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A Deep Learning-Based Chair System That Detects Sitting Posture.

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    This study introduces an intelligent sitting posture detection system using depth cameras and AI on a Raspberry Pi. It accurately identifies poor posture for health warnings, offering a comfortable and portable solution.

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    Area of Science:

    • Biomedical Engineering
    • Artificial Intelligence
    • Human-Computer Interaction

    Background:

    • Poor sitting posture can cause musculoskeletal issues like muscle soreness and spinal alignment problems.
    • Existing posture detection systems often rely on cumbersome or easily damaged sensors.
    • The need for a comfortable, accurate, and privacy-preserving posture monitoring solution is critical.

    Purpose of the Study:

    • To develop an intelligent sitting posture detection system using readily available hardware.
    • To enable real-time monitoring and alerting of poor sitting posture.
    • To provide a comfortable, portable, and privacy-conscious solution for posture analysis.

    Main Methods:

    • Utilized two depth cameras mounted on a chair to capture user posture data.
    • Employed an artificial intelligence (AI) model (lightweight EfficientNet) on an embedded Raspberry Pi for posture recognition.
    • Integrated Bluetooth for transmitting posture data to a smartphone application for display and recording.

    Main Results:

    • Achieved a high accuracy of 99.71% in sitting posture detection.
    • Demonstrated a rapid execution speed of approximately one posture result per second.
    • The system proved to be power-efficient, portable, and privacy-preserving due to edge computing.

    Conclusions:

    • The proposed system effectively detects sitting posture with high accuracy and speed.
    • The chair-mounted depth camera approach enhances user comfort and system durability.
    • Edge computing on a Raspberry Pi offers a practical, power-saving, and private solution for posture monitoring.