Among hospitalized patients, getting up from bed can lead to fall injuries, 20% of which are severe cases such as broken bones or head injuries. To monitor patients’ bed-side status, we propose a deep neural network model, Bed Exit Detection Network (BED Net), for bed-exit
behavior recognition. The BED Net consists of two sub-networks: a Posture Detection Network (Pose Net), and an Action Recognition Network (AR Net). The Pose Net leverages state-of-the-art neural-network-based keypoint detection algorithms to detect human postures from color camera images.
The output sequences from Pose Net are passed to the AR Net for bed-exit behavior recognition. By formatting a pre-trained model as an intermediary, we train the proposed network using a newly collected small dataset, HP-BED-Dataset. We will show the results of our proposed BED Net.