Print Email Facebook Twitter Heart Rate Estimation in Intense Exercise Videos using Temporal Superpixels Title Heart Rate Estimation in Intense Exercise Videos using Temporal Superpixels Author Marwade, Anwesh (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor van Gemert, J.C. (mentor) Degree granting institution Delft University of Technology Date 2021-07-26 Abstract Estimation of physiological signals like heart rate using remote photoplethysmography (rPPG) provides non-contact health monitoring with important applications in remote healthcare, patient interaction, and sport related activities. Existing rPPG approaches can robustly measure heart rate from facial videos under some degree of motion through face tracking and alignment. However, accurate face detection is not always feasible as the subject's face might be occluded or even outside the camera frame. This is especially the case for unconstrained settings like exercise and sports. Here we present IBIS-CNN, a novel approach to rPPG based heart rate estimation using spatio-temporal superpixels, which improves on existing models by eliminating the requirement for a visible face and face tracking. Experiments conducted on two publicly available rPPG datasets in addition to a self-collected dataset show that IBIS-CNN outperforms the existing rPPG methods which have difficulty in estimating heart rate in these unconstrained settings. We present experiments to analyse the issues with detection/alignment based methods, highlighting the promising potential for using temporal superpixels for rPPG based HR estimation using a convolutional neural network in IBIS-CNN. Subject Heart Rate estimationTemporal SuperpixelsMachine LearningPhysiologyComputer Vision To reference this document use: http://resolver.tudelft.nl/uuid:51c5e7c9-4c64-494d-acdd-96d3982cab0c Part of collection Student theses Document type master thesis Rights © 2021 Anwesh Marwade Files PDF Anwesh_Final_Report.pdf 6.01 MB Close viewer /islandora/object/uuid:51c5e7c9-4c64-494d-acdd-96d3982cab0c/datastream/OBJ/view