@inproceedings{d4f848ddebe64898976251794ed7911c,
title = "Detection of Involuntary Movement with Wearable Technology",
abstract = "Nowadays, 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%.",
keywords = "Accelerometer, Bluetooth, CNN, LSTM, Smart Watch, Trichotillomania, Wearable Technology",
author = "Yasin Koseoglu and Ali Boyaci",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2022 ; Conference date: 09-06-2022 Through 11-06-2022",
year = "2022",
doi = "10.1109/HORA55278.2022.9800040",
language = "English",
series = "HORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "HORA 2022 - 4th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings",
}