Development of Motion Detection Algorithm Using 3d Sensors for Patient Monitoring Support Service System...20th World Congress on Medical and Health Informatics (MEDINFO), August 9-13, 2025, Taipei, Taiwan
With the aging of the overall patient population, the incidence of patients developing delirium during hospitalization is increasing. This study aims to improve post-operative safety management and reduce the workload of nurses related to patient care. We have developed a monitoring system that uses...
| Publicado en: | Studies in Health Technology & Informatics Vol. 329; pp. 371 - 376 |
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| Autores principales: | , , , , , , , , , , |
| Formato: | pictorial proceedings research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187334882&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187334882 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09269630 U1V jtl: Studies in Health Technology & Informatics issn: 09269630 maglogo: N pubinfo: dt: 2025 vid: 329 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 187334882 187334882 187334882 10.3233/SHTI250864 187334882 ppf: 371 ppct: 5 formats: tig: atl: Development of Motion Detection Algorithm Using 3d Sensors for Patient Monitoring Support Service System...20th World Congress on Medical and Health Informatics (MEDINFO), August 9-13, 2025, Taipei, Taiwan aug: au: Masami MUKAI Yukihiro YOSHIDA Masaya YOTSUKURA Mieko MACHIDA Miyuki KANEMITSU Yoshiaki MIURA Katsuya NAGASE Yota OZEKI Tomohisa SAITO Masato KATAOKA Shun-ichi WATANABE affil: National Cancer Center Hospital, Division of Medical Informatics sug: subj: Motion Capture Motion Analysis Systems Data Analysis, Computer Assisted Algorithms Software Design Monitoring, Physiologic Congresses and Conferences Taiwan Taiwan Human Hospital Units Descriptive Statistics ab: With the aging of the overall patient population, the incidence of patients developing delirium during hospitalization is increasing. This study aims to improve post-operative safety management and reduce the workload of nurses related to patient care. We have developed a monitoring system that uses 3D sensors to detect specific behaviors and motions that require attention in cases where patients exhibit abnormal behaviors, such as falls and self-removal of IV lines, and trigger alerts. In this paper, we present an algorithm for detecting dangerous motions. We use the point cloud data generated by the 3D sensors that collect 3D information. We analyze the motions of subjects based on changes in the point cloud data and tag specific human body motions and behaviors. When there are no obstacles in the imaging direction of the 3D sensors, we detect human body movements (supine position on the bed, half sitting up, and separated from the bed) with an F-measure of 98.33% and motions (thrashing limbs, touching mouth/neck/arms, no action) with an F-measure of 98.23%. We detect the basic motions that trigger alert notifications. However, the detection accuracy decreases depending on the imaging conditions and subject movements. We use invisible and safe near-infrared light for motion detection and recognition to perform imaging even after lights are turned off, without disturbing patients' sleep. Motion recognition using point cloud data is a privacy-friendly monitoring method with a low risk of acquiring personally identifiable information. In the future, we plan to verify the algorithm using actual patients and investigate the detection of motions in addition to those considered in this study. pubtype: Academic Journal doctype: pictorial proceedings research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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