Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106188
DC Field | Value | Language |
---|---|---|
dc.contributor | Research Institute for Smart Ageing | en_US |
dc.contributor | Department of Biomedical Engineering | en_US |
dc.creator | Mao, YJ | en_US |
dc.creator | Tam, AYC | en_US |
dc.creator | Shea, QTK | en_US |
dc.creator | Zheng, YP | en_US |
dc.creator | Cheung, JCW | en_US |
dc.date.accessioned | 2024-05-03T00:45:41Z | - |
dc.date.available | 2024-05-03T00:45:41Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/106188 | - |
dc.language.iso | en | en_US |
dc.publisher | MDPI | en_US |
dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Mao Y-J, Tam AY-C, Shea QT-K, Zheng Y-P, Cheung JC-W. eNightTrack: Restraint-Free Depth-Camera-Based Surveillance and Alarm System for Fall Prevention Using Deep Learning Tracking. Algorithms. 2023; 16(10):477 is available at https://dx.doi.org/10.3390/a16100477. | en_US |
dc.subject | Computer vision | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Object tracking | en_US |
dc.subject | Patient monitor | en_US |
dc.subject | Bed exiting | en_US |
dc.subject | Fall | en_US |
dc.subject | Hospital ward | en_US |
dc.title | eNightTrack : restraint-free depth-camera-based surveillance and alarm system for fall prevention using deep learning tracking | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 16 | en_US |
dc.identifier.issue | 10 | en_US |
dc.identifier.doi | 10.3390/a16100477 | en_US |
dcterms.abstract | Falls are a major problem in hospitals, and physical or chemical restraints are commonly used to "protect" patients in hospitals and service users in hostels, especially elderly patients with dementia. However, physical and chemical restraints may be unethical, detrimental to mental health and associated with negative side effects. Building upon our previous development of the wandering behavior monitoring system "eNightLog", we aimed to develop a non-contract restraint-free multi-depth camera system, "eNightTrack", by incorporating a deep learning tracking algorithm to identify and notify about fall risks. Our system evaluated 20 scenarios, with a total of 307 video fragments, and consisted of four steps: data preparation, instance segmentation with customized YOLOv8 model, head tracking with MOT (Multi-Object Tracking) techniques, and alarm identification. Our system demonstrated a sensitivity of 96.8% with 5 missed warnings out of 154 cases. The eNightTrack system was robust to the interference of medical staff conducting clinical care in the region, as well as different bed heights. Future research should take in more information to improve accuracy while ensuring lower computational costs to enable real-time applications. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Algorithms, Oct. 2023, v. 16, no. 10, 477 | en_US |
dcterms.isPartOf | Algorithms | en_US |
dcterms.issued | 2023-10 | - |
dc.identifier.isi | WOS:001090603000001 | - |
dc.identifier.eissn | 1999-4893 | en_US |
dc.identifier.artn | 477 | en_US |
dc.description.validate | 202405 bcrc | en_US |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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algorithms-16-00477.pdf | 7.88 MB | Adobe PDF | View/Open |
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