Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave Sensing

TL;DR

This paper presents a non-contact method for detecting abnormal breathing patterns using infrared light-wave sensing combined with machine learning, achieving high accuracy in simulated environments.

Contribution

It introduces a novel approach integrating infrared light-wave sensing with ensemble machine learning models for non-invasive respiratory anomaly detection.

Findings

Random forest achieved 96.75% accuracy.

Ensemble models outperformed single classifiers.

Effective at 0.5m sensing distance.

Abstract

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.