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dc.contributor.authorNizam, Y.*
dc.contributor.authorAbdul Jamil, M.M.*
dc.contributor.authorMohd, M.N.H.*
dc.contributor.authorYouseffi, Mansour*
dc.contributor.authorDenyer, Morgan C.T.*
dc.date.accessioned2019-04-01T15:37:28Z
dc.date.available2019-04-01T15:37:28Z
dc.date.issued2018-07
dc.identifier.citationNizam Y, Abdul Jamil MM, Mohd MNH et al (2018) A novel algorithm for human fall detection using height, velocity and position of the subject from depth maps. International Journal of Integrated Engineering. 10(3): 32-41.en_US
dc.identifier.urihttp://hdl.handle.net/10454/16944
dc.descriptionYesen_US
dc.description.abstractHuman fall detection systems play an important role in our daily life, because falls are the main obstacle for elderly people to live independently and it is also a major health concern due to aging population. Different approaches are used to develop human fall detection systems for elderly and people with special needs. The three basic approaches include some sort of wearable devices, ambient based devices or non-invasive vision-based devices using live cameras. Most of such systems are either based on wearable or ambient sensor which is very often rejected by users due to the high false alarm and difficulties in carrying them during their daily life activities. This paper proposes a fall detection system based on the height, velocity and position of the subject using depth information from Microsoft Kinect sensor. Classification of human fall from other activities of daily life is accomplished using height and velocity of the subject extracted from the depth information. Finally position of the subject is identified for fall confirmation. From the experimental results, the proposed system was able to achieve an average accuracy of 94.81% with sensitivity of 100% and specificity of 93.33%.en_US
dc.description.sponsorshipPartly sponsored by Center for Graduate Studies. This work is funded under the project titled “Biomechanics computational modeling using depth maps for improvement on gait analysis”. Universiti Tun Hussein Onn Malaysia for provided lab components and GPPS (Project Vot No. U462) sponsor.en_US
dc.language.isoenen_US
dc.rights(c) 2018 UTHM Publisher. All right reserved. This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.en_US
dc.subjectFall detectionen_US
dc.subjectKinect sensoren_US
dc.subjectDepth imagesen_US
dc.subjectNon-invasiveen_US
dc.subjectDepth sensoren_US
dc.titleA novel algorithm for human fall detection using height, velocity and position of the subject from depth mapsen_US
dc.status.refereedYesen_US
dc.date.Accepted2018-05-16
dc.date.application2018-07-02
dc.typeArticleen_US
dc.type.versionPublished versionen_US
refterms.dateFOA2019-04-01T15:37:28Z
dc.relation.urlhttp://penerbit.uthm.edu.my/ojs/index.php/ijie/article/view/2129


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