# Enhancing Wearable Tap Water Audio Detection through Subclass Annotation in the HD-Epic Dataset

arXiv: https://arxiv.org/abs/2505.20788 · HTML: https://mlsanity.com/paper/2505.20788/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 35 references that are in the map.
- First posted on arXiv 27 May 2025.

## Builds on (most cited first, 10 of 10)

- [Rescaling Egocentric Vision](https://mlsanity.com/paper/2006.13256/) (2020)
- [FSD50K: An Open Dataset of Human-Labeled Sound Events](https://mlsanity.com/paper/2010.00475/) (2020)
- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [HD-EPIC: A Highly-Detailed Egocentric Video Dataset](https://mlsanity.com/paper/2502.04144/) (2025)
- [Deep Learning in Human Activity Recognition with Wearable Sensors: A Review on Advances](https://mlsanity.com/paper/2111.00418/) (2021)
- [Epic-Sounds: A Large-scale Dataset of Actions That Sound](https://mlsanity.com/paper/2302.00646/) (2023)
- [Audio-Adaptive Activity Recognition Across Video Domains](https://mlsanity.com/paper/2203.14240/) (2022)
- [Audio-Based Activities of Daily Living (ADL) Recognition with Large-Scale Acoustic Embeddings from Online Videos](https://mlsanity.com/paper/1810.08691/) (2018)
- [Multi-modal Atmospheric Sensing to Augment Wearable IMU-Based Hand Washing Detection](https://mlsanity.com/paper/2410.03549/) (2024)
- [User Environment Detection with Acoustic Sensors Embedded on Mobile Devices for the Recognition of Activities of Daily Living](https://mlsanity.com/paper/1711.00124/) (2017)

## Abstract (arXiv)

Wearable human activity recognition has been shown to benefit from the inclusion of acoustic data, as the sounds around a person often contain valuable context. However, due to privacy concerns, it is usually not ethically feasible to record and save microphone data from the device, since the audio could, for instance, also contain private conversations. Rather, the data should be processed locally, which in turn requires processing power and consumes energy on the wearable device. One special use case of contextual information that can be utilized to augment special tasks in human activity recognition is water flow detection, which can, e.g., be used to aid wearable hand washing detection. We created a new label called tap water for the recently released HD-Epic data set, creating 717 hand-labeled annotations of tap water flow, based on existing annotations of the water class. We analyzed the relation of tap water and water in the dataset and additionally trained and evaluated two lightweight classifiers to evaluate the newly added label class, showing that the new class can be learned more easily.