Publication:
Real-Time Threshold-Based Fall Detection System Using Wearable IoT

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dc.citedby2
dc.contributor.authorAmir N.I.M.en_US
dc.contributor.authorDziyauddin R.A.en_US
dc.contributor.authorMohamed N.en_US
dc.contributor.authorIsmail N.S.N.en_US
dc.contributor.authorZulkifli N.S.A.en_US
dc.contributor.authorDin N.M.en_US
dc.contributor.authorid57271913000en_US
dc.contributor.authorid57198512001en_US
dc.contributor.authorid26422450900en_US
dc.contributor.authorid36198276900en_US
dc.contributor.authorid54895883500en_US
dc.contributor.authorid9335429400en_US
dc.date.accessioned2023-05-29T09:39:36Z
dc.date.available2023-05-29T09:39:36Z
dc.date.issued2022
dc.descriptionInternet of things; Wearable technology; ADXL 335; Condition; Detection system; Fall detection; Fall detection system; IoT; Real- time; Threshold methods; Time thresholds; Wearable devices; Fall detectionen_US
dc.description.abstractThis paper presents a Real-Time Fall Detection System (FDS) in the form of a wearable device integrating an ADXL335 accelerometer as a fall detection sensor, and classify the falling condition based on the threshold method. This system detects the wearer's movements and analyses the result in binary output conditions of 'Fall' for any fall occurrence or 'Normal' for other activities. The transmitter or FDS-Tx which is attached to the user's garment will constantly transmit data reading to the receiver or FDS-Rx via XBee module for data analysis. Raspberry Pi as the processor in FDS-Rx provides computational resources for immediate output analysis, by using threshold method, the computed results are sent to the cloud utilizing the Wi-Fi to display the user's condition on the authority's dashboard for further action. The working conditions of the systems are validated through an experiment of 10 volunteers whose perform several activities including fall events. Based on the threshold proposed, the results showed 97% sensitivity, 69% specificity and 83% accuracy from the experiment. Thus, this system fulfilled the real-Time working condition integrating (IoT) as accordingly. � 2022 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICSSA54161.2022.9870974
dc.identifier.epage178
dc.identifier.scopus2-s2.0-85138717267
dc.identifier.spage173
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85138717267&doi=10.1109%2fICSSA54161.2022.9870974&partnerID=40&md5=011c27357f0ff2e0fd57f8d1ccfa847b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/27105
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle4th International Conference on Smart Sensors and Application: Digitalization for Societal Well-Being, ICSSA 2022
dc.titleReal-Time Threshold-Based Fall Detection System Using Wearable IoTen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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