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A SEMG-angle model based on HMM for human robot interaction.

Yanyan Chen1,2, Le Liang1,2, Maochuan Wu1

  • 1Lianyungang Jari deepsoft Technology Co., LTD, Lianyungang, Jiangsu 222000, China.

Technology and Health Care : Official Journal of the European Society for Engineering and Medicine
|May 3, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a Hidden Markov Model (HMM) for processing surface electromyography (SEMG) signals in exoskeleton robot human-robot interaction (HRI). The HMM approach demonstrated superior accuracy and efficiency compared to traditional neural network models.

Keywords:
BP neural networkHMMPCARBF neural networkSEMG-angle modelfeature extraction

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Area of Science:

  • Rehabilitation Robotics
  • Human-Robot Interaction (HRI)
  • Biomedical Signal Processing

Background:

  • Developing intuitive Human-Robot Interaction (HRI) systems is crucial for effective exoskeleton robot rehabilitation.
  • Integrating surface electromyography (SEMG) signals enhances the naturalness of HRI.

Purpose of the Study:

  • To propose a SEMG-angle model utilizing the Hidden Markov Model (HMM) for improved HRI in exoskeleton systems.
  • To compare the performance of HMM-based models against Back Propagation (BP) and Radial Basis Function (RBF) neural networks for SEMG signal analysis.

Main Methods:

  • Principal Component Analysis (PCA) was employed for feature extraction and dimensionality reduction of SEMG signals.
  • Developed and evaluated SEMG-angle models using HMM, BP neural networks, and RBF neural networks.

Main Results:

  • The HMM-based model achieved a higher average accuracy (93.063%) compared to BP (88.180%) and RBF (88.752%) neural networks.
  • HMM demonstrated lower computational complexity while maintaining superior modeling performance.

Conclusions:

  • SEMG signals exhibit properties suitable for stochastic process modeling, aligning well with HMM structures.
  • HMM provides a robust and efficient method for classifying and modeling SEMG signals, outperforming conventional BP and RBF networks in this application.