Automatic annotation of heart rate sequences

Lopes, Júlio Castro; Vieira, João; Antunes, Alexandre Fernandes; Deusdado, Leonel; Lopes, Rui Pedro

http://hdl.handle.net/10198/29005

Use this identifier to reference this record.

Name:	Description:	Size:	Format:
Automatic_Annotation_of_Heart_Rate_Sequences.pdf		647.65 KB	Adobe PDF	Download

Send Feedback

Authors

Lopes, Júlio Castro

Vieira, João

Antunes, Alexandre Fernandes

Deusdado, Leonel

Lopes, Rui Pedro

Abstract(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.

Keywords

Heart rate Machine learning Annotation Web application

URI

http://hdl.handle.net/10198/29005

Citation

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