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For the purpose of monitoring health conditions of employees in physically demanding professions, particularly those in emergency services and the military, a system utilizing two stereoscopic RGB-D cameras, Intel® RealSense™ D455, was designed. A pilot measurement was conducted using four exercises: Squats, Cardiopulmonary Resuscitation, Crawling on All Fours, and McGill’s Anterior Trunk Flexor Test. MediaPipe from Google LLC was used for anatomical landmark detection, supplemented by pixel-to-point deprojection into the depth map. The results suggest that the system is capable of assessing even complex exercises and movements. Measurement errors mainly arise from incorrect pixel-to-point deprojection of the RGB image into the depth map, affecting the positions of individual landmarks. The results primarily show the visibility values of individual landmarks for the given exercises and camera positions in frontal and three-quarters views. This information could potentially be used to correct pixel-to-point deprojection errors.

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RGB-D Motion Capture System for Monitoring Health Conditions of Employees in Physically Demanding Professions

  • 会议论文

  • 发表日期：

    2025年

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  • Petr Volf,
  • Jan Hejda,
  • Marek Sokol,
  • Lýdie Leová,
  • Yi-Jia Lin,
  • Tommy Sugiarto,
  • Patrik Kutílek

摘要

For the purpose of monitoring health conditions of employees in physically demanding professions, particularly those in emergency services and the military, a system utilizing two stereoscopic RGB-D cameras, Intel® RealSense™ D455, was designed. A pilot measurement was conducted using four exercises: Squats, Cardiopulmonary Resuscitation, Crawling on All Fours, and McGill’s Anterior Trunk Flexor Test. MediaPipe from Google LLC was used for anatomical landmark detection, supplemented by pixel-to-point deprojection into the depth map. The results suggest that the system is capable of assessing even complex exercises and movements. Measurement errors mainly arise from incorrect pixel-to-point deprojection of the RGB image into the depth map, affecting the positions of individual landmarks. The results primarily show the visibility values of individual landmarks for the given exercises and camera positions in frontal and three-quarters views. This information could potentially be used to correct pixel-to-point deprojection errors.