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

arXiv: https://arxiv.org/abs/2203.10882 · HTML: https://mlsanity.com/paper/2203.10882/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 21 Mar 2022.

## Builds on (most cited first, 16 of 16)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [TSM: Temporal Shift Module for Efficient Video Understanding](https://mlsanity.com/paper/1811.08383/) (2018)
- [DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks](https://mlsanity.com/paper/1805.07888/) (2018)
- [Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement](https://mlsanity.com/paper/2006.03790/) (2020)
- [Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks](https://mlsanity.com/paper/1905.02419/) (2019)
- [RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation](https://mlsanity.com/paper/1910.11515/) (2019)
- [Remote Heart Rate Measurement from Highly Compressed Facial Videos: an End-to-end Deep Learning Solution with Video Enhancement](https://mlsanity.com/paper/1907.11921/) (2019)
- [The Way to my Heart is through Contrastive Learning: Remote Photoplethysmography from Unlabelled Video](https://mlsanity.com/paper/2111.09748/) (2021)
- [PulseGAN: Learning to generate realistic pulse waveforms in remote photoplethysmography](https://mlsanity.com/paper/2006.02699/) (2020)
- [VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video](https://mlsanity.com/paper/1810.04927/) (2018)
- [Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner](https://mlsanity.com/paper/2007.06786/) (2020)
- [AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searching](https://mlsanity.com/paper/2004.12292/) (2020)
- [Analysis of CNN-based remote-PPG to understand limitations and sensitivities](https://mlsanity.com/paper/1911.02736/) (2019)
- [Remote Photoplethysmography: Rarely Considered Factors](https://mlsanity.com/paper/2004.12695/) (2020)
- [Learning Higher-Order Dynamics in Video-Based Cardiac Measurement](https://mlsanity.com/paper/2110.03690/) (2021)

## Built on by (most cited first, 12 of 12)

- [rPPG-Toolbox: Deep Remote PPG Toolbox](https://mlsanity.com/paper/2210.00716/) (2022)
- [Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videos](https://mlsanity.com/paper/2211.16922/) (2022)
- [Camera-Based HRV Prediction for Remote Learning Environments](https://mlsanity.com/paper/2305.04161/) (2023)
- [PhysFlow: Skin tone transfer for remote heart rate estimation through conditional normalizing flows](https://mlsanity.com/paper/2407.21519/) (2024)
- [Deep adaptative spectral zoom for improved remote heart rate estimation](https://mlsanity.com/paper/2403.06902/) (2024)
- [PhysMamba: State Space Duality Model for Remote Physiological Measurement](https://mlsanity.com/paper/2408.01077/) (2024)
- [UbiHR: Resource-efficient Long-range Heart Rate Sensing on Ubiquitous Devices](https://mlsanity.com/paper/2410.19279/) (2024)
- [BeatFormer: Efficient motion-robust remote heart rate estimation through unsupervised spectral zoomed attention filters](https://mlsanity.com/paper/2507.14885/) (2025)
- [Periodic-MAE: Periodic Video Masked Autoencoder for rPPG Estimation](https://mlsanity.com/paper/2506.21855/) (2025)
- [PhysFlow: Frequency Decoupled with Dual-Field Rectified Flow for Remote Photoplethysmography](https://mlsanity.com/paper/2606.23226/) (2026)
- [CAST-Phys: Contactless Affective States Through Physiological signals Database](https://mlsanity.com/paper/2507.06080/) (2025)
- [Facial Spatiotemporal Graphs: Leveraging the 3D Facial Surface for Remote Physiological Measurement](https://mlsanity.com/paper/2601.13724/) (2026)

## Abstract (arXiv)

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.