  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.
