An architecture for capturing and synchronizing heart rate and body motion for stress inference

Lopes, Júlio Castro; Vieira, João; Van-Deste, Isaac; Lopes, Rui Pedro

http://hdl.handle.net/10198/29140

Use this identifier to reference this record.

Name:	Description:	Size:	Format:
An_Architecture_for_Capturing.pdf		987.64 KB	Adobe PDF	Download

Send Feedback

Authors

Abstract(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.

Keywords

Heart rate Stress Body motion Machine learning

URI

http://hdl.handle.net/10198/29140

Citation

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