Respiratory Anomaly Detection using Reflected Infrared Light-wave Signals

Abstract

In this study, we present a non-contact respiratory anomaly detection method using incoherent light-wave signals reflected from the chest of a mechanical robot that can breathe like human beings. In comparison to existing radar and camera-based sensing systems for vitals monitoring, this technology uses only a low-cost ubiquitous infrared light source and sensor. This light-wave sensing system recognizes different breathing anomalies from the variations of light intensity reflected from the chest of the robot within a 0.5m-1.5m range with an average classification accuracy of up to 96.6% using machine learning.

1 page poster paper, 1 figure, 2 tables, accepted and presented in 23rd Wireless Telecommunications Symposium 2024. Symposium proceedings link: https://wtsconference.org/documents/WTS%202024%20-%20Program.pdf . Full version at 2311.01367v1