Automatic Annotation of Heart Rate Sequences
Conference Paper
Overview
Research
Identity
Additional Document Info
View All
Overview
abstract
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.
This work is funded by the European Regional Development
Fund (ERDF) through the Regional Operational Program
North 2020, within the scope of Project GreenHealth - Digital
strategies in biological assets to improve well-being and
promote green health, Norte-01-0145-FEDER-000042. This
work has been supported by FCT - Fundação para a Ciência
e Tecnologia within the Project Scope: UIDB/05757/2020.
