Human activity detection using smart wearable sensing devices with feed forward neural networks and PSO

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dc.contributor.authorAl Hassani, Raghad Tariq
dc.contributor.authorAtilla, Doğu Çağdaş
dc.date.accessioned2023-04-17T06:58:20Z
dc.date.available2023-04-17T06:58:20Z
dc.date.issued2023en_US
dc.departmentEnstitüler, Lisansüstü Eğitim Enstitüsü, Elektrik ve Bilgisayar Mühendisliği Ana Bilim Dalıen_US
dc.description.abstractHospitals must continually monitor their patients’ actions to lower the chance of accidents, such as patient falls and slides. Human behavior is difficult to track due to the complexity of human activities and the unpredictable nature of their conduct. As a result, creating a static link that is used to influence human behavior is challenging, since it is hard to forecast how individuals will think or act in response to a certain event. Mobility tracking depends on intelligent monitoring systems that apply artificial intelligence (AI) applications referred to as “categories”. Because motion sensors, such as gyroscopes and accelerometers, output unconnected data that lack labels, event detection is a vital task. The fall feature parameters of tridimensional accelerometers and gyroscope sensors are presented and used, and the classification technique is based on distinguishing characteristics. This study focuses on the age-old problem of tracking turbulence in motion to improve detection precision. We trained the model, considering that detection accuracy is limited by factors such as the subject’s mass, velocity, and gait style. This is performed by employing an experimental dataset. When we used the sophisticated technique of particle swarm optimization (PSO) in combination with a four-stage forward neural network (4SFNN) to forecast four different types of turbulent motion, we observed that the total prediction accuracy was 98.615% accurate.en_US
dc.identifier.citationAl_Hassani, R. T., Atilla, D. C. (2023). Human activity detection using smart wearable sensing devices with feed forward neural networks and PSO. Applied Sciences, 13(6), 3716.en_US
dc.identifier.issn2076-3417
dc.identifier.issue6en_US
dc.identifier.scopus2-s2.0-85152070038
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://hdl.handle.net/20.500.12939/3471
dc.identifier.volume13en_US
dc.identifier.wosWOS:000954162000001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorAl Hassani, Raghad Tariq
dc.institutionauthorAtilla, Doğu Çağdaş
dc.language.isoen
dc.relation.ispartofApplied Sciences-Basel
dc.relation.isversionof10.3390/app13063716en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - İdari Personel ve Öğrencien_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject4SFNNen_US
dc.subjectPSOen_US
dc.subjectAECen_US
dc.subjectPredictionen_US
dc.subjectAccuracyen_US
dc.subjectMAEen_US
dc.subjectFallingen_US
dc.subjectMSEen_US
dc.subjectSDLen_US
dc.subjectFOLen_US
dc.subjectFKLen_US
dc.titleHuman activity detection using smart wearable sensing devices with feed forward neural networks and PSO
dc.typeArticle

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