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  4. Adaptive multichannel FES neuroprosthesis with learning control and automatic gait assessment

Adaptive multichannel FES neuroprosthesis with learning control and automatic gait assessment

Journal of NeuroEngineering and Rehabilitation, 2020 · DOI: https://doi.org/10.1186/s12984-020-0640-7 · Published: December 31, 2020

NeurologyNeurorehabilitationBiomechanics

Simple Explanation

This research focuses on creating a neuroprosthesis that uses functional electrical stimulation (FES) to help people with spinal cord injuries walk better. It adapts to each person's unique walking style by learning from their previous steps. The system continuously assesses the knee and foot joint angles during each step, compares them to a healthy gait pattern, and adjusts the electrical stimulation to support the muscles needed for walking. The goal is to provide real-time feedback and assistance, helping individuals achieve a more natural and efficient walking motion. The neuroprosthesis stimulates antagonistic muscle pairs for knee flexion and extension, as well as for ankle joint dorsi- and plantarflexion during all gait phases.

Study Duration
Not specified
Participants
Four people with an incomplete SCI
Evidence Level
Not specified

Key Findings

  • 1
    The proposed neuroprosthesis was able to generate individually fitted stimulation patterns for three of the participants with incomplete SCI.
  • 2
    For two of the three remaining participants, a slight improvement in the average foot angles could be observed.
  • 3
    For one participant slight improvements in the averaged knee angles could be observed; improvements where in the range of 4° at the times of peak dorsiflexion, peak plantarflexion, or peak knee flexion.

Research Summary

This paper presents an adaptive FES neuroprosthesis for full-leg support, utilizing learning control and automatic gait assessment. The system aims to provide individually tailored stimulation patterns to assist individuals with SCI in improving their gait. The neuroprosthesis incorporates continuous joint angle assessment, dynamic time warping for reference adjustment, and an iterative learning controller that adapts to the individual's gait in real-time. Initial testing with SCI participants showed promising results, with the system generating individualized stimulation patterns and some participants experiencing slight improvements in joint angles. However, challenges remain in areas such as knee angle reset sensitivity and ILC gain tuning.

Practical Implications

Personalized Rehabilitation

The neuroprosthesis's adaptive nature allows for tailored rehabilitation programs that respond to the individual's specific needs and gait patterns.

Real-time Biofeedback

The system provides continuous feedback to the user through targeted muscle stimulation, potentially enhancing the learning and motor control during rehabilitation.

Advanced Gait Assessment

The proposed gait assessment method can be used for automated clinical gait assessment, biofeedback, or gamification of rehabilitation training.

Study Limitations

  • 1
    Sensitivity to the knee angle reset
  • 2
    Timing problems in participants with significant gait fluctuations
  • 3
    Automatic ILC gain tuning

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