Multi-modal Atmospheric Sensing to Augment Wearable IMU-Based Hand Washing Detection
This work addresses hand washing detection for workers in medical and professional fields to improve hygiene compliance, but it is incremental as it builds on existing IMU-based methods by adding sensors without demonstrating clear performance gains.
The paper tackled the problem of low specificity in wearable IMU-based hand washing detection by introducing a multi-modal sensor prototype with humidity, temperature, and barometric sensors, resulting in a benchmark dataset of 10 participants and 43 hand-washing events, but machine learning analysis indicated that distinct features from humidity patterns remain to be identified.
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.