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基于系统识别的活动识别算法与惯性传感器.

Ali Nouriani, Alec Jonason, James Jean

    IEEE journal of biomedical and health informatics
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    PubMed
    概括

    这项研究引入了一种新的胸部佩戴传感器系统,用于准确识别人类活动. 该系统在识别日常活动时达到90%以上的准确性,有利于帕金森氏症.

    科学领域:

    • 生物医学工程 生物医学工程
    • 可穿戴技术可穿戴技术
    • 人类活动识别 人类活动识别

    背景情况:

    • 准确识别人类活动对于健康监测至关重要,特别是对于患有帕金森病 (PD) 等疾病的人来说.
    • 传统方法通常需要多个传感器或复杂的设置,限制实际应用.
    • 一个单一的,不引人注目的传感器为连续和可访问的监控提供了一个有希望的途径.

    研究的目的:

    • 开发和验证使用单个可穿戴惯性测量传感器的强大的活动识别系统.
    • 找出十种不同的日常活动,包括躺着,站着,坐着,腰和走路.
    • 评估该系统在临床和远程家庭监测环境中对帕金森病患者的有效性.

    主要方法:

    • 使用单个胸部安装的惯性测量传感器来获取数据.
    • 采用传输函数识别方法,根据特定活动的传感器信号规范来确定输入输出信号.
    • 应用Wiener过器,具有自动相关性和交叉相关性,用于使用训练数据识别转移函数.
    • 通过对已识别的传输函数的输入输出错误进行比较来实现实时活动识别.

    主要成果:

    • 开发的系统在识别十个不同的活动时,平均准确度超过了90%.
    • 在临床和家庭环境中,使用来自帕金森病患者的数据来验证性能.

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  • 该系统在实时活动识别方面被证明是有效的.

    • 结论:

    • 一个单一的可穿戴传感器系统可以实现高精度的人类活动识别.
    • 这项技术在监测帕金森病患者的活动水平和跌倒风险方面具有重大潜力.
    • 该系统有助于客观地评估姿势不稳定性,并实时识别高风险活动.