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An architecture for capturing and synchronizing heart rate and body motion for stress inference
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Orientador(es)
Resumo(s)
This paper aims to propose a system for capturing
and synchronizing human heart rate (HR) and body motion
(BMR) for stress inference. For this purpose, OpenPose skeletonbased
method was used, which is capable of analyzing sequential
videos, processing them frame by frame, and obtaining an
approximation to the human figure composed of 18 key points,
roughly corresponding to the joints. It is expected that by
combining these two distinct measurements, HR and BMR, a
more grounded evaluation of player stress levels while playing
a Virtual Reality (VR) game, will be achieved. The experiment
was conducted with 5 participants playing 5 different types of
games, with different levels of intensity. During the game, the
players wore a smartwatch to measure the HR and images were
captured to calculate the BMR. Future work will assess this
dataset to confirm the stress level in these 5 situations.
Descrição
Palavras-chave
Heart rate Stress Body motion Machine learning
Contexto Educativo
Citação
Lopes, Júlio Castro; Vieira, João; Van-Deste, Isaac; Lopes, Rui Pedro (2023). An architecture for capturing and synchronizing heart rate and body motion for stress inference. In 2023 IEEE 11th International Conference on Serious Games and Applications for Health (SeGAH). 28-30 August 2023, Athens. p. 1-7. ISBN 979-835034607-7
Editora
IEEE