no code implementations • 4 May 2024 • Joaquim Comas, Adria Ruiz, Federico Sukno
Recent advancements in remote heart rate measurement (rPPG), motivated by data-driven approaches, have significantly improved accuracy.
no code implementations • 11 Mar 2024 • Joaquim Comas, Adria Ruiz, Federico Sukno
The objective of our proposed model is to tailor the CZT to match the characteristics of each specific dataset sensor, facilitating a more optimal and accurate estimation of HR from the rPPG signal without compromising generalization across diverse datasets.
no code implementations • 21 Mar 2022 • Joaquim Comas, Adria Ruiz, Federico Sukno
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.
no code implementations • 10 Dec 2019 • Joaquim Comas, Decky Aspandi, Xavier Binefa
In this work, we propose a multi-modal emotion recognition model based on deep learning techniques using the combination of peripheral physiological signals and facial expressions.