# A Real-World Dataset for detecting Handwashing in daily Life using Wrist Motion Data from Wearables

## TL;DR

This paper introduces OCDetect, a real-world dataset for detecting handwashing using wearable motion data, which could help support individuals with OCD.

## Contribution

The paper presents the first unscripted dataset of compulsive handwashing recorded in real-life settings using wearable sensors.

## Key findings

- The OCDetect Dataset includes 3000 handwashing events from 22 participants over 28 days.
- The dataset achieves an F1 score of 0.77 for detecting handwashing from background activities on unseen participants.

## Abstract

Handwashing detection is a relevant research topic with applications in healthcare and professional environments. While usually related to hygiene improvement, handwashing detection could also be used to support individuals with obsessive-compulsive disorder (OCD). For these individuals, compulsive, long, and frequent handwashing has a negative impact. An automated system could spot compulsive handwashing in real-time and augment the therapy process. No activity recognition datasets containing in-the-wild-recorded compulsive handwashing are available. With this work, we present the OCDetect Dataset, the first dataset with unscripted, compulsive handwashing. It contains recordings from inertial measurement units (IMUs) of 22 participants over 28 days, with 3000 recorded hand washes. For each hand wash, we supply its user-annotated kind (compulsive / routine). We provide an overview of related datasets and describe the recording, cleaning, labeling, and final features of our dataset. We reach a maximum F1 score of 0.77 (avg.: 0.33, chance level: 0.03) when spotting handwashing from all background activities on unseen participants. Our dataset and code for the reproduction of our results are publicly available.