@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}
}

{"_id":"GwcDRZfgXHH58KXMx","bibbaseid":"wu-daoudi-amad-sparrow-dhondt-fusionofphysiologicalandbehaviouralsignalsonspdmanifoldswithapplicationtostressandpaindetection-2022","author_short":["Wu, Y.","Daoudi, M.","Amad, A.","Sparrow, L.","D'Hondt, F."],"bibdata":{"bibtype":"article","type":"article","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":[{"propositions":[],"lastnames":["Wu"],"firstnames":["Yujin"],"suffixes":[]},{"propositions":[],"lastnames":["Daoudi"],"firstnames":["Mohamed"],"suffixes":[]},{"propositions":[],"lastnames":["Amad"],"firstnames":["Ali"],"suffixes":[]},{"propositions":[],"lastnames":["Sparrow"],"firstnames":["Laurent"],"suffixes":[]},{"propositions":[],"lastnames":["D'Hondt"],"firstnames":["Fabien"],"suffixes":[]}],"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","bibtex":"@article{Wu2022,\nabstract = {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.},\nauthor = {Wu, Yujin and Daoudi, Mohamed and Amad, Ali and Sparrow, Laurent and D'Hondt, Fabien},\ndoi = {10.1109/SMC53654.2022.9945260},\nfile = {: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},\nisbn = {9781665452588},\nissn = {1062922X},\njournal = {Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},\nkeywords = {covariance matrix,multimodal fusion,pain detection,stress detection,symmetric positive definite manifold.},\npages = {2949--2955},\ntitle = {{Fusion of Physiological and Behavioural Signals on SPD Manifolds with Application to Stress and Pain Detection}},\nvolume = {2022-Octob},\nyear = {2022}\n}\n","author_short":["Wu, Y.","Daoudi, M.","Amad, A.","Sparrow, L.","D'Hondt, F."],"key":"Wu2022","id":"Wu2022","bibbaseid":"wu-daoudi-amad-sparrow-dhondt-fusionofphysiologicalandbehaviouralsignalsonspdmanifoldswithapplicationtostressandpaindetection-2022","role":"author","urls":{},"keyword":["covariance matrix","multimodal fusion","pain detection","stress detection","symmetric positive definite manifold."],"metadata":{"authorlinks":{}},"downloads":1},"bibtype":"article","biburl":"https://nextcloud.univ-lille.fr/index.php/s/pPGQfg4JXoHoBE6/download/BIB_FDH.bib","dataSources":["F636tzkHrc3ThLeTQ","ATRkM2TLoR6tFHedP","Th2hJq5hckp8eADcs","3GW57JhuLNkW5xJr9"],"keywords":["covariance matrix","multimodal fusion","pain detection","stress detection","symmetric positive definite manifold."],"search_terms":["fusion","physiological","behavioural","signals","spd","manifolds","application","stress","pain","detection","wu","daoudi","amad","sparrow","d'hondt"],"title":"Fusion of Physiological and Behavioural Signals on SPD Manifolds with Application to Stress and Pain Detection","year":2022,"downloads":1}