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Communication Dans Un Congrès Année : 2022

Fusion of Physiological and Behavioural Signals on SPD Manifolds with Application to Stress and Pain Detection

Résumé

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-ofthe-art results for stress and pain detection tasks.
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Dates et versions

hal-03883660 , version 1 (03-12-2022)

Identifiants

  • HAL Id : hal-03883660 , version 1

Citer

Yujin Wu, Mohamed Daoudi, Ali Amad, Laurent Sparrow, D Fabien. Fusion of Physiological and Behavioural Signals on SPD Manifolds with Application to Stress and Pain Detection. International Conference on Systems, Man, and Cybernetics, Oct 2022, Prague, Czech Republic. ⟨hal-03883660⟩
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