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相关概念视频

Muscle Recovery and Fatigue01:24

Muscle Recovery and Fatigue

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Muscle fatigue refers to the decline in a muscle's ability to maintain the force of contraction after prolonged activity. It primarily stems from changes within muscle fibers. Even before experiencing muscle fatigue, one may feel tired and have the urge to stop the activity. This response, known as central fatigue, occurs due to changes in the central nervous system, namely the brain and spinal cord. While there is no single mechanism that induces fatigue, it may serve as a protective...
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相关实验视频

Updated: May 24, 2025

Assessing the Accuracy of Fitness Smartwatch Data for Cardiovascular and Physical Activity Monitoring: A Validation Study in Digital Health
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Published on: February 21, 2025

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使用可穿戴设备数据的机器学习方法检测疲劳.

Karthik Gopalakrishnan, Zhi Li, Mehdi Boukhechba

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |March 3, 2025
    PubMed
    概括
    此摘要是机器生成的。

    使用可穿戴加速度计的客观疲劳评估显示,对于全身性红斑狼 (SLE) 和Sjögren有希望.

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    科学领域:

    • 生物医学工程 生物医学工程
    • 数字健康数字健康
    • 可穿戴技术可穿戴技术

    背景情况:

    • 慢性疲劳是免疫媒介炎症疾病的主要症状,如系统性红斑狼 (SLE) 和Sjögren病 (SjD).
    • 目前的疲劳评估主要使用主观的自我报告问卷.
    • 需要客观的,被动的措施来补充主观的数据,并提供对疲劳对日常生活的影响更深入的见解.

    研究的目的:

    • 客观地估计SLE和SjD患者的疲劳,使用加速度计数据和机器学习.
    • 为了比较客观的疲劳指标与人口统计学上匹配的健康志愿者 (HNV).
    • 探索可穿戴设备在开发疲劳数字生物标志物方面的潜力.

    主要方法:

    • 在24周内收集了96名参与者的加速度计数据,使用ActiGraph中心点洞察观察在自由生活环境中.
    • 从原始加速度计数据中提取基于活动,基于睡眠和昼夜节律的特征.
    • 训练了一种机器学习分类器来区分日常疲劳状态 (疲劳/不疲劳) 并使用交叉验证进行评估.

    主要成果:

    • 机器学习模型有效地区分了疲劳状态与比随机性能更好.
    • 单独的加速度计特征在疲劳检测中与单独的基线参与者特征相似.
    • 模型性能 (ROC-AUC) 在单个队列中从0.440.70不等,在组合时改善到0.760.83.

    结论:

    • 可穿戴设备显示出开发疲劳的客观数字生物标志物的巨大潜力.
    • 这些数字生物标志物可以帮助评估各种治疗领域的治疗反应.
    • 客观的疲劳监测可以为主观的患者报告提供有价值的补充数据.