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基于智能手机的平衡评估使用机器学习

Marjan Nassajpour, Mustafa Shuqair, Amie Rosenfeld

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 5, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究使用智能手机加速度计和机器学习来准确评估平衡,为老年人和正在康复的人提供了一种方便的工具. 该方法提供客观的平衡分数,以改善在家和远程健康监测.

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    科学领域:

    • 生物医学工程 生物医学工程
    • 老年学是一门学科.
    • 康复科学 康复科学 康复科学

    背景情况:

    • 平衡评估对于预防老年人跌倒和康复至关重要.
    • 当前平衡测试可能耗时,需要专门的设备,并且缺乏客观的,持续的监控.
    • 需要客观和可访问的平衡评估工具,以便在临床和家庭广泛使用.

    研究的目的:

    • 开发和验证基于智能手机的方法,以客观地估计经过修改的临床测试的感官相互作用在平衡 (m-CTSIB) 的得分.
    • 调查机器学习算法在分析智能手机加速度计数据的有效性,以评估平衡.
    • 为家庭和远程平衡监控提供方便和可访问的工具.

    主要方法:

    • 利用智能手机加速度计数据和机器学习 (XGBOOST) 来估计m-CTSIB分数.
    • 使用智能手机传感器和地面真相的强力板系统,从28名参与者 (21-88岁) 收集了同时的数据.
    • 验证了算法的性能与黄金标准的力板测量.

    主要成果:

    • 该XGBOOST算法实现了高相关性 (0.92) 与地面真实性m-CTSIB得分从力板数据获得.
    • 证明了使用智能手机传感器进行客观平衡评估的可行性.
    • 开发的方法证明在量化平衡参数方面可靠和准确.

    结论:

    • 使用机器学习进行基于智能手机的平衡评估是一种可靠和客观的方法.
    • 这项技术为在家和远程健康监测提供了一个有前途的解决方案.
    • 这些发现支持将这种方法纳入远程医疗,以改善患者的护理和生活质量.