NVIDIA Jetson Nano and Python-based Economical Human Fall Detection and Analysis System

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dc.contributor.authorChudanat Sudthongkhong
dc.contributor.authorSiwat Suksri
dc.contributor.authorChanate Ratanaubol
dc.contributor.authorSookyuen Tepthong
dc.contributor.authorJira Jitsupa
dc.contributor.authorPutawan Suksai
dc.contributor.correspondenceC. Sudthongkhong; King Mongkut's University of Technology Thonburi (KMUTT), School of Architecture and Design, Department of Medical and Science Media, Bangkok, Thailand; email: medicalmedia01@gmail.com
dc.date.accessioned2025-03-10T07:34:45Z
dc.date.available2025-03-10T07:34:45Z
dc.date.issued2023
dc.description.abstractEvery year, around one-third of elderly individuals experience falls at home, especially in high-risk areas like bathrooms and stairs. Uneven floor surfaces exacerbate these dangers, impeding elderly mobility and significantly increasing fall risks, with recurrent falls being common. Recognizing this pressing concern, our project introduces a 'Human Fall Detection and Estimation System' to mitigate harm. This system deploys a specialized camera with gesture recognition software to monitor for falls by detecting posture deviations. When a fall occurs, the system records the location and uses advanced Image Processing for precise Pose Estimation. Deep Learning analyzes Pose Estimation data to gauge fall severity and simultaneously alerts caregivers via the network for swift assistance. Incidents are logged in a database for root cause analysis, facilitating more effective elderly care systems. our system plays a crucial role in preventing and addressing elderly falls, swiftly detecting and assessing incidents, and alerting caregivers [1], enhancing elderly safety and well-being. © 2023 IEEE.
dc.identifier.citationProceedings - 17th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2023
dc.identifier.doi10.1109/SITIS61268.2023.00083
dc.identifier.isbn979-835037091-1
dc.identifier.scopus2-s2.0-85190146908
dc.identifier.urihttps://repository.dusit.ac.th//handle/123456789/4555
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights.holderScopus
dc.subjectCaregiver alert system
dc.subjectDeep Learning
dc.subjectElderly falls
dc.subjectFall detection system
dc.subjectHuman posture analysis
dc.titleNVIDIA Jetson Nano and Python-based Economical Human Fall Detection and Analysis System
dc.typeConference paper
mods.location.urlhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85190146908&doi=10.1109%2fSITIS61268.2023.00083&partnerID=40&md5=5274e930d185855ede0cd92fb8fe5e90
oaire.citation.endPage467
oaire.citation.startPage463
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