In the domain of non-contact biometrics and human activity recognition, the lack of a versatile, multimodal dataset poses a significant bottleneck. To address this, we introduce the Oulu Multi Sensing (OMuSense-23) dataset that includes biosignals obtained from a mmWave radar, and an RGB-D camera. The dataset features data from 50 individuals in three distinct poses -- standing, sitting, and lying down -- each featuring four specific breathing pattern activities: regular breathing, reading, guided breathing, and apnea, encompassing both typical situations (e.g., sitting with normal breathing) and critical conditions (e.g., lying down without breathing). In our work, we present a detailed overview of the OMuSense-23 dataset, detailing the data acquisition protocol, describing the process for each participant. In addition, we provide, a baseline evaluation of several data analysis tasks related to biometrics, breathing pattern recognition and pose identification. Our results achieve a pose identification accuracy of 87\% and breathing pattern activity recognition of 83\% using features extracted from biosignals. The OMuSense-23 dataset is publicly available as resource for other researchers and practitioners in the field.
@article{arxiv.2407.06137,
title = {OMuSense-23: A Multimodal Dataset for Contactless Breathing Pattern Recognition and Biometric Analysis},
author = {Manuel Lage Cañellas and Le Nguyen and Anirban Mukherjee and Constantino Álvarez Casado and Xiaoting Wu and Praneeth Susarla and Sasan Sharifipour and Dinesh B. Jayagopi and Miguel Bordallo López},
journal= {arXiv preprint arXiv:2407.06137},
year = {2024}
}