Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Loss

TL;DR

This paper introduces a lightweight neural network for remote heart rate estimation from facial videos, utilizing convolutional derivatives and a novel temporal loss to improve accuracy and efficiency.

Contribution

The paper proposes a new model combining convolutional derivatives and a time-shift invariant loss for efficient and accurate remote PPG-based heart rate estimation.

Findings

Competitive accuracy on public datasets

Lower model complexity and computational cost

Effective handling of temporal offsets

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.