Noncontact Respiratory Anomaly Detection Using Infrared Light-Wave Sensing

• Published in: IEEE transactions on human-machine systems Vol. 54; no. 3; pp. 292 - 303
• Main Authors: Islam, Md Zobaer, Martin, Brenden, Gotcher, Carly, Martinez, Tyler, O'Hara, John F., Ekin, Sabit
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Anomalies; Anomaly detection; Biomedical monitoring; Breathing; Classification; Data collection; Decision trees; Infrared radiation; light-wave sensing (LWS); Machine learning; Monitoring; noncontact vitals monitoring; Photodetectors; Respiration; respiration monitoring; Respiratory rate; Robot sensing systems; Sleep apnea; Waveforms
• ISSN: 2168-2291; 2168-2305
• DOI: 10.1109/THMS.2024.3381574

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 noninvasive 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 0.5 m 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 LWS setup.