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Title: Classification of anxiety based on EDA and HR
Author: Sebastião, Raquel
Keywords: Anxiety
Physiological data
Heart rate
Eletrodermal activity
Wearable measurements
Mobile applications
Issue Date: 2021
Publisher: Springer
Abstract: This work presents anxiety classification using physiological data, namely, EDA (eletrodermal activity) and HR (heart rate), collected with a sensing wrist-wearable device during a neutral baseline state condition. For this purpose, the WESAD public available dataset was used. The baseline condition was collected for around 20 min on 15 participants. Afterwards, to assess anxiety scores, the shortened 6-item STAI was filled by the participants. Using train and test sets with 70% and 30% of data, respectively, the proposed ensemble of 100 bagged classification trees obtained an overall accuracy of 95.7%. This, along with the high precision and recall obtained, reveal the good performance of the proposed classifier and support the ability of anxiety score classification using physiological data. Such a classification task can be integrated in a mobile application presenting coping strategies to deal and manage anxiety.
Peer review: yes
DOI: 10.1007/978-3-030-69963-5_8
ISBN: 978-3-030-69962-8
Publisher Version:
Appears in Collections:DETI - Capítulo de livro
IEETA - Capítulo de livro

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