Abstract: In-bed pose estimation has shown value in fields such as hospital patient monitoring, sleep studies, and smart homes. However, this pose estimation comes with specific challenges including the notable differences in lighting conditions throughout a day and also having different pose distribution from the common human surveillance viewpoint. This project aims to provide combine bed position detection and patient posture estimation. Dataset: adapt and develop datasets (e.g. simulated pose estimation ).
Contact: David Ahmedt-Aristizabal