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dc.contributor.authorChawakorn Sri-Ngernyuangen_US
dc.contributor.authorPrakarnkiat Youngkongen_US
dc.contributor.authorDuangruedee Lasukaen_US
dc.contributor.authorKitti Thamrongaphichartkulen_US
dc.contributor.authorWatcharapong Pingmuangen_US
dc.date.accessioned2019-03-18T02:22:10Z-
dc.date.available2019-03-18T02:22:10Z-
dc.date.issued2019-01-10en_US
dc.identifier.other2-s2.0-85062058489en_US
dc.identifier.other10.1109/BMEiCON.2018.8609998en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85062058489&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/63628-
dc.description.abstract© 2018 IEEE. One of the critical issues in hospitals is the injury from falling out of patient bed. Some of these cases lead to death. Considering this type of incident, a monitoring and alarming system called NEFs (Never Ever Falls) is introduced to prevent patients from falling out of the bed. In this paper, on-bed pattern recognition is developed by applying Neural Network Pattern Recognition from MATLAB. In the experiment, data from 6 persons in 5 different on-bed patterns (Sitting inside the bed, Supine, Lateral on the left, Lateral on the right and sitting at bedsides and corners) is recorded. According to the confusion matrix, training and validation confusion tables show 99.5% and 89.1% accuracy, respectively.en_US
dc.subjectComputer Scienceen_US
dc.subjectEngineeringen_US
dc.subjectPhysics and Astronomyen_US
dc.titleNeural Network for On-bed Movement Pattern Recognitionen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleBMEiCON 2018 - 11th Biomedical Engineering International Conferenceen_US
article.stream.affiliationsKing Mongkut s University of Technology Thonburien_US
article.stream.affiliationsChiang Mai Universityen_US
Appears in Collections:CMUL: Journal Articles

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