对流体摄入监测系统的流体摄入评估方法的分析
Chia-Yeh Hsieh1, Hsiang-Yun Huang2, Chia-Tai Chan2
1Bachelor's Program in Medical Informatics and Innovative Applications, Fu Jen Catholic University, New Taipei City 24205, Taiwan.
Biosensors
|January 22, 2024
概括
精确的液体摄入量监测对于水分的补充至关重要. 这项研究发现,分析来自手腕设备和智能容器的惯性传感器数据,特别是考虑到口饮大小,可以显著提高评估液体消耗的准确性.
科学领域:
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
- 健康监测 卫生监测
背景情况:
- 有效的液体摄入量监测对于保持足够的水分和管理个人液体消耗行为至关重要.
- 现有的基于惯性传感器的液体摄入量评估方法主要分为手腕穿戴和智能容器方法.
- 了解这些方法的性能差异和影响因素对于开发准确的水分跟踪系统至关重要.
研究的目的:
- 用惯性传感器对基于手腕的和基于智能容器的液体摄入量评估方法进行比较分析.
- 评估手势识别和体积估计对这两种方法的性能的影响.
- 为了研究液体容器填充水位和个人口饮大小对液体摄入评估准确性的影响.
主要方法:
- 采用惯性传感器来获取手腕式和智能容器设置中的数据.
- 实施后处理技术,以提高手势识别的准确性.
- 开发并与一般支持向量回归 (SVR) 模型进行比较,以估计体积.
主要成果:
- 实现了高的手势识别准确度:92.89%的手腕穿戴和91.8%的智能容器方法后处理.
- 证明了依赖sip大小的模型在两种方法中在体积估计方面显著优于一般的SVR模型.
- 观察到平均绝对百分比误差 (MAPE),平均绝对偏差 (MAD) 和根平均平方误差 (RMSE) 的实质性改善 (超过50%),除了小口饮大小的MAPE外.
结论:
- 基于惯性传感器的液体摄入量评估可以通过手腕穿戴和智能容器方法实现.
- 准确的手势识别和精确的饮水量估计是有效监测液体摄入量的关键因素.
- 将吸水量信息纳入模型显著提高了体积估计的准确性,为更个性化的水分跟踪铺平了道路.
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