  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 non-invasive 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 0.5m 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 light-wave sensing setup.
