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Joint angle estimation during shoulder abduction exercise using contactless technology.

Ali Barzegar Khanghah1,2, Geoff Fernie3,4,5, Atena Roshan Fekr3,4

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Summary

This study introduces a novel marker-less tele-rehabilitation system using LiDAR and skeleton tracking for accurate 3D joint angle analysis. The depth-based approach enhances remote movement assessment, improving tele-rehab capabilities.

Keywords:
CalibrationMarker-less joint angle estimationMotion captureSystem validationTele-rehabilitation

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Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Computer Vision

Background:

  • Tele-rehabilitation (tele-rehab) leverages communication technologies for remote healthcare delivery, gaining importance during the COVID-19 pandemic.
  • Existing tele-rehab systems often lack comprehensive movement analysis capabilities.
  • A novel approach integrating depth sensing and skeleton tracking is proposed to address this limitation.

Purpose of the Study:

  • To develop and validate a marker-less system for precise 3D joint angle assessment in tele-rehabilitation.
  • To evaluate the performance of LiDAR depth technology combined with skeleton tracking algorithms (Cubemos, Mediapipe).
  • To enhance the accuracy of joint angle calculations through personalized calibration techniques.

Main Methods:

  • Collected depth videos using an LiDAR camera and motion data via a Motion Capture (Mocap) system from 14 participants performing shoulder abduction exercises.
  • Integrated LiDAR data with Cubemos and Mediapipe skeleton tracking frameworks to estimate 3D joint angles.
  • Validated the system by comparing estimated joint angles with Mocap ground truth, applying various regression models for calibration.

Main Results:

  • The Cubemos framework demonstrated higher accuracy in joint angle estimation compared to Mediapipe.
  • The proposed system showed strong correlation with Mocap, despite minor deviations due to noise.
  • System precision decreased with increased distance from the LiDAR sensor, but calibration significantly improved accuracy, with linear regression models performing best.

Conclusions:

  • The marker-less, depth-based system effectively tracks body joints and upper-limb angles for tele-rehabilitation.
  • The system achieved robust correlation with Mocap, indicating its potential for precise joint tracking.
  • LiDAR's depth sensing enables accurate angle computation beyond the scope of traditional RGB cameras, enhancing tele-rehab applications.