Respiratory Anomaly Detection using Reflected Infrared Light-wave Signals
This provides a low-cost, non-contact method for monitoring breathing anomalies, but it is incremental as it builds on existing sensing technologies.
The study tackled respiratory anomaly detection by using reflected infrared light-wave signals from a mechanical robot's chest, achieving an average classification accuracy of up to 96.6% with machine learning.
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.