# OMuSense-23: A Multimodal Dataset for Contactless Breathing Pattern Recognition and Biometric Analysis

arXiv: https://arxiv.org/abs/2407.06137 · HTML: https://mlsanity.com/paper/2407.06137/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 51 references that are in the map.
- First posted on arXiv 22 May 2024.

## Builds on (most cited first, 8 of 8)

- [Deep Learning for Sensor-based Activity Recognition: A Survey](https://mlsanity.com/paper/1707.03502/) (2017)
- [MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing](https://mlsanity.com/paper/2305.10345/) (2023)
- [A Reproducible Study on Remote Heart Rate Measurement](https://mlsanity.com/paper/1709.00962/) (2017)
- [Face2PPG: An unsupervised pipeline for blood volume pulse extraction from faces](https://mlsanity.com/paper/2202.04101/) (2022)
- [OPERAnet: A Multimodal Activity Recognition Dataset Acquired from Radio Frequency and Vision-based Sensors](https://mlsanity.com/paper/2110.04239/) (2021)
- [A Scoping Review of Deep Learning Methods for Photoplethysmography Data](https://mlsanity.com/paper/2401.12783/) (2024)
- [Depression Recognition using Remote Photoplethysmography from Facial Videos](https://mlsanity.com/paper/2206.04399/) (2022)
- [CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensors](https://mlsanity.com/paper/2202.03283/) (2022)

## Built on by (most cited first, 3 of 3)

- [Quality-Aware Framework for Video-Derived Respiratory Signals](https://mlsanity.com/paper/2512.14093/) (2025)
- [Phenomenon-Graph JEPA: Label-Efficient Representation Learning for Contactless Cardiorespiratory Sensing](https://mlsanity.com/paper/2609.32928/) (2026)
- [Learning Multimodal Sensor Fusion with JEPA Representations for Breathing Activity and Pose Recognition](https://mlsanity.com/paper/2610.15891/) (2026)

## Abstract (arXiv)

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.