Respiratory Anomaly Detection using Reflected Infrared Light-wave
  Signals
Md Zobaer Islam Brenden Martin Carly Gotcher Tyler Martinez John F. O'Hara Sabit Ekin
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 light source (e.g., infrared light emitting diode) and
sensor (e.g., photodetector). This light-wave sensing (LWS) 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. The anomaly detection
model demonstrates up to 96.6% average accuracy in classifying 7 different
types of breathing data using machine learning. The model can also detect
faulty data collected by the system that does not contain breathing
information. The developed system can be utilized at home or healthcare
facilities as a smart, non-contact and discreet respiration monitoring method.