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An Assist-as-Needed Control Strategy Based on a Subjective Intention Decline Model.

Hao Yan1,2, Fangcao Zhang3, Xingao Li1,2

  • 1School of Mechanical and Equipment Engineering, Hebei University of Engineering, Handan 056038, China.

Bioengineering (Basel, Switzerland)
|November 27, 2024
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Summary

This study introduces an assist-as-needed strategy for stroke rehabilitation, using a subjective intention decline model to optimize assistive force. The method enhances patient initiative and prevents over-reliance on robotic assistance during training.

Keywords:
assist-as-needed control strategyquantum particle swarm optimization algorithmsubjective intention attenuation model

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Area of Science:

  • Rehabilitation Engineering
  • Biomedical Engineering
  • Neurorehabilitation

Background:

  • Stroke rehabilitation requires patient engagement for optimal outcomes.
  • Current assistive systems can lead to patient dependence.
  • Physiological excitement positively influences rehabilitation efficacy.

Purpose of the Study:

  • To develop an adaptive assist-as-needed control strategy for stroke rehabilitation.
  • To enhance patient initiative and reduce reliance on assistive devices.
  • To integrate subjective intention and limb movement assessment for personalized support.

Main Methods:

  • A subjective intention decline model using surface electromyography (sEMG) optimized with Quantum Particle Swarm Optimization (QPSO) and Support Vector Machine (SVM).
  • A limb movement assessment module analyzing interaction force and position error.
  • Real-time adjustment of assistive force based on combined limb movement and subjective status evaluation.

Main Results:

  • Reduced average assist force by 65.66% compared to traditional impedance control.
  • Reduced average assist force by 35.2% compared to force/position-based control.
  • Achieved 93.41% accuracy in identifying subject fatigue levels using the intention decline module.

Conclusions:

  • The proposed strategy effectively enhances patient initiative in rehabilitation training.
  • The system successfully prevents over-dependence on assistive control.
  • This approach offers a more personalized and effective stroke rehabilitation experience.