical stimulation control
Eric M Schearer1, Yu-Wei Liao, Eric J Perreault
1Department of Mechanical Engineering, Northwestern University, Evanston, IL, USA. eschearer@u.northwestern.edu
Summary
Optimizing functional electrical stimulation (FES) system identification, this study introduces novel sampling methods. These approaches reduce the experiments needed to accurately model muscle force recruitment curves, improving control system development.
Area of Science:
- Biomedical Engineering
- Rehabilitation Technology
- Neuroprosthetics
Background:
- Controlling multiple-input multiple-output functional electrical stimulation (MIMO-FES) systems is challenging due to the extensive time needed for system model identification.
- The high dimensionality of input spaces in MIMO-FES systems significantly contributes to the time-consuming model identification process.
Purpose of the Study:
- To explore optimal methods for sampling the input space in MIMO-FES systems.
- To present and evaluate two novel methods for optimally sampling isometric muscle force recruitment curves.
Main Methods:
- Developed two optimal sampling strategies: one maximizing information about recruitment curve parameters, the other minimizing predicted output force variance.
- Compared these optimal methods against two traditional methods using simulation.
- The simulation model was identified from experimental recruitment data of a human subject with high spinal cord injury.
Main Results:
- Optimal sampling methods yielded estimates of output force with reduced error compared to previously used methods.
- The proposed optimal sampling strategies required fewer system identification experiments to achieve comparable output prediction accuracy.
Conclusions:
- Optimal sampling methods significantly enhance the efficiency of identifying MIMO-FES system models.
- These findings can accelerate the development and improve the control of FES systems for individuals with spinal cord injuries.
