# Algorithmic Principles of Camera-based Respiratory Motion Extraction

arXiv: https://arxiv.org/abs/2105.07537 · HTML: https://mlsanity.com/paper/2105.07537/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 65 references that are in the map.
- First posted on arXiv 16 May 2021.

## Builds on (most cited first, 2 of 2)

- [Advancing Non-Contact Vital Sign Measurement using Synthetic Avatars](https://mlsanity.com/paper/2010.12949/) (2020)
- [FarSense: Pushing the Range Limit of WiFi-based Respiration Sensing with CSI Ratio of Two Antennas](https://mlsanity.com/paper/1907.03994/) (2019)

## Built on by (most cited first, 6 of 6)

- [SleepVST: Sleep Staging from Near-Infrared Video Signals using Pre-Trained Transformers](https://mlsanity.com/paper/2404.03831/) (2024)
- [Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera](https://mlsanity.com/paper/2306.03711/) (2023)
- [Improved Touchless Respiratory Rate Sensing](https://mlsanity.com/paper/2211.11630/) (2022)
- [Efficient and Robust Multidimensional Attention in Remote Physiological Sensing through Target Signal Constrained Factorization](https://mlsanity.com/paper/2505.07013/) (2025)
- [A Robust Camera-based Method for Breath Rate Measurement](https://mlsanity.com/paper/2512.03827/) (2025)
- [Subject-Relative Micro-Motion and Sleep Dynamics for Near-Infrared Video Sleep Staging](https://mlsanity.com/paper/2609.05550/) (2026)

## Abstract (arXiv)

Measuring the respiratory signal from a video based on body motion has been proposed and recently matured in products for video health monitoring. The core algorithm for this measurement is the estimation of tiny chest/abdominal motions induced by respiration, and the fundamental challenge is motion sensitivity. Though prior arts reported on the validation with real human subjects, there is no thorough/rigorous benchmark to quantify the sensitivities and boundary conditions of motion-based core respiratory algorithms that measure sub-pixel displacement between video frames. In this paper, we designed a setup with a fully-controllable physical phantom to investigate the essence of core algorithms, together with a mathematical model incorporating two motion estimation strategies and three spatial representations, leading to six algorithmic combinations for respiratory signal extraction. Their promises and limitations are discussed and clarified via the phantom benchmark. The insights gained in this paper are intended to improve the understanding and applications of camera-based respiration measurement in health monitoring.