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    This study introduces a privacy-preserving system using Light Detection and Ranging (LiDAR) to monitor desk workers' leg positions, improving posture and reducing health risks. The system accurately identifies 15 leg positions, enhancing workplace ergonomics and safety.

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    Area of Science:

    • Ergonomics
    • Computer Science
    • Public Health

    Background:

    • Sedentary work increases the prevalence of poor posture and associated long-term health issues.
    • Current posture monitoring methods raise privacy and comfort concerns due to multiple sensors and cameras.
    • Proper leg positioning is crucial for maintaining good overall posture.

    Purpose of the Study:

    • To develop a privacy-preserving system for monitoring leg positions using Light Detection and Ranging (LiDAR).
    • To identify and alert workers about poor leg positions to improve posture and prevent health problems.
    • To provide posture statistics for safety engineers to identify at-risk workers.

    Main Methods:

    • A LiDAR-based system captures horizontal leg outlines at knee height.
    • A stacked autoencoder reproduces latent representations of leg outlines.
    • One-class and multi-class support vector machines identify 15 distinct leg positions and reject unrecognized ones.

    Main Results:

    • The system achieved over 98% accuracy in identifying leg positions on new participants.
    • The system effectively monitors leg positions while preserving user privacy and ergonomics.
    • Alerts for poor leg positions and posture statistics dashboards were generated.

    Conclusions:

    • The developed LiDAR system offers a promising solution for improving desk workers' posture.
    • This technology enhances workplace ergonomics and reduces long-term health risks associated with poor posture.
    • The privacy-preserving nature of the system addresses limitations of current monitoring methods.