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Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Published on: April 18, 2025

A microcomputer FES system for wrist moving control.

Li Cao¹, Jin-Sheng Yang, Zhi-Long Geng

¹College of Civil Aviation, Nanjing University of Aeronautics & Astronautics, Nanjing, 210016, China. caoli@nuaa.edu.cn

Advances in Experimental Medicine and Biology

|March 25, 2011

Summary

This summary is machine-generated.

A portable functional electrical stimulation (FES) system uses a self-adaptive PI control strategy with a neural network identifier to adapt to muscle variability, achieving under 4% tracking precision in experiments.

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Area of Science:

Biomedical Engineering
Rehabilitation Technology
Control Systems

Background:

Functional electrical stimulation (FES) systems require precise control to effectively stimulate muscles.
Muscle responses are inherently time-varying and nonlinear, posing challenges for traditional control methods.
Existing FES controllers often lack adaptability to individual muscle condition changes.

Purpose of the Study:

To develop and evaluate a portable, closed-loop FES system with enhanced adaptability.
To address the challenges of time-varying nonlinearities in muscle activation.
To improve the precision and reliability of FES-assisted movements.

Main Methods:

A portable closed-loop FES system controlled by a microcomputer was designed.
A self-adaptive proportional-integral (PI) control strategy was implemented, incorporating a neural network identifier (NNI).
The NNI dynamically identified the muscle model online by assessing muscle working condition variability.
PI controller parameters were optimized based on the NNI's online identification results.

Main Results:

The system demonstrated the ability to adapt to changing muscle conditions through online identification.
Tracking experiments conducted on able-bodied volunteers showed high precision.
The achieved tracking precision was consistently under 4%.

Conclusions:

The proposed self-adaptive PI control strategy effectively manages the time-varying nonlinear dynamics of muscle systems.
The portable closed-loop FES system offers a promising approach for precise and adaptive muscle stimulation.
The system's high precision suggests potential for improved rehabilitation and assistive applications.