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Prediction of the Attention Area in Ambient Intelligence Tasks

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Innovative Issues in Intelligent Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 623))

Abstract

With recent advances in Ambient Intelligence (AmI), it is becoming possible to provide support to a human in an AmI environment. This paper presents an Adaptive Neuro-Fuzzy Inference System (ANFIS) model based scheme, named as prediction of the attention area using ANFIS (PAA_ANFIS), which predicts the human attention area on visual display with ordinary web camera. The PAA_ANFIS model was designed using trial and error based on various experiments in simulated gaming environment. This study was conducted to illustrate that ANFIS is effective with hybrid learning, for the prediction of eye-gaze area in the environment. PAA_ANFIS results show that ANFIS has been successfully implemented for predicting within different learning context scenarios in a simulated environment. The performance of the PAA_ANFIS model was evaluated using standard error measurements techniques. The Matlab® simulation results indicate that the performance of the ANFIS approach is valuable, accurate and easy to implement. The PAA_ANFIS results are based on analysis of different model settings in our environment. To further validate the PAA_ANFIS, forecasting results are then compared with linear regression. The comparative results show the superiority and higher accuracy achieved by applying the ANFIS, which is equipped with the capability of generating linear relationship and the fuzzy inference system in input-output data. However, it should be noted that an increase in the number of membership functions (MF) will increase the system response time.

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Acknowledgments

We gratefully acknowledge Prof. Dr. Plamen Angelov (leads the intelligent System Research Group at School of Computing and Communications Lancaster University) for providing valuable comments and suggestion during span of this research work, without which we have been unable to complete this research.

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Authors and Affiliations

  1. Data Science Group, School of Computer and Communication, Lancaster Univeristy, Lancaster, UK

    Jawad Shafi & Plamen Angelov

  2. Chair of Excellence, Carlos III University, Madrid, Spain

    Plamen Angelov

  3. Department of Computer Sciences, COMSATS Institute of Information Technology, Lahore, Pakistan

    Jawad Shafi & Muhammad Umair

Authors
  1. Jawad Shafi
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  2. Plamen Angelov
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  3. Muhammad Umair
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Correspondence to Jawad Shafi .

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Editors and Affiliations

  1. Inst of Info & Commu Tech, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Vassil Sgurev

  2. Iona College, Machine Intelligence Institute, New Rochelle, New York, USA

    Ronald R. Yager

  3. Sys Res Intit of Polish Acad of Science, Intelligent Systems Laboratory, Warsaw, Poland

    Janusz Kacprzyk

  4. and Information Technologies, University of Library Studies, Sofia, Bulgaria

    Vladimir Jotsov

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Shafi, J., Angelov, P., Umair, M. (2016). Prediction of the Attention Area in Ambient Intelligence Tasks. In: Sgurev, V., Yager, R., Kacprzyk, J., Jotsov, V. (eds) Innovative Issues in Intelligent Systems. Studies in Computational Intelligence, vol 623. Springer, Cham. https://doi.org/10.1007/978-3-319-27267-2_2

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  • DOI: https://doi.org/10.1007/978-3-319-27267-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27266-5

  • Online ISBN: 978-3-319-27267-2

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