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 develop an algorithm to combine bed position detection, patient posture estimation, and action recognition
Dataset: own CSIRO dataset and public datasets, e.g. pressure pose (Harvard), multimodal in bed pose (North Eastern University).
Contact: Ahmedt Aristizabal, David (Data61, Black Mountain)