# UbiHR: Resource-efficient Long-range Heart Rate Sensing on Ubiquitous Devices

arXiv: https://arxiv.org/abs/2410.19279 · HTML: https://mlsanity.com/paper/2410.19279/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 87 references that are in the map.
- First posted on arXiv 25 Oct 2024.

## Builds on (most cited first, 20 of 20)

- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [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)
- [DoctorGLM: Fine-tuning your Chinese Doctor is not a Herculean Task](https://mlsanity.com/paper/2304.01097/) (2023)
- [Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks](https://mlsanity.com/paper/1905.02419/) (2019)
- [PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer](https://mlsanity.com/paper/2111.12082/) (2021)
- [RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation](https://mlsanity.com/paper/1910.11515/) (2019)
- [Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling](https://mlsanity.com/paper/2007.08213/) (2020)
- [EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurement](https://mlsanity.com/paper/2110.04447/) (2021)
- [PulseGAN: Learning to generate realistic pulse waveforms in remote photoplethysmography](https://mlsanity.com/paper/2006.02699/) (2020)
- [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)
- [SCAMPS: Synthetics for Camera Measurement of Physiological Signals](https://mlsanity.com/paper/2206.04197/) (2022)
- [Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videos](https://mlsanity.com/paper/2211.16922/) (2022)
- [DistancePPG: Robust non-contact vital signs monitoring using a camera](https://mlsanity.com/paper/1502.08040/) (2015)
- [Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Loss](https://mlsanity.com/paper/2203.10882/) (2022)
- [AdaDeep: A Usage-Driven, Automated Deep Model Compression Framework for Enabling Ubiquitous Intelligent Mobiles](https://mlsanity.com/paper/2006.04432/) (2020)
- [Efficient Real-Time Camera Based Estimation of Heart Rate and Its Variability](https://mlsanity.com/paper/1909.01206/) (2019)
- [Enabling Smartphone-based Estimation of Heart Rate](https://mlsanity.com/paper/1912.08910/) (2019)

## Built on by (most cited first, 2 of 2)

- [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)
- [From Neck to Head: Bio-Impedance Sensing for Head Pose Estimation](https://mlsanity.com/paper/2507.12884/) (2025)

## Abstract (arXiv)

Ubiquitous on-device heart rate sensing is vital for high-stress individuals and chronic patients. Non-contact sensing, compared to contact-based tools, allows for natural user monitoring, potentially enabling more accurate and holistic data collection. However, in open and uncontrolled mobile environments, user movement and lighting introduce. Existing methods, such as curve-based or short-range deep learning recognition based on adjacent frames, strike the optimal balance between real-time performance and accuracy, especially under limited device resources. In this paper, we present UbiHR, a ubiquitous device-based heart rate sensing system. Key to UbiHR is a real-time long-range spatio-temporal model enabling noise-independent heart rate recognition and display on commodity mobile devices, along with a set of mechanisms for prompt and energy-efficient sampling and preprocessing. Diverse experiments and user studies involving four devices, four tasks, and 80 participants demonstrate UbiHR's superior performance, enhancing accuracy by up to 74.2% and reducing latency by 51.2%.