使用单点加速度计对人类行走环境的分类
Loubna Baroudi1, Kira Barton2,3, Stephen M Cain4
1Mechanical Engineering, University of Michigan, Ann Arbor, 48109, USA. lbaroudi@umich.edu.
Scientific reports
|February 6, 2024
概括
这项研究开发了一种机器学习模型,使用加速度计数据区分室内和室外步行. 该模型准确地识别了步行上下文,揭示了户外步行比室内步行更快,更长.
科学领域:
- 生物力学 生物力学
- 机器学习 机器学习
- 可穿戴技术可穿戴技术
背景情况:
- 现实世界的行走数据提供了有价值的移动性见解,但由于日常变化,往往难以解释.
- 整合上下文信息对于从运动模式中提取有意义的数据至关重要.
- 区分室内和室外步行对于准确的移动性分析至关重要.
研究的目的:
- 开发和验证一种机器学习算法,用于使用加速度计数据对室内和室外散步进行分类.
- 为了利用走路特征和上下文之间的关系来改进数据解释.
- 描述室内和室外环境之间行走模式的差异.
主要方法:
- 在一周内从参与者的大腿上收集了加速度计数据.
- 使用GPS和自我报告数据隔离和标记步行节目.
- 经过训练和验证的随机森林和集体支持矢量机器模型,使用一个离开一个参与者的交叉验证方案.
- 在选择的模型中实现了高精度 (0.941),F1得分 (0.963) 和AUROC (0.931).
- 将验证的模型应用于单独的数据集,以标记室内和室外散步.
主要成果:
- 机器学习模型在区分室内和室外散步方面取得了很高的性能.
- 与会者在室内相比,在户外表现出明显更快,更长,更连续的步行模式.
- 仅仅是移动数据,当上下文化时,可以提供关于环境因素的准确信息.
结论:
- 通过机器学习将现实世界的运动数据置于上下文中,可以增强解释和理解.
- 开发的算法准确地区分了室内和室外步行,提供了有价值的上下文见解.
- 基于环境的行走行为差异的特征可以加深我们对人类移动性和健康的理解.
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