Non-contact Breathing Analysis 3D Scanning and Printing Deformation Object Control Deformable Object Collision Visual Bioinformatics
Animation Computer Graphics Advanced Computer Graphics Game Design Shader and GPU Programming
Non-contact Breathing Analysis

WiKiSpiro: Non-contact Respiration volume Monitoring during Sleep
Phuc Nguyen, Shane Transue, Min-Hyung Choi, Ann C. Halbower, and Tam Vu

Abstract: Respiration volume has been widely used as an important indication for diagnosis and treatment of pulmonary diseases and other health care related issues such as critically ill patients neonatal ventilation, post-operative monitoring and various others. Most of existing technologies for respiration volume monitoring require physical contact with the human body. While wireless-based approaches have also been discussed in the literature, there are still limitations in terms of estimation accuracy and time efficiency preventing these approaches from being realized in practice. In this paper, we present an automated, wireless-based, vision-supervised system, called WiKiSpiro, for monitoring an individual's respiration volume. In particular, we present a system design encompassing a wireless device, motion tracking and control system, and a set of methods to accurately derive breathing volume from the reflected signal and to address challenges caused by body movement and posture changes. We present our preliminary results of WikiSpiro, and identify possible challenges for future research and development.

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Real-time Tidal Volume Estimation using Iso-surface Reconstruction
Shane Transue, Phuc Nguyen, Tam Vu, and Min-Hyung. Choi

Abstract: Breathing volume measurement has long been an important physiological indication widely used for the diagnosis and treatment of pulmonary diseases. However, most of existing breathing volume monitoring techniques require either physical contact with the patient or are prohibitively expensive. In this paper we present an automated and inexpensive non-contact, vision-based method for monitoring an individual's tidal volume, which is extracted from a three-dimensional (3D) chest surface reconstruction from a single depth camera. In particular, formulating the respiration monitoring process as a 3D space-time volumetric representation, we introduce a real-time surface reconstruction algorithm to generate omni-direction deformation states of a patient's chest while breathing, which reflects the change in tidal volume over time. These deformation states are then used to estimate breathing volume through a per-patient correlation metric acquired through a Bayesian-network learning process. Through prototyping and implementation, our results indicate that we have achieved 92.2% to 94.19% accuracy in the tidal volume estimations through the experimentation based on the proposed vision-based method.

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