# Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave Sensing

arXiv: https://arxiv.org/abs/2301.03713 · HTML: https://mlsanity.com/paper/2301.03713/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 111 references that are in the map.
- First posted on arXiv 9 Jan 2023.

## Builds on (most cited first, 3 of 3)

- [DistancePPG: Robust non-contact vital signs monitoring using a camera](https://mlsanity.com/paper/1502.08040/) (2015)
- [WiFi-based Real-time Breathing and Heart Rate Monitoring during Sleep](https://mlsanity.com/paper/1908.05108/) (2019)
- [Non-contact Vital Signs Monitoring through Visible Light Sensing](https://mlsanity.com/paper/1807.05408/) (2018)

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

- [FreCT: Frequency-augmented Convolutional Transformer for Robust Time Series Anomaly Detection](https://mlsanity.com/paper/2505.00941/) (2025)
- [Evolutionary Optimization of 1D-CNN for Non-contact Respiration Pattern Classification](https://mlsanity.com/paper/2312.13035/) (2023)

## Abstract (arXiv)

Human respiratory rate and its pattern convey essential information about the physical and psychological states of the subject. Abnormal breathing can indicate fatal health issues leading to further diagnosis and treatment. Wireless light-wave sensing (LWS) using incoherent infrared light shows promise in safe, discreet, efficient, and non-invasive human breathing monitoring without raising privacy concerns. The respiration monitoring system needs to be trained on different types of breathing patterns to identify breathing anomalies.The system must also validate the collected data as a breathing waveform, discarding any faulty data caused by external interruption, user movement, or system malfunction. To address these needs, this study simulated normal and different types of abnormal respiration using a robot that mimics human breathing patterns. Then, time-series respiration data were collected using infrared light-wave sensing technology. Three machine learning algorithms, decision tree, random forest and XGBoost, were applied to detect breathing anomalies and faulty data. Model performances were evaluated through cross-validation, assessing classification accuracy, precision and recall scores. The random forest model achieved the highest classification accuracy of 96.75% with data collected at a 0.5m distance. In general, ensemble models like random forest and XGBoost performed better than a single model in classifying the data collected at multiple distances from the light-wave sensing setup.