Detection of involuntary movement with wearable technology

dc.contributor.authorKoseoglu, Yasin
dc.contributor.authorBoyaci, Ali
dc.date.accessioned2023-01-19T13:07:13Z
dc.date.available2023-01-19T13:07:13Z
dc.date.issued2022en_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractNowadays, wearable accelerometers have improved the popularity of smartwatches, and physical activity measurements. This article aims to use a smartwatch as a stimulant against hair pulling obsession (Trichotillomania). Data were collected for 4 hours with a user who pulls out his beard at indefinite intervals, and machine learning models were applied to the collected data. A Watch X is preferred as a programmable watch. This smartwatch is programmed to create an alert when the user makes the beard plucking action. Based on this, it is aimed to prevent Trichotillomania obsession by applying positive punishment with an alert (stimulant). CNN and LSTM models were compared to find the most suitable model and it was seen that LSTM had better accuracy than CNN. However, in terms of speed performance, CNN gave better results. As a result of all comparisons, the LSTM model was observed as the most suitable model with an accuracy rate of 91%.en_US
dc.identifier.doi10.1109/HORA55278.2022.9800040en_US
dc.identifier.scopus2-s2.0-85133965099en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://hdl.handle.net/11467/6100
dc.identifier.urihttps://doi.org/10.1109/HORA55278.2022.9800040
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofHORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedingsen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectAccelerometer; Bluetooth; CNN; LSTM; Smart Watch; Trichotillomania; Wearable Technologyen_US
dc.titleDetection of involuntary movement with wearable technologyen_US
dc.typeConference Objecten_US

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