开放代谢:使用在口袋中佩戴的智能手机估计能源支出
Haedo Cho1, Patrick Slade2,3
1John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA.
Communications engineering
|February 6, 2026
概括
使用智能手机的腿部运动,OpenMetabolics准确地估计了能源消耗,与现有工具相比,将错误减少了一半. 这种可访问的,开源的监视器有助于大规模的体育活动研究和健康结果研究.
科学领域:
- 生物医学工程 生物医学工程
- 医疗信息学 医疗信息学
- 可穿戴技术可穿戴技术
背景情况:
- 身体不活动是全球主要的死亡原因,需要客观的身体活动测量工具.
- 目前的活动监测器在准确性和适合大规模研究方面存在局限性.
- 了解活动与健康结果之间的关系需要改进监测方法.
研究的目的:
- 开发和验证使用智能手机技术准确,可访问的体育活动监测器.
- 改进日常活动中对能源消耗的估计.
- 促进对体力活动和健康结果的大规模研究.
主要方法:
- 开发了OpenMetabolics,这是一个生物机械信息系统,使用口袋中的智能手机测量腿部运动.
- 采用数据驱动的机器学习模型,将腿部肌肉活动与能量消耗相关联.
- 实现了口袋运动工件校正模型,用于在不同类型的服装上进行准确的监控.
主要成果:
- 在实际活动中,OpenMetabolics在能源支出估计中实现了18%的累积错误,大约是现有工具的两倍.
- 口袋运动工件校正模型确保了准确的监测,无论穿着什么衣服.
- 一项为期一周的研究揭示了个人和人口水平的活动模式.
结论:
- 开放代谢在准确和可访问的身体活动监测方面取得了重大进展.
- 该系统的开源性质促进了研究和公共卫生倡议的广泛采用.
- 这种工具可以支持大规模的研究,将身体活动与健康结果联系起来,为政策提供信息,并指导干预.
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