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Automatic annotation of heart rate sequences
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Orientador(es)
Resumo(s)
Heart Rate (HR) measurement is one of the most
effective ways to determine whether a person is stressed or
not. The analysis of a series of HR measurements can help
determine whether the HR decreased, increased dramatically,
or remained consistent during that time period. With this in
mind, an automatic annotator that can automatically label HR
sequences, determining these three possible states, is an ideal
solution because it eliminates the need for a human to do it
manually. This paper presents a web-based application that, given
a .csv file containing Heart Rate successive measurements and
their respective time stamps, can label sequences of any size
that the user specifies. This opens up the possibility of training
Machine Learning models with this data and classifying whether
the user is in a stressful situation or not, in real time. Although
further refinements will be made, our annotator proved to be
robust and consistent in its annotation performance.
Descrição
Palavras-chave
Heart rate Machine learning Annotation Web application
Contexto Educativo
Citação
Lopes, Júlio Castro; Vieira, João; Antunes, Alexandre Fernandes; Deusdado, Leonel; Lopes, Rui Pedro (2023). Automatic annotation of heart rate sequences. In 11th International Conference on Serious Games and Applications for Health (SeGAH), Athens, Greece, 28-30 August 2023. ISBN 979-8-3503-4607-7. p. 1-6
Editora
IEEE