Long Distance Vital Signs Monitoring with Person Identification for Smart Home Solutions.

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Abstract

Imaging photoplethysmography has already been proved to be successful in short distance (below 1m). However, most of the real-life use cases of measuring vital signs require the system to work at longer distances, to be both more reliable and convenient for the user. The possible scenarios that system designers must have in mind include monitoring of the vital signs of residents in nursing homes, disabled people, who can’t move, constant support for people regardless of the performed activity (e.g., during sleeping), infants, etc. In this work we verified the possibility of remote pulse estimation at a distance above 5m. Additionally, we integrated the deep learning algorithm for person tracking and identification, even when facial features are not visible. In this way, we enabled the collection of user specific measurements to create personalized vital signs patterns and we provided the support for monitoring of multiple people using one video stream. The preliminary results showed that it is possible to accurately (RMSE $< 2.8$ beats per minute) extract pulse from visible light sequences acquired with a webcam at a distance of 6m after applying a proper image pre-processing algorithm.