Non-generalized Analysis of the Multimodal Signals for Emotion Recognition: Preliminary Results. Londoño-Delgado, E., Becerra, M., A., Duque-Mejía, C., M., Zapata, J., C., Mejía-Arboleda, C., Castro-Ospina, A., E., & Peluffo-Ordóñez, D., H. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 11466 LNBI, pages 363-373, 2019. Springer Verlag.
Non-generalized Analysis of the Multimodal Signals for Emotion Recognition: Preliminary Results [link]Website  doi  abstract   bibtex   1 download  
Emotions are mental states associated with some stimuli, and they have a relevant impact on the people living and are correlated with their physical and mental health. Different studies have been carried out focused on emotion identification considering that there is a universal fingerprint of the emotions. However, this is an open field yet, and some authors had refused such proposal which is contrasted with many results which can be considered as no conclusive despite some of them have achieved high results of performances for identifying some emotions. In this work an analysis of identification of emotions per individual based on physiological signals using the known MAHNOB-HCI-TAGGING database is carried out, considering that there is not a universal fingerprint based on the results achieved by a previous meta-analytic investigation of emotion categories. The methodology applied is depicted as follows: first the signals were filtered and normalized and decomposed in five bands (δ, θ, α, β, γ ), then a features extraction stage was carried out using multiple statistical measures calculated of results achieved after applied discrete wavelet transform, Cepstral coefficients, among others. A feature space dimensional reduction was applied using the selection algorithm relief F. Finally, the classification was carried out using support vector machine, and k-nearest neighbors and its performance analysis was measured using 10 folds cross-validation achieving high performance uppon to 99%.
@inproceedings{
 title = {Non-generalized Analysis of the Multimodal Signals for Emotion Recognition: Preliminary Results},
 type = {inproceedings},
 year = {2019},
 keywords = {Emotion recognition,Physiological signals,Signal processing},
 pages = {363-373},
 volume = {11466 LNBI},
 websites = {https://link.springer.com/chapter/10.1007%2F978-3-030-17935-9_33},
 publisher = {Springer Verlag},
 id = {bed0ce78-e514-305b-a533-98b50f15dfe4},
 created = {2022-01-26T03:00:05.427Z},
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 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
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 last_modified = {2022-01-26T03:00:05.427Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
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 abstract = {Emotions are mental states associated with some stimuli, and they have a relevant impact on the people living and are correlated with their physical and mental health. Different studies have been carried out focused on emotion identification considering that there is a universal fingerprint of the emotions. However, this is an open field yet, and some authors had refused such proposal which is contrasted with many results which can be considered as no conclusive despite some of them have achieved high results of performances for identifying some emotions. In this work an analysis of identification of emotions per individual based on physiological signals using the known MAHNOB-HCI-TAGGING database is carried out, considering that there is not a universal fingerprint based on the results achieved by a previous meta-analytic investigation of emotion categories. The methodology applied is depicted as follows: first the signals were filtered and normalized and decomposed in five bands (δ, θ, α, β, γ ), then a features extraction stage was carried out using multiple statistical measures calculated of results achieved after applied discrete wavelet transform, Cepstral coefficients, among others. A feature space dimensional reduction was applied using the selection algorithm relief F. Finally, the classification was carried out using support vector machine, and k-nearest neighbors and its performance analysis was measured using 10 folds cross-validation achieving high performance uppon to 99%.},
 bibtype = {inproceedings},
 author = {Londoño-Delgado, Edwin and Becerra, Miguel Alberto and Duque-Mejía, Carolina M. and Zapata, Juan Camilo and Mejía-Arboleda, Cristian and Castro-Ospina, Andrés Eduardo and Peluffo-Ordóñez, Diego Hernán},
 doi = {10.1007/978-3-030-17935-9_33},
 booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}
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