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dc.contributor.authorLoh Mei, Yee
dc.contributor.authorLim Chee, Chin
dc.contributor.authorChong Yen, Fook
dc.contributor.authorMaslia, Dali
dc.contributor.authorShafriza Nisha, Basah
dc.date.accessioned2020-12-16T08:33:27Z
dc.date.available2020-12-16T08:33:27Z
dc.date.issued2019
dc.identifier.citationJournal of Physics: Conference Series, vol.1372, 2019, 8 pagesen_US
dc.identifier.issn1742-6588 (print)
dc.identifier.issn1742-6596 (online)
dc.identifier.urihttp://dspace.unimap.edu.my:80/xmlui/handle/123456789/69030
dc.descriptionLink to publisher's homepage at https://iopscience.iop.org/issue/1742-6596/1372/1en_US
dc.description.abstractThe IoT fall detection system detects the fall through the data classification of falling and daily living activity. It includes microcontroller board (Arduino Mega 2560), Inertial Measurement Unit sensor (Gy-521 mpu6050) and WI-FI module (ESP8266-01). There total ten (10) subjects in this project. The data of falling and non-falling (daily living activity) can be identified. The falling is the frontward fall, while the daily living activity includes standing, sitting, walking and crouching. K-nearest neighbour (k-NN) classifiers were used in the data classification. The accuracy of k-NN classifiers were 100% between falling and nonfalling class. The feature was selected based on the percentage of accuracy of the k-NN classifier. The features of the Aareal.z (97.14%) and Angle.x (97.24%) were selected due to the good performance during the classification of the falling and non-falling class. The performance of the Aareal.z (58.41%) and Angle.x (57.78%) were satisfactory during the subclassification of the non-falling class. Hence, the feature of Aareal.z and Angle.x were selected as the features which were implemented in the IoT fall detection device.en_US
dc.language.isoenen_US
dc.publisherIOP Publishingen_US
dc.relation.ispartofseriesInternational Conference on Biomedical Engineering (ICoBE);
dc.subjectInternet of Things (IoT)en_US
dc.subjectSensoren_US
dc.subjectWearable sensoren_US
dc.titleInternet of Things (IoT) Fall Detection using Wearable Sensoren_US
dc.typeArticleen_US
dc.identifier.urlhttps://iopscience.iop.org/issue/1742-6596/1372/1
dc.contributor.urlcclim@unimap.edu.myen_US


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