@article{Wu2022,
abstract = {Existing multimodal stress/pain recognition approaches generally extract features from different modalities independently and thus ignore cross-modality correlations. This paper proposes a novel geometric framework for multimodal stress/pain detection utilizing Symmetric Positive Definite (SPD) matrices as a representation that incorporates the correlation relationship of physiological and behavioural signals from covariance and cross-covariance. Considering the non-linearity of the Riemannian manifold of SPD matrices, well-known machine learning techniques are not suited to classify these matrices. Therefore, a tangent space mapping method is adopted to map the derived SPD matrix sequences to the vector sequences in the tangent space where the LSTM-based network can be applied for classification. The proposed framework has been evaluated on two public multimodal datasets, achieving both the state-of-the-art results for stress and pain detection tasks.},
author = {Wu, Yujin and Daoudi, Mohamed and Amad, Ali and Sparrow, Laurent and D'Hondt, Fabien},
doi = {10.1109/SMC53654.2022.9945260},
file = {:C\:/Users/fabie/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Wu et al. - 2022 - Fusion of Physiological and Behavioural Signals on SPD Manifolds with Application to Stress and Pain Detection(2).pdf:pdf},
isbn = {9781665452588},
issn = {1062922X},
journal = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
keywords = {covariance matrix,multimodal fusion,pain detection,stress detection,symmetric positive definite manifold.},
pages = {2949--2955},
title = {{Fusion of Physiological and Behavioural Signals on SPD Manifolds with Application to Stress and Pain Detection}},
volume = {2022-Octob},
year = {2022}
}