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Related Experiment Videos

Sensor systems for lower limb functional electrical stimulation (FES) control.

R Williamson1, B J Andrews

  • 1Department of Biomedical Engineering, 10-102 Clinical Sciences Building, University of Alberta, Edmonton, Canada T6G 2J5.

Medical Engineering & Physics
|December 21, 2000
PubMed
Summary
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New wearable sensors accurately predict knee buckles in paraplegics. These advanced sensor systems offer real-time gait analysis and control for functional electrical stimulation (FES).

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Wearable Sensors

Background:

  • Gait impairment significantly affects individuals with paraplegia, necessitating advanced assistive technologies.
  • Accurate real-time monitoring of joint kinematics is crucial for developing effective control strategies for functional electrical stimulation (FES).
  • Existing sensor systems often face challenges with size, cabling, and real-time processing capabilities.

Purpose of the Study:

  • To design and evaluate novel wearable sensor systems for monitoring gait and joint angles in individuals with paraplegia.
  • To assess the systems' ability to predict incipient knee buckles and measure knee and hip flexion during sit-to-stand movements.
  • To determine the feasibility of using these sensor systems for real-time FES control.

Main Methods:

Related Experiment Videos

  • Developed two sensor systems with integrated accelerometers, magnetic sensors, gyroscopes, and strain gauges, placed at the belt/AFO or AFO/thigh.
  • Utilized a flexible wire bus to minimize cabling effects, with maximum cluster size of 14 cm³ and 75 g.
  • Employed a threshold method for knee buckle prediction and analytical/neural network methods for joint angle measurement.

Main Results:

  • Sensors detected five phases of normal gait with 40 ms resolution in able-bodied individuals.
  • Successfully predicted incipient knee buckles in a paraplegic individual with a minimum lead time of 30 ms.
  • Achieved accuracy of 3.2 degrees for knee flexion and 3.8 degrees for knee/hip flexion during sit-to-stand trials.

Conclusions:

  • The designed sensor systems demonstrate high accuracy and responsiveness for gait analysis and joint angle measurement.
  • The systems show potential for real-time prediction of gait deviations like knee buckles.
  • Feasibility for real-time control of FES in paraplegic individuals is suggested, paving the way for improved mobility solutions.