用多传感器融合提升可穿戴设备手洗检测准确率
Multi-modal Atmospheric Sensing to Augment Wearable IMU-Based Hand Washing Detection
- 在惯性传感器基础上增加温湿度气压传感器
- 10人43次洗手数据验证，湿度变化显著提升识别
- 开源数据集+标注流程优化，适合医疗健康监测
洗手是个人卫生的重要环节，基于可穿戴IMU的洗手检测在医疗与职业领域具有应用价值。尽管该方法可行，但部分结果特异性较低，误报率高。本文提出一种新型开源原型设备，集成湿度、温度和气压传感器。构建了包含10名参与者和43次洗手事件的基准数据集，并评估了附加传感器的增益。视觉分析显示，洗手过程中相对湿度明显上升；机器学习分析表明，需进一步挖掘此类湿度模式所对应的特征。额外传感器在标注流程和模型中均具实用价值。
原文摘要 · Abstract (English)
Hand washing is a crucial part of personal hygiene. Hand washing detection is a relevant topic for wearable sensing with applications in the medical and professional fields. Hand washing detection can be used to aid workers in complying with hygiene rules. Hand washing detection using body-worn IMU-based sensor systems has been shown to be a feasible approach, although, for some reported results, the specificity of the detection was low, leading to a high rate of false positives. In this work, we present a novel, open-source prototype device that additionally includes a humidity, temperature, and barometric sensor. We contribute a benchmark dataset of 10 participants and 43 hand-washing events and perform an evaluation of the sensors' benefits. Added to that, we outline the usefulness of the additional sensor in both the annotation pipeline and the machine learning models. By visual inspection, we show that especially the humidity sensor registers a strong increase in the relative humidity during a hand-washing activity. A machine learning analysis of our data shows that distinct features benefiting from such relative humidity patterns remain to be identified.
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