  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.
