SpiRadar: Radar-Based Non-Contact Spirometry via Sparse Polynomial Framework

Yonathan Eder, Yhonatan Kvich, Naama Golan, Adi Wagerhoff, Safit Levy, Lior Barbash, Ron Berant, Patrick Stafler, Yonina C. Eldar

Chronic respiratory diseases affect hundreds of millions of people worldwide, with spirometry serving as the gold standard for pulmonary function assessment. However, conventional spirometry's reliance on mouthpieces and nose clips creates discomfort and technical challenges that can compromise test quality, particularly in pediatric populations where cooperation difficulties are amplified. This study presents SpiRadar, a comprehensive radar-based framework for non-contact spirometry that eliminates physical contact requirements while enabling accurate curve reconstruction, clinical parameter estimation, and bronchodilator response (BDR) assessment. Our key contributions include: (1) a methodological framework integrating physiologically-motivated preprocessing for robust signal extraction, a feature-dependent polynomial transformation linking radar-measured thoracic displacement to spirometric volume curves, and sparse optimization enabling generalization to unseen subjects without individual calibration; (2) a rigorous validation on a pediatric cohort of 39 subjects (ages 6-18 years) undergoing 58 spirometry trials, including healthy children and asthma patients tested pre- and post-bronchodilator, using subject-level leave-one-out cross-validation; (3) clinical-grade performance outperforming alternative non-contact methods, achieving accurate curve reconstruction (mean RMSE: 0.23 +- 0.12 L) and strong correlations (above 0.84) for key spirometric parameters. BDR classification achieved 89.5% accuracy with balanced sensitivity (90.9%) and specificity (87.5%). These results establish clinical feasibility for our non-contact spirometry approach, with particular promise for pediatric asthma monitoring and remote patient care.