通过分析和实践方法减轻 ambulatory wrist-worn wearable监测中的数据质量挑战
Jonas Van Der Donckt1, Nicolas Vandenbussche2,3, Jeroen Van Der Donckt4
1IDLab, Ghent University - Imec, Technologiepark-Zwijnaarde, 9052, Ghent, Belgium. Jonvdrdo.VanDerDonckt@UGent.be.
Scientific reports
|July 30, 2024
概括
这项研究解决了用于慢性疾病管理的可穿戴数据分析方面的挑战. 它引入了提高数据准确性和可靠性的方法,以改善患者监测和结果.
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
- 数据科学数据科学数据科学
背景情况:
- 慢性疾病管理需要持续监测患者以获得最佳结果.
- 可穿戴技术提供客观,连续的数据,但面临分析挑战.
- 现有的方法在数据错误,不磨损期和文物方面扎.
研究的目的:
- 探索和解决用于慢性疾病管理的可穿戴传感器数据的数据分析挑战.
- 引入实际的对策,以提高可穿戴数据的质量和可靠性.
- 提供可复制的方法来验证数据处理管道和评估数据变化.
主要方法:
- 为了分析,利用了两个真实世界的数据集 (mBrain21,ETRI lifelog2020).
- 开发了参与者合规可视化和偏见评估问卷.
- 实施了针对非磨损期检测的优化管道和使用tsflex和Plotly-Resampler的以可视化为导向的验证方法.
- 应用了引导方法来评估缺少数据的特征可变性.
主要成果:
- 对于常见的可穿戴数据问题,展示了实际的对策.
- 有效使用可扩展的可视化工具验证的处理管道.
- 在部分缺失数据段的存在下,量化特征变化.
- 为增强可重复性提供开放访问代码.
结论:
- 开发了可操作的方法,以改善用于慢性疾病管理的可穿戴数据收集和分析.
- 拟议的方法提高了可穿戴设备衍生的健康数据的可靠性和透明度.
- 这项工作促进了可穿戴技术在纵向患者护理中的有效整合.
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