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Intention Detection Using Physical Sensors and Electromyogram for a Single Leg Knee Exoskeleton.

Dae-Hoon Moon1, Donghan Kim2, Young-Dae Hong3

  • 1Department of Electrical and Computer Engineering, Ajou University, Suwon 16499, Korea. anseogns56@ajou.ac.kr.

Sensors (Basel, Switzerland)
|October 17, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel knee exoskeleton with prismatic ankle movement to enhance wearer mobility. Combining length between knee and ankle sensors with electromyogram (EMG) signals significantly improves user intention detection accuracy and speed.

Keywords:
LBKA sensorelectromyogram (EMG)intention detectionknee exoskeletonneural networkself-alignmentsensor fusion

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Area of Science:

  • Biomedical Engineering
  • Robotics
  • Rehabilitation Technology

Background:

  • Knee exoskeletons can restrict wearer mobility, particularly during sitting.
  • Existing single-leg exoskeletons face challenges in detecting user intention due to lack of bilateral leg information.

Purpose of the Study:

  • To develop a knee exoskeleton with unimpeded mobility.
  • To enhance user intention detection for single-leg exoskeleton use.

Main Methods:

  • Designed a knee exoskeleton with a prismatic ankle joint to allow unrestricted movement.
  • Utilized length between knee center of rotation and ankle (LBKA) measurements for intention detection.
  • Integrated electromyogram (EMG) sensors with LBKA and encoder data.

Main Results:

  • The exoskeleton design prevents limitations in wearer movement, such as during sitting.
  • Intention detection accuracy reached 82.1% using LBKA and encoder sensors.
  • Adding EMG sensors increased detection accuracy to 92% and improved speed by 27.1 ms.

Conclusions:

  • The developed knee exoskeleton effectively maintains wearer mobility.
  • Multi-sensor fusion, including LBKA and EMG, significantly enhances intention detection for single-leg exoskeleton applications.