AI Article Synopsis
- The research focuses on improving sensory feedback and control systems for functional electrical stimulation (FES) to help correct foot drop, using advanced signal purification techniques like low pass filtering and wavelet denoising.
- The adaptive logic networks (ALN) generated control pulses for stimulating the leg during crucial movement phases, but issues arose with sporadic spikes and inconsistent stimulation signals.
- The introduction of adaptive restriction rules (ARR) enhances the safety and reliability of the stimulation pattern compared to traditional fixed rules, offering a more dynamic response during use.
Article Abstract
We report on our advances in sensory feedback data processing and control system design for functional electrical stimulation (FES) assisted correction of foot drop. We have applied 2 methods of signal purification on the bin integrated electroneurogram (i.e., optimized low pass filtering and wavelet denoising) before training adaptive logic networks (ALN). ALN generated stimulation control pulses, which correspond to the swing phase of the impaired leg when dorsal flexion of the foot is necessary to provide safe ground clearance. However, the obtained control signal contained sporadic stimulation spikes in the stance phase, which can collapse the subject, and infrequent broken stimulation pulses in the swing phase, which can result in unpredictable consequences. In this study, we have introduced adaptive restriction rules (ARR), which are initially used as previously reported and then dynamically adapted during the use of the system. Our results suggest that ARR provide a safer and more reliable stimulation pattern than fixed restriction rules.
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| http://dx.doi.org/10.1046/j.1525-1594.1999.06375.x | DOI Listing |
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