Authors:
Phil Lopes
1
;
Nuno Fachada
2
;
Micaela Fonseca
1
;
Hugo Gamboa
3
and
Claudia Quaresma
3
Affiliations:
1
Lusofona University, HEI-Lab, Campo Grande, 376, 1749-024 Lisboa, Portugal
;
2
Lusofona University, COPELABS, Campo Grande, 376, 1749-024 Lisboa, Portugal
;
3
LIBPhys, NOVA School of Science and Technology, NOVA University Lisbon, 2829-516 Caparica, Portugal
Keyword(s):
Virtual Reality, Data Processing, Biofeedback, Machine Learning.
Abstract:
This position paper proposes the hypothesis that physiological noise artefacts can be classified based on the type of movements performed by participants in Virtual Reality contexts. To assess this hypothesis, a detailed research plan is proposed to study the influence of movement on the quality of the captured physiological signals. This paper argues that the proposed plan can produce a valid model for classifying noisy physiological signal features, providing insights into the influence of movement on artefacts, while contributing to the development of movement-based filters and the implementation of best practices for using various associated technologies.