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Volume: 33 | Article ID: art00006
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Bed Exit Detection Network (BED Net) for Patients Bed-Exit Monitoring Based on Color Camera Images
  DOI :  10.2352/ISSN.2470-1173.2021.8.IMAWM-269  Published OnlineJanuary 2021
Abstract

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.

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Fan Bu, Qian Lin, Jan Allebach, "Bed Exit Detection Network (BED Net) for Patients Bed-Exit Monitoring Based on Color Camera Imagesin Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World,  2021,  pp 269-1 - 269-8,  https://doi.org/10.2352/ISSN.2470-1173.2021.8.IMAWM-269

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