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  4. OIDA: An optimal interval detection algorithm for automatized determination of stimulation patterns for FES‑Cycling in individuals with SCI

OIDA: An optimal interval detection algorithm for automatized determination of stimulation patterns for FES‑Cycling in individuals with SCI

Journal of NeuroEngineering and Rehabilitation, 2022 · DOI: https://doi.org/10.1186/s12984-022-01018-2 · Published: April 5, 2022

NeurologyRehabilitationBiomedical

Simple Explanation

FES-Cycling enables individuals with spinal cord injuries to exercise paralyzed muscles by activating specific muscles at the correct times. The study introduces an algorithm, OIDA, that uses torque feedback from a crank power-meter to identify optimal stimulation intervals for muscles during FES-Cycling. The algorithm's functionality was demonstrated on a subject with complete SCI, showing successful identification of stimulation intervals for quadriceps and hamstring muscles, leading to smooth cycling.

Study Duration
12+ months
Participants
1 male participant with complete spinal cord injury (T4, ASI A)
Evidence Level
Not specified

Key Findings

  • 1
    The OIDA algorithm successfully identified stimulation intervals for the left and right quadriceps and hamstring muscles in a subject with complete SCI.
  • 2
    Smooth cycling was achieved without further adaptation using both crank-angle and normalized thigh-angle as input signals.
  • 3
    The algorithm does not rely on constant angular velocity, making it applicable to mobile FES-Cycling systems.

Research Summary

The study introduces an algorithm (OIDA) for automated determination of stimulation patterns in FES-Cycling, utilizing torque feedback to identify optimal stimulation intervals for specific muscle groups. The OIDA algorithm was successfully tested on a subject with complete SCI, demonstrating its ability to identify suitable stimulation intervals for quadriceps and hamstring muscles, enabling smooth cycling. The presented algorithm can help reduce fitting time and improve cycling quality, and its independence from constant angular velocity makes it suitable for mobile FES-Cycling systems.

Practical Implications

Reduced Fitting Time

The automatic determination of stimulation patterns can significantly shorten the initial fitting phase, improving user experience.

Improved Cycling Quality

Optimized stimulation patterns based on net-torque enhance the smoothness and efficiency of pedaling during FES-Cycling sessions.

Mobile FES-Cycling

The algorithm's independence from constant angular velocity facilitates its implementation in mobile FES-Cycling systems, expanding accessibility.

Study Limitations

  • 1
    The algorithm was validated on only a single subject.
  • 2
    Tests were performed without resistance from the home trainer, which introduces a particular challenge.
  • 3
    Over-ground cycling was only demonstrated with the IMU-based pattern.

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