HARMES: A Multi-Modal Dataset for Wearable Human Activity Recognition with Motion, Environmental Sensing and Sound

TL;DR

HARMES is a novel multi-modal wearable dataset combining motion, environmental, and audio sensors, enabling improved activity recognition for daily living activities through extensive benchmarking and analysis.

Contribution

This work introduces HARMES, the first dataset combining motion, environmental, and audio data for wearable human activity recognition, with extensive benchmarks and analysis.

Findings

Modality contributions vary by activity and provide complementary information.

HARMES is nearly six times larger than previous wrist-inertial-acoustic datasets.

Cross-subject generalization improves with multi-modal data.

Abstract

With each sensing modality exhibiting inherent strengths and limitations, multi-modal approaches for wearable Human Activity Recognition (HAR) are becoming increasingly relevant -- particularly for recognizing Activities of Daily Living (ADLs), where individual modalities often produce ambiguous signals for similar or complex activities. This work introduces HARMES, a multi-modal wearable dataset combining three wrist-recorded modalities: motion sensing via an Inertial Measurement Unit (IMU), atmospheric environmental sensors (humidity, temperature, and pressure), and audio. Collected from 20 participants performing household activities in their own homes, HARMES totals over 80 hours of recorded data, with approximately three hours of labeled activity data per participant across 15 ADL classes. To the best of our knowledge, HARMES is the first dataset to combine this particular sensor trio, and it is nearly six times larger than the previously largest wrist-inertial-acoustic HAR dataset. In an extensive benchmark, we evaluate cross-subject generalization and conduct an ablation study revealing that modality contributions are activity-dependent and can provide complementary value, particularly for activities that are ambiguous from motion data alone. HARMES is freely available at Zenodo, alongside example code for loading the dataset and training models on GitHub.