用可穿戴设备数据对运动和环境因素的个性化心率反应进行建模
Achille Nazaret1, Sana Tonekaboni2, Gregory Darnell3
1Columbia University, New York, NY, USA.
NPJ digital medicine
|November 16, 2023
概括
这项研究引入了一种新的混合模型,将生理方程和神经网络结合起来,从可穿戴数据中准确评估心肺呼吸能力. 该模型提供个性化的健身见解,并高准确地预测运动期间的心率反应.
科学领域:
- 心血管生理学心血管生理学
- 机器学习在健康中的应用
- 可穿戴技术 数据分析 数据分析
背景情况:
- 心率对运动的反应是心肺健康和健康的关键指标.
- 现有的心率动态生理模型仅限于受控环境,并与现实世界可穿戴数据作斗争.
- 需要强大的个性化模型,适用于大规模的可穿戴数据集.
研究的目的:
- 开发一种混合生理和神经网络模型,以使用可穿戴数据进行个性化健身评估.
- 创建一个多维的体能表示,动态适应环境因素和炼强度.
- 为了验证模型在预测心率反应的准确性及其与既定健身指标的相关性.
主要方法:
- 开发了一个混合模型,将普通微分方程 (ODEs) 与神经网络集成在一起.
- 神经网络根据个性化表示,环境因素 (地形,天气) 和炼强度动态调整了ODE参数.
- 该模型在果心脏和运动研究中的7465名用户的270,707次炼的大数据集上进行了训练和验证.
主要成果:
- 混合模型准确地预测了未来的心率对运动强度的反应,平均误差为 6.1 BPM.
- 学习的健身表现与传统的心肺呼吸系统健身指标,如VO2max (解释变异0.81) 有着很强的相关性.
- 该模型证明了可解释性,量化了环境因素 (如温度和湿度) 对心率的影响.
结论:
- 将生理学ODEs与神经网络相结合,为健康应用提供了一种可解释,强大和富有表现力的方法.
- 这种混合模型有效地利用大规模的可穿戴数据进行个性化的健身评估和理解生理反应.
- 这种方法在推进远程健康监测和个性化运动指导方面具有重大潜力.
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