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

arXiv: https://arxiv.org/abs/2410.03549 · HTML: https://mlsanity.com/paper/2410.03549/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 21 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 1 of 1)

- [Automated Quality Assessment of Hand Washing Using Deep Learning](https://mlsanity.com/paper/2011.11383/) (2020)

## Built on by (most cited first, 3 of 3)

- [HARMES: A Multi-Modal Dataset for Wearable Human Activity Recognition with Motion, Environmental Sensing and Sound](https://mlsanity.com/paper/2605.02596/) (2026)
- [Enhancing Wearable Tap Water Audio Detection through Subclass Annotation in the HD-Epic Dataset](https://mlsanity.com/paper/2505.20788/) (2025)
- [A Comparison of Fusion Techniques for Multi-Modal Human Activity Recognition on the HARMES Dataset](https://mlsanity.com/paper/2606.27886/) (2026)

## Abstract (arXiv)

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.