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

• URL: http://arxiv.org/abs/2207.08811v1
• Date: Sun, 17 Jul 2022 16:54:24 GMT
• Title: Fusion of Physiological and Behavioural Signals on SPD Manifolds with Application to Stress and Pain Detection
• Authors: Yujin WU, Mohamed Daoudi, Ali Amad, Laurent Sparrow, Fabien D'Hondt
• Abstract summary: multimodal stress/pain recognition approaches generally extract features from different modalities independently. This paper proposes a novel geometric framework for multimodal stress/pain detection utilizing Symmetric Positive Definite (SPD) matrices.
• Score: 1.9844265130823329
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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