# Non-Contact Health Monitoring During Daily Personal Care Routines

arXiv: https://arxiv.org/abs/2506.09718 · HTML: https://mlsanity.com/paper/2506.09718/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 16 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 10 of 10)

- [Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks](https://mlsanity.com/paper/1905.02419/) (2019)
- [rPPG-Toolbox: Deep Remote PPG Toolbox](https://mlsanity.com/paper/2210.00716/) (2022)
- [MMPD: Multi-Domain Mobile Video Physiology Dataset](https://mlsanity.com/paper/2302.03840/) (2023)
- [Camera Measurement of Physiological Vital Signs](https://mlsanity.com/paper/2111.11547/) (2021)
- [Spiking-PhysFormer: Camera-Based Remote Photoplethysmography with Parallel Spike-driven Transformer](https://mlsanity.com/paper/2402.04798/) (2024)
- [Camera-Based HRV Prediction for Remote Learning Environments](https://mlsanity.com/paper/2305.04161/) (2023)
- [Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes](https://mlsanity.com/paper/2409.19223/) (2024)
- [Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin Tones](https://mlsanity.com/paper/2404.05003/) (2024)
- [ALPHA: AnomaLous Physiological Health Assessment Using Large Language Models](https://mlsanity.com/paper/2311.12524/) (2023)
- [A Dataset and Toolkit for Multiparameter Cardiovascular Physiology Sensing on Rings](https://mlsanity.com/paper/2505.04172/) (2025)

## Built on by (most cited first, 3 of 3)

- [PhysDrive: A Multimodal Remote Physiological Measurement Dataset for In-vehicle Driver Monitoring](https://mlsanity.com/paper/2507.19172/) (2025)
- [M3PD Dataset: Dual-view Photoplethysmography (PPG) Using Front-and-rear Cameras of Smartphones in Lab and Clinical Settings](https://mlsanity.com/paper/2511.02349/) (2025)
- [FacePhys: State of the Heart Learning](https://mlsanity.com/paper/2512.06275/) (2025)

## Abstract (arXiv)

Remote photoplethysmography (rPPG) enables non-contact, continuous monitoring of physiological signals and offers a practical alternative to traditional health sensing methods. Although rPPG is promising for daily health monitoring, its application in long-term personal care scenarios, such as mirror-facing routines in high-altitude environments, remains challenging due to ambient lighting variations, frequent occlusions from hand movements, and dynamic facial postures. To address these challenges, we present LADH (Long-term Altitude Daily Health), the first long-term rPPG dataset containing 240 synchronized RGB and infrared (IR) facial videos from 21 participants across five common personal care scenarios, along with ground-truth PPG, respiration, and blood oxygen signals. Our experiments demonstrate that combining RGB and IR video inputs improves the accuracy and robustness of non-contact physiological monitoring, achieving a mean absolute error (MAE) of 4.99 BPM in heart rate estimation. Furthermore, we find that multi-task learning enhances performance across multiple physiological indicators simultaneously. Dataset and code are open at https://github.com/McJackTang/FusionVitals.