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Lower Body Joint Angle Prediction Using Machine Learning and Applied Biomechanical Inverse Dynamics.

Zachary Choffin1, Nathan Jeong1, Michael Callihan2

  • 1Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA.

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Summary

This study developed a novel footwear sensor to predict lower body joint angles, aiding in the prevention of workplace injuries. The system accurately monitored angles from ankle to the lumbosacral joint (L5S1).

Keywords:
foot sensorinertial measurement unitjoint angle detectionlower bodymachine learning

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Area of Science:

  • Biomechanics
  • Wearable Technology
  • Occupational Health

Background:

  • Extreme joint angles in the lower body elevate injury risk, leading to chronic pain and economic losses in occupational settings.
  • Workplace injuries affecting lower body joints are prevalent, necessitating effective prevention and monitoring strategies.

Purpose of the Study:

  • To predict lower body joint angles (ankle to lumbosacral joint L5S1) using plantar pressure measurements from footwear sensors.
  • To validate the accuracy of the developed sensor system against a motion capture system for joint angle prediction.

Main Methods:

  • A custom footwear sensor with six force-sensing resistors (FSR) and Bluetooth LE was designed.
  • Gaussian Process Regression (GPR) was employed to model and predict joint angles.
  • An Xsens motion capture system served as the ground truth for 3D joint angle validation.

Main Results:

  • The footwear sensor system demonstrated promising accuracy with low root mean square error (RMSE) for all measured joints.
  • The lumbosacral joint (L5S1) angle was predicted with an RMSE of 0.21° (X-axis) and 0.22° (Y-axis).

Conclusions:

  • The proposed plantar pressure sensor system effectively predicts and monitors lower body joint angles.
  • This technology offers potential for injury prevention and enhanced training programs for occupational workers.