Cargando…
The Design of CNN Architectures for Optimal Six Basic Emotion Classification Using Multiple Physiological Signals
This study aimed to design an optimal emotion recognition method using multiple physiological signal parameters acquired by bio-signal sensors for improving the accuracy of classifying individual emotional responses. Multiple physiological signals such as respiration (RSP) and heart rate variability...
Autores principales: | Oh, SeungJun, Lee, Jun-Young, Kim, Dong Keun |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7038703/ https://www.ncbi.nlm.nih.gov/pubmed/32041226 http://dx.doi.org/10.3390/s20030866 |
Ejemplares similares
-
A novel hybrid transformer-CNN architecture for environmental microorganism classification
por: Shao, Ran, et al.
Publicado: (2022) -
Multiple Brain Tumor Classification with Dense CNN Architecture Using Brain MRI Images
por: Özkaraca, Osman, et al.
Publicado: (2023) -
A CNN-LSTM model for six human ankle movements classification on different loads
por: Li, Min, et al.
Publicado: (2023) -
Optimizing Deep CNN Architectures for Face Liveness Detection
por: Koshy, Ranjana, et al.
Publicado: (2019) -
Signals classification based on IA-optimal CNN
por: Zhang, Yalun, et al.
Publicado: (2021)