OMuSense-23: A Multimodal Dataset for Contactless Breathing Pattern Recognition and Biometric Analysis
<|reference_start|>OMuSense-23: A Multimodal Dataset for Contactless Breathing Pattern Recognition and Biometric Analysis: 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.<|reference_end|>
@article{cañellas2024omusense-23:,
    title={OMuSense-23: A Multimodal Dataset for Contactless Breathing Pattern
  Recognition and Biometric Analysis},
    author={Manuel Lage Ca~nellas, Le Nguyen, Anirban Mukherjee, Constantino
  'Alvarez Casado, Xiaoting Wu, Praneeth Susarla, Sasan Sharifipour, Dinesh B.
  Jayagopi, Miguel Bordallo L'opez},
    journal={arXiv preprint arXiv:2407.06137},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06137},
    primaryClass={cs.CV}
}