An intelligent environment to assess auditory emotional recognition

In recent years, mobile devices and applications have known a growth that is unprecedented in any other technological field, reaching virtually all aspects of our lives including sports, leisure, social relationships or health. This paper describes the development of 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 in Portugal. The secondary aim is to study behavioral features, acquired unobtrusively from the interaction of the participant with the device, in search for a relationship with medical conditions, cognitive impairments, auditory emotional recognition or socio-demographic indicators. This will establish the foundation for the prediction of such aspects based on the analysis of people's interaction with technological devices, providing new potentially interesting diagnostic tools.

[1]  José Neves,et al.  Using Mouse Dynamics to Assess Stress During Online Exams , 2015, HAIS.

[2]  Davide Carneiro,et al.  Conflict Resolution and its Context: From the Analysis of Behavioural Patterns to Efficient Decision-Making , 2014 .

[3]  José Neves,et al.  A Statistical Classifier for Assessing the Level of Stress from the Analysis of Interaction Patterns in a Touch Screen , 2012, SOCO.

[4]  José Neves,et al.  Conflict Resolution and its Context , 2014 .

[5]  José Neves,et al.  A Discomfort-Sensitive Chair for Pointing Out Mental Fatigue , 2015, ISAmI.

[6]  Carlo Curino,et al.  A data-oriented survey of context models , 2007, SGMD.

[7]  Davide Carneiro,et al.  Multimodal behavioral analysis for non-invasive stress detection , 2012, Expert Syst. Appl..

[8]  Regan L. Mandryk,et al.  Identifying emotional states using keystroke dynamics , 2011, CHI.

[9]  Lisa M. Vizer.  Detecting cognitive and physical stress through typing behavior , 2009, CHI Extended Abstracts.

[10]  Anind K. Dey,et al.  Understanding and Using Context , 2001, Personal and Ubiquitous Computing.

[11]  Fanglin Chen,et al.  StudentLife: assessing mental health, academic performance and behavioral trends of college students using smartphones , 2014, UbiComp.

[12]  Haroon Shakirat Oluwatosin.  Client-Server Model , 2014 .

[13]  Arkady B. Zaslavsky,et al.  Context Aware Computing for The Internet of Things: A Survey , 2013, IEEE Communications Surveys & Tutorials.

[14]  Mary Czerwinski,et al.  Under pressure: sensing stress of computer users , 2014, CHI.

[15]  José Neves,et al.  Detection of Distraction and Fatigue in Groups through the Analysis of Interaction Patterns with Computers , 2014, IDC.

[16]  P. Janarthanan,et al.  An Intelligent Approach to Information Retrieval System Using Enhanced DIG and FP-Tree Techniques , 2014 .