2023/11/02 by Md Zobaer Islam, Brenden Martin, Islam, Md Zobaer +9
Computer Science · Engineering · Medicine · #Anomaly Detection Techniques and Applications #COVID-19 diagnosis using AI #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Non-Invasive Vital Sign Monitoring #Signal Processing (eess.SP) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2311.01367
openalex publication_date 2023/11/02 · openalex created_date 2023/11/04 · openalex updated_date 2026/07/28
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.