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一个基于深度学习的椅子系统,可以检测坐姿.

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    科学领域:

    • 生物医学工程 生物医学工程
    • 人工智能的人工智能
    • 人与计算机的交互

    背景情况:

    • 不良的坐姿可以导致肌肉骨问题,如肌肉疼痛和脊柱对齐问题.
    • 现有的姿势检测系统通常依赖于繁或容易损坏的传感器.
    • 需要一个舒适,准确和保护隐私的姿势监控解决方案至关重要.

    研究的目的:

    • 使用现有硬件开发智能坐姿检测系统.
    • 为了实时监控和警报不良坐姿.
    • 提供一个舒适的,便携式的,和隐私意识的解决方案,用于姿势分析.

    主要方法:

    • 利用两个安装在椅子上的深度摄像头来捕获用户姿势数据.
    • 在嵌入式Raspberry Pi上使用人工智能 (AI) 模型 (轻量级的EfficientNet) 进行姿势识别.
    • 集成蓝牙用于将姿势数据传输到智能手机应用程序以显示和记录.

    主要成果:

    • 在坐姿检测中达到99.71%的高精度.
    • 演示了每秒大约一个姿势结果的快速执行速度.
    • 由于边缘计算,该系统被证明是节能,便携和保护隐私的.

    结论:

    • 拟议的系统有效地检测高精度和速度的坐姿.
    • 安装在椅子上的深度摄像头方法提高了用户的舒适度和系统的耐用性.
    • 在Raspberry Pi上的边缘计算为姿势监控提供了实用,节能和私密的解决方案.