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Exploratory Research in Computer Vision, Optical Flow & Contactless Physiological Monitoring
Exploring novel approaches to assess breathing patterns by integrating video-based computer vision analysis with contactless sensor signals.
Traditional respiratory monitoring often relies on sensors attached directly to the body, which can be obtrusive and disrupt natural sleep. Video-based methods, combined with advanced signal processing from ambient sources, offer a completely non-invasive alternative.
To develop robust techniques utilizing video streams and supplemental sensors to accurately measure respiratory rate, tidal volume variations, and abnormal breathing patterns without direct skin contact.
Employing computer vision algorithms, optical flow tracking of chest-wall displacement, and machine learning to extract respiratory waveforms from video, integrating data from other non-invasive sensors for enhanced clinical reliability.
Initial tests indicate that combining video analysis with additional sensor signals significantly enhances breathing pattern detection and movement artifact rejection, providing a viable contactless alternative for sleep and respiratory monitoring.
This exploratory research investigates multi-modal fusion between video analysis (remote photoplethysmography and optical flow) and contactless mattress sensors.