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

TL;DR

The paper introduces OMuSense-23, a comprehensive multimodal dataset with biosignals from radar and RGB-D camera for contactless biometric and breathing pattern recognition, enabling new research in non-contact human activity analysis.

Contribution

It provides a new, publicly available multimodal dataset with detailed protocols and baseline evaluations for biometric and breathing pattern recognition tasks.

Findings

Achieved 87% accuracy in pose identification.

Achieved 83% accuracy in breathing pattern recognition.

Dataset includes diverse activities and poses for robust analysis.

Abstract

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.