Efficient remote photoplethysmography with temporal derivative modules and time-shift invariant loss

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• dc.contributor.author Comas, Joaquim
• dc.contributor.author Ruiz Ovejero, Adrià
• dc.contributor.author Sukno, Federico Mateo
• dc.date.accessioned 2023-06-06T06:14:47Z
• dc.date.available 2023-06-06T06:14:47Z
• dc.date.issued 2022
• dc.description Comunicació presentada a 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), celebrat del 19 al 20 de juny de 2022 a Nova Orleans, Estats Units.
• dc.description.abstract We present a lightweight neural model for remote heart rate estimation focused on the efficient spatio-temporal learning of facial photoplethysmography (PPG) based on i) modelling of PPG dynamics by combinations of multiple convolutional derivatives, and ii) increased flexibility of the model to learn possible offsets between the facial video PPG and the ground truth. PPG dynamics are modelled by a Temporal Derivative Module (TDM) constructed by the incremental aggregation of multiple convolutional derivatives, emulating a Taylor series expansion up to the desired order. Robustness to ground truth offsets is handled by the introduction of TALOS (Temporal Adaptive LOcation Shift), a new temporal loss to train learning-based models. We verify the effectiveness of our model by reporting accuracy and efficiency metrics on the public PURE and UBFC-rPPG datasets. Compared to existing models, our approach shows competitive heart rate estimation accuracy with a much lower number of parameters and lower computational cost.
• dc.description.sponsorship This work is partly supported by the eSCANFace project (PID2020-114083GB-I00) funded by the Spanish Ministry of Science and Innovation.
• dc.format.mimetype application/pdf
• dc.identifier.citation Comas J, Ruiz, A, Sukno F. Efficient remote photoplethysmography with temporal derivative modules and time-shift invariant loss. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2022 Jun 19-20; New Orleans, United States. [Piscataway]: IEEE; 2022. p. 2181-90. DOI: 10.1109/CVPRW56347.2022.00237
• dc.identifier.doi http://dx.doi.org/10.1109/CVPRW56347.2022.00237
• dc.identifier.isbn 978-1-6654-8740-5
• dc.identifier.issn 2160-7508
• dc.identifier.uri http://hdl.handle.net/10230/57051
• dc.language.iso eng
• dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
• dc.relation.ispartof 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW); 2022 Jun 19-20; New Orleans, United States. [Piscataway]: IEEE; 2022. p. 2181-90.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-114083GB-I00
• dc.rights © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.1109/CVPRW56347.2022.00237
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.subject.keyword Training
• dc.subject.keyword Adaptation models
• dc.subject.keyword Convolution
• dc.subject.keyword Computational modeling
• dc.subject.keyword Estimation
• dc.subject.keyword Photoplethysmography
• dc.subject.keyword Time division multiplexing
• dc.title Efficient remote photoplethysmography with temporal derivative modules and time-shift invariant loss
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/acceptedVersion