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Evaluation of Different Controllers for Sensing-Based Movement Intention Estimation and Safe Tracking in a Simulated

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Summary

This study developed an intelligent control framework for elbow exoskeletons, improving real-time movement intention tracking using High-Density Surface Electromyography (HD-sEMG) and LSTM networks. The sliding mode controller demonstrated superior accuracy and robustness for neurorehabilitation applications.

Keywords:
PID controldeep learningimpedance controlsliding mode controlupper limb assistive exoskeleton robot elbow orthosis

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Robotics
  • Neuroscience

Background:

  • Elbow exoskeleton control for rehabilitation is hindered by real-time intention estimation challenges.
  • Millions of patients annually require active rehabilitation for conditions like stroke and spinal cord injury.
  • Precise motion tracking in exoskeletons can restore independence and reduce healthcare costs.

Purpose of the Study:

  • To develop an intelligent control framework for elbow exoskeletons enabling precise and safe real-time motion intention tracking.
  • To estimate user movement intention from High-Density Surface Electromyography (HD-sEMG) signals.
  • To compare the performance of PID, impedance, and sliding mode controllers for exoskeleton control.

Main Methods:

  • Utilized a public HD-sEMG dataset from 12 healthy individuals across four isometric tasks and three effort levels.
  • Preprocessed EMG signals and extracted 13 time-domain features.
  • Trained an LSTM network for real-time estimation of desired joint angle, followed by controller implementation (PID, impedance, sliding mode).

Main Results:

  • The LSTM model achieved high accuracy (RMSE=0.630 Nm, R²=0.965, Pearson=0.985), a 47% R² improvement over traditional methods.
  • The sliding mode controller demonstrated superior performance with minimal tracking errors (avg. RMSE=0.21 Nm, R²≈0.96) across all tasks and effort levels.
  • The impedance controller showed good performance for flexion/extension but deteriorated with pronation/supination; PID control was unsuitable.

Conclusions:

  • The proposed LSTM-sliding mode hybrid architecture offers exceptional accuracy, robustness, and transparency for real-time intention monitoring.
  • This framework shows significant promise for advanced upper-limb exoskeletons in neurorehabilitation and assistive applications.
  • Further hardware validation is recommended for real-time clinical implementation.