Multi-modal Atmospheric Sensing to Augment Wearable IMU-Based Hand Washing Detection

TL;DR

This paper introduces a multi-modal sensor device for improved hand washing detection, combining IMU data with humidity, temperature, and barometric sensors, and provides a benchmark dataset for future research.

Contribution

We present a novel open-source device with additional sensors and a benchmark dataset, demonstrating the potential benefits of multi-modal sensing for hand washing detection accuracy.

Findings

Humidity sensor shows increased readings during hand washing

Additional sensors improve annotation and machine learning models

Distinct humidity features for detection remain to be identified

Abstract

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.