Attention based hybrid deep learning model for wearable based stress recognition

Simple item page
AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-10-05T16:33:14Z
Fecha de publicación2024-01-01
ResumenStress recognition is the process of identifying and assessing an individual's physiological and psychological responses to stressors, which has significant implications for human well-being. Artificial intelligence plays a pivotal role in stress diagnosis by leveraging advanced algorithms to analyse complex physiological data and uncover subtle stress indicators. Previous studies have predominantly employed conventional methods for stress recognition, such as handcrafted features and machine learning algorithms, but these approaches may lack precision and robustness. The proposed method in this research addresses these limitations through a hybrid deep learning model with an attention mechanism, enabling comprehensive feature extraction and dynamic information prioritization. This novel approach enhances stress recognition by accurately fusing multiple physiological modalities and capturing both short-term and long-term patterns associated with stress. The input to the proposed model are physiological signals such as electrocardiogram (ECG) and electrodermal activity (EDA), which are normalized and preprocessed. Afterwards, hybrid CNN-LSTM model leverages the strengths of both convolutional and recurrent networks, enabling it to extract features from the preprocessed data. Then the inclusion of an attention mechanism layer further enhances the model's capability to dynamically weigh and prioritize features from different modalities, enhancing the model's ability to capture key stress-related patterns. Finally, the proposed model involves utilizing the trained model to categorize stress based on the fused features and attention-weighted inputs, achieving improved performance. Experimental findings showcase the effectiveness of the proposed model, achieving an accuracy of 92.70\% and a weighted F1-score of 90\%. These results outperform the CNN, CNN-LSTM, and decision-based fusion methods.es_ES
Doihttps://doi.org/10.1016/j.engappai.2023.107391es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/5080
Idiomaenes_ES
EditorialPERGAMON-ELSEVIER SCIENCE LTDes_ES
RelaciónEngineering Applications of Artificial Intelligencees_ES
URL relacionadohttps://doi.org/10.1016/j.engappai.2023.107391es_ES
DerechosAcceso restringido / Suscripción (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_16eces_ES
FuenteEngineering Applications of Artificial Intelligence
Palabra claveStress recognitiones_ES
Palabra claveMultimodal physiological dataes_ES
Palabra claveHybrid deep learninges_ES
Palabra claveAttentiones_ES
TítuloAttention based hybrid deep learning model for wearable based stress recognitiones_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorTanwar, Ritu
AutorPhukan, Orchid Chetia
AutorSingh, Ghanapriya
AutorPal, Pankaj Kumar
AutorTiwari, Sanju
AutorTanwar, Ritues_ES
AutorPhukan, Orchid Chetiaes_ES
AutorSingh, Ghanapriyaes_ES
AutorPal, Pankaj Kumares_ES
AutorTiwari, Sanjues_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
NúmeroBes_ES
URL relacionadahttps://doi.org/10.1016/j.engappai.2023.107391
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen127es_ES

Files

Collections

Documentación Cientifica