An Automated Stress Recognition for Digital Healthcare: Towards E-Governance

Simple item page
AudienciaPúblico en generales_ES
CoberturaMéxico[ 143]es_ES
Fecha de ingreso2026-10-05T16:35:01Z
Fecha de publicación2022-01-01
ResumenMental health is of utmost importance in present times as mental health problems can have a negative impact on an individual. Stress recognition is an important part of the digital healthcare system as stress may act as a catalyst and lead to mental health problems or further amplify them. With the advancement of technology, the presence of smart wearable devices is seen and it can be used to automate stress recognition for digital healthcare. These smart wearable devices have physiological sensors embedded into them. The data collected from these physiological sensors have paved an efficient way for stress recognition in the user. Most of the previous work related to stress recognition was done using classical machine learning approaches. One of the major drawbacks related to these approaches is that they require manually extracting important features that will be helpful in stress recognition. Extracting these features requires human domain expertise. Another drawback of previous works was that it only caters to specific groups of individuals such as stress among youths, stress due to the workplace, etc. and fails to generalize. To overcome the issues related to previous works done, this study proposes a transformer-based deep learning approach for automating the feature extraction phase and classifying a user’s state into three classes baseline, stress, and amusement.es_ES
Doihttps://doi.org/10.1007/978-3-031-22950-3_10es_ES
ISBN978-303122949-7es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/6484
IdiomaInglés[20]es_ES
EditorialSpringer Science and Business Media Deutschland GmbHes_ES
RelaciónCommunications in Computer and Information Sciencees_ES
URL relacionadohttps://doi.org/10.1007/978-3-031-22950-3_10es_ES
DerechosAcceso restringido / Suscripción (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_16eces_ES
FuenteCommunications in Computer and Information Science
Palabra claveDeep learninges_ES
Palabra claveMental healthes_ES
Palabra claveStress recognitiones_ES
Palabra claveTransformeres_ES
Palabra claveWESADes_ES
TítuloAn Automated Stress Recognition for Digital Healthcare: Towards E-Governancees_ES
TipoPonenciaes_ES
ArbitradoHa sido Arbitradoes_ES
AutorPhukan, Orchid Chetia
AutorSingh, Ghanapriya
AutorTiwari, Sanju
AutorButt, Saad
AutorPhukan, Orchid Chetiaes_ES
AutorSingh, Ghanapriyaes_ES
AutorTiwari, Sanjues_ES
AutorButt, Saades_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Rango de páginas117-125es_ES
URL relacionadahttps://doi.org/10.1007/978-3-031-22950-3_10
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen1666 CCISes_ES

Files

Collections

Documentación Cientifica