Stress Identification from Electrodermal Activity by Support Vector Machines

  • Roberto Sánchez-Reolid
  • Arturo Martínez-Rodrigo
  • Antonio Fernández-CaballeroEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11486)


Continuous atmosphere of competitiveness, job pressure, economic status and social judgment in modern societies leads many people to a frenetic life rhythm, thus favoring the appearance of stress. Consequently, early detection of calm and negative stress is useful to prevent long-term mental illness as depression or anxiety. This paper describes the acquisition of electrodermal activity (EDA) signals from a commercial wearable, and their storage and processing. Several time-domain, frequency-domain and morphological features are extracted over the skin conductance response component of the EDA signals. Afterwards, classification is undergone by using several support vector machines (SVMs). The International Affective Pictures System has been used to evoke calmness and distress to validate the classification results. The best results obtained during training and validation for each of the SVMs report around 87.7% accuracy for Gaussian and cubic kernels.


Electrodermal activity Support vector machines Calmness Distress 



This work has been partially supported by Spanish Ministerio de Ciencia, Innovación y Universidades, Agencia Estatal de Investigación (AEI)/European Regional Development Fund (FEDER, UE) under DPI2016-80894-R grant, and by CIBERSAM of the Instituto de Salud Carlos III. Roberto Sánchez-Reolid holds BES-2017-081958 scholarship from Spanish Ministerio de Educación y Formación Profesional. Arturo Martínez-Rodrigo holds 2018/11744 grant from European Regional Development Fund (FEDER, UE).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Roberto Sánchez-Reolid
    • 1
    • 2
  • Arturo Martínez-Rodrigo
    • 1
    • 3
  • Antonio Fernández-Caballero
    • 1
    • 2
    • 4
    Email author
  1. 1.Departamento de Sistemas InformáticosUniversidad de Castilla-La ManchaAlbaceteSpain
  2. 2.Instituto de Investigación en Informática de AlbaceteUniversidad de Castilla-La ManchaAlbaceteSpain
  3. 3.Instituto de Tecnologías AudiovisualesUniversidad de Castilla-La ManchaCuencaSpain
  4. 4.CIBERSAM (Biomedical Research Networking Centre in Mental Health)MadridSpain

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