Context acquisition in auditory emotional recognition studies

  • Davide CarneiroEmail author
  • Ana P. Pinheiro
  • Paulo Novais
Original Research


This paper describes an environment to assess auditory emotional recognition based on a mobile application. The primary aim of this work is to provide a valuable instrument that can be used both in research and clinical settings, responding to the strong need of validated measures of emotional processing, especially in Portugal. The secondary aim is to acquire and study the participants’ interaction behavior with the technological device (e.g. touch patterns, touch intensity), in search for a relationship with medical conditions, cognitive impairments, auditory emotional recognition capacities or socio-demographic indicators. This will establish the basis for the prediction of such aspects as a function of an individual’s interaction with technological devices, potentially providing new diagnostic tools.


Ambient intelligence Soft sensors Behavioral biometrics Auditory emotional recognition 



This work has been supported by FCT-Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013 and Grant PTDC/MHN-PCN/3606/2012. The work of Davide Carneiro is supported by a post-doctoral Grant by FCT (SFRH/BPD/ 109070/2015). The work of Ana P. Pinheiro is supported by FCT Investigator Grant IF/00334/2012 funded by Fundação para a Ciência e a Tecnologia (FCT, Portugal).


  1. Bolchini C, Curino CA, Quintarelli E, Schreiber FA, Tanca L (2007) A data-oriented survey of context models. ACM Sigmod Record 36(4):19–26CrossRefGoogle Scholar
  2. Carneiro D, Novais P, Gomes M, Oliveira PM, Neves J (2013) A statistical classifier for assessing the level of stress from the analysis of interaction patterns in a touch screen. Soft computing models in industrial and environmental applications, Springer, pp 257–266Google Scholar
  3. Carneiro D, Novais P, Neves J (2014) Conflict resolution and Its context: from the analysis of behavioural patterns to efficient decision-making. Law, Governance and Technology Series, vol 18. SpringerGoogle Scholar
  4. Carneiro D, Novais P, Pêgo JM, Sousa N, Neves J (2015) Using mouse dynamics to assess stress during online exams. Hybrid Artif Intell Syst, Springer, pp 345–356Google Scholar
  5. Castillo Montoya JC, Novais P, Fernandez Caballero A, Carneiro D (2012) Multimodal behavioural analysis for non-invasive stress detection. Expert Syst Appl 39(18):13376–13389CrossRefGoogle Scholar
  6. Dey AK (2001) Understanding and using context. Personal ubiquitous Comput 5(1):4–7CrossRefGoogle Scholar
  7. Epp C, Lippold M, Mandryk RL (2011) Identifying emotional states using keystroke dynamics. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, ACM, pp 715–724Google Scholar
  8. Hernandez J, Paredes P, Roseway A, Czerwinski M ( 2014) Under pressure: sensing stress of computer users. Proceedings of the SIGCHI conference on Human factors in computing systems, ACM, pp 51–60Google Scholar
  9. Jo-Anne Bachorowski (1999) Vocal expression and perception of emotion. Curr Dir Psychol Sci 8(2):53–57CrossRefGoogle Scholar
  10. Oluwatosin HS (2014) Client-server model. IOSR J Comput Eng (IOSR-JCE) 16(1):67Google Scholar
  11. Patrik PN, Laukka P (2003) Communication of emotions in vocal expression and music performance: different channels, same code? Psychol Bull 129(5):770CrossRefGoogle Scholar
  12. Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Context aware computing for the internet of things: a survey. Commun Surv Tutor IEEE 16(1):414–454CrossRefGoogle Scholar
  13. Pimenta A, Carneiro D, Novais P, Neves J (2015) Detection of distraction and fatigue in groups through the analysis of interaction patterns with computers. Intell Distrib Comput VIII, Springer, pp 29–39Google Scholar
  14. Pimenta A, Carneiro D, Novais P, Neves J (2015) A discomfort-sensitive chair for pointing out mental fatigue. Ambient Intell Softw Appl, Springer, pp 57–64Google Scholar
  15. Vizer LM (2009) Detecting cognitive and physical stress through typing behavior. CHI’09 Extended Abstracts on Human Factors in Computing Systems, ACM, pp 3113–3116Google Scholar
  16. Wang R, Chen F, Chen Z, Li T, Harari G, Tignor S, Zhou X, Ben-Zeev D, Campbell AT (2014) Studentlife: assessing mental health, academic performance and behavioral trends of college students using smartphones. Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, ACM, pp 3–14Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.CIICESI, ESTGFPolytechnic Institute of PortoFelgueirasPortugal
  2. 2.Algoritmi Centre/Department of InformaticsUniversity of MinhoBragaPortugal
  3. 3.School of PsychologyUniversity of MinhoBragaPortugal
  4. 4.Faculty of PsychologyUniversity of LisbonLisbonPortugal

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