OMuSense-23: A Multimodal dataset for contactless breathing pattern recognition and biometric analysis

OMuSense-23 is a multimodal dataset for non-contact biometric and breathing analysis. This database comprises RGBD and mmWave radar data collected from 50 participants. The data capture process involves participants engaging in four breathing pattern activities (normal breathing, reading, guided breathing, and breath holding to simulate apnea) each one performed in three distinct static poses: standing (A), sitting (B), and lying down (C). For citations please refer to the paper: Manuel Lage Cañellas, Le Nguyen, Anirban Mukherjee, Constantino Álvarez Casado, Xiaoting Wu, Nhi Nguyen, Praneeth Susarla, Sasan Sharifipour, Dinesh B. Jayagopi, Miguel Bordallo López, "OmuSense-23: A Multimodal Dataset For Contactless Breathing Pattern Recognition And Biometric Analysis", arXiv:2407.06137, 2024