Lung Volume Estimation Using Upper Chest Motion Tracking with a Intel Realsense Depth Camera

Résumé fourni par la source

Contactless respiratory monitoring has emerged as a promising alternative to traditional spirometry, offering advantages in comfort, hygiene, and continuous assessment in both clinical and homecare settings. This study proposes a method for estimating lung volume by tracking chest motion using a 3D Intel RealSense depth camera. By capturing real-time depth data from the thoracic region, the system identifies and tracks key surface landmarks throughout the respiratory cycle. The 3D velocity vector of these landmarks reflects the dynamic expansion and contraction of the chest during breathing. Statistical features such as maximum, minimum, average, and variability of motion are extracted and used to train regression models. Linear regression model based on a three-point chest tracking ($\mathrm{F}_{3}$-LR-20) demonstrated the highest predictive performance. The model's outputs were compared to established reference values from the Baldwin and Japanese Respiratory Society (JSR) equations, with results showing a closer alignment to the JSR standard.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.