Applications of RGB-D Cameras in Healthcare

Abstract

RGB-D cameras are a type of imaging device that includes a standard colour camera and a depth sensor. The depth sensor records depth images, where each pixel measures the distance of the target from the camera. Using data from the RGB-D camera, we can recon- struct a three-dimensional scene. Since the introduction of the Microsoft Kinect, multiple affordable RGB-D cameras have reached the market and are even integrated into laptops and mobile phones. In this thesis, healthcare applications of RGB-D cameras are investigated, focussing on two areas. The first is Structured Light Plethysmography (SLP), a method for measuring lung function. The non-contact nature of SLP is ideal for respiratory measurement, as it avoids potential contamination without requiring single-use equipment. The current SLP device is large and expensive, while the data it produces is low-resolution and does not adapt to the patient. A new SLP processing pipeline based on an RGB-D camera is proposed, which will produce automatically aligned, high-resolution data in a portable device. Algorithm development to enhance the data processing capabilities of this new pipeline is presented. A method for interpreting data from the high-resolution grid, via a control point-based shape modelling technique, is used to show differences in the respiratory pat- tern before and after exercise. A system is described for compensating for the motion of the subject, so the resulting data only shows motion due to breathing. This method is demonstrated by compensating for the pedalling motion of a subject on an exercise bike. The second application area is the Neonatal Intensive Care Unit (NICU), which cares for critically ill new-born babies. A proof-of-concept study is designed and conducted to investigate the potential use of an RGB-D camera in the NICU. In this thesis, the collec- tion of 24 hours of RGB-D video footage is presented, and it is shown how the infant’s respiratory rate can be measured from the depth data.