Advertisement

Non-intrusive Monitoring of Attentional Behavior in Teams

  • Davide CarneiroEmail author
  • Dalila Durães
  • Javier Bajo
  • Paulo Novais
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 678)

Abstract

Attention is a very important cognitive and behavioral process, by means of which an individual is able to focus on a single aspect of information, while ignoring others. In a time in which we are drawn in notifications, beeps, vibrations and blinking messages, the ability to focus becomes increasingly important. This is true in many different domains, from the workplace to the classroom. In this paper we present a non-intrusive distributed system for monitoring attention in teams of people. It is especially suited for teams working at the computer. The presented system is able to provide real-time information about each individual as well as information about the team. It can be very useful for team managers to identify potentially distracting events or individuals, as well as to detect the onset of mental fatigue or boredom, which significantly influence attention. In the overall, this tool may prove very useful for team managers to implement better human resources management strategies.

Keywords

Attentional Behavior Non-intrusive Monitoring Distributed Computing 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Estes, W.K.: Handbook of Learning and Cognitive Processes (Volume 4): Attention and Memory. Psychology Press (2014)Google Scholar
  2. 2.
    Davenport, T.H., Beck, J.C.: The attention economy: Understanding the new currency of business. Harvard Business Press (2013)Google Scholar
  3. 3.
    ATTENTION-DEFICIT, S.O., et al.: Adhd: clinical practice guideline for the diagnosis, evaluation, and treatment of attention-deficit/hyperactivity disorder in children and adolescents. Pediatrics (2011) peds–2011Google Scholar
  4. 4.
    McBride, D.L.: Distraction of clinicians by smartphones in hospitals: a concept analysis. Journal of advanced nursing 71(9) (2015) 2020–2030Google Scholar
  5. 5.
    Simola, J., Hyönä, J., Kuisma, J.: Perception of visual advertising in different media: from attention to distraction, persuasion, preference and memory. Frontiers Media SA (2015)Google Scholar
  6. 6.
    Gottlieb, J.: Attention, learning, and the value of information. Neuron 76(2) (2012) 281–295Google Scholar
  7. 7.
    Augusto, J.C., Callaghan, V., Cook, D., Kameas, A., Satoh, I.: Intelligent environments: a manifesto. Human-Centric Computing and Information Sciences 3(1) (2013) 1–18Google Scholar
  8. 8.
    Carneiro, D., Novais, P., Pêgo, J.M., Sousa, N., Neves, J.: Using mouse dynamics to assess stress during online exams. In: Hybrid Artificial Intelligent Systems. Springer (2015) 345–356Google Scholar
  9. 9.
    Pimenta, A., Carneiro, D., Novais, P., Neves, J.: Monitoring mental fatigue through the analysis of keyboard and mouse interaction patterns. In: Hybrid Artificial Intelligent Systems. Springer (2013) 222–231Google Scholar
  10. 10.
    Pimenta, A., Carneiro, D., Novais, P., Neves, J.: Detection of distraction and fatigue in groups through the analysis of interaction patterns with computers. In: Intelligent Distributed Computing VIII. Springer (2015) 29–39Google Scholar
  11. 11.
    Carneiro, D., Pimenta, A., Gonçalves, S., Neves, J., Novais, P.: Monitoring and improving performance in human–computer interaction. Concurrency and Computation: Practice and Experience (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Davide Carneiro
    • 1
    • 3
    Email author
  • Dalila Durães
    • 2
  • Javier Bajo
    • 2
  • Paulo Novais
    • 3
  1. 1.CIICESI, ESTGFPolytechnic Institute of PortoPortoPortugal
  2. 2.Department of Artificial IntelligenceTechnical University of MadridMadridSpain
  3. 3.Algoritmi Center/Department of InformaticsUniversity of MinhoBragaPortugal

Personalised recommendations