An FMCW Radar-Based Intelligent System for Non-Contact Detection and Monitoring of Pneumonia Symptoms

Ariana Tulus Purnomo (Computer Science and Informatics Department, Faculty of Engineering and Technology, Sampoerna University)
Raffy Frandito (Mechanical Engineering Department, Faculty of Engineering and Technology, Sampoerna University)
Edrick Hensel Limantoro (Computer Science and Informatics Department, Faculty of Engineering and Technology, Sampoerna University)
Rafie Djajasoepena (Information System Department, Faculty of Engineering and Technology, Sampoerna University)
Muhammad Agni Catur Bhakti (Computer Science and Informatics Department, Faculty of Engineering and Technology, Sampoerna University)
Ding Bing Lin (Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology)

Indonesian Journal of Computing, Engineering, and Design
Vol. 6 No. 1 (2024): IJoCED

Pneumonia is one of the most common contagious respiratory diseases, and one of its symptoms is shortness of breath. This symptom underscores the need for non-contact monitoring methods, which our paper addresses by proposing a strategy that uses Frequency-Modulated Continuous Wave (FMCW) radar to extract breathing waveforms and then classifies them with an eXtreme Gradient Boosting (XGBoost) model. The model performs well on our dataset, using stratified k-fold cross-validation and Mel-Frequency Cepstral Coefficients (MFCC) feature extraction. This intelligent system can correctly identify deep and deep-quick breathing patterns with 98% and 87.5% recall scores, respectively. Integrating FMCW and XGBoost offers a promising solution for early detection and real-time monitoring of pneumonia