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Prediction of the attention area in ambient intelligence tasks

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Publication date3/02/2016
Host publicationInnovative issues in intelligent systems
EditorsVassil Sgurev, Ronald Yager, Janusz Kacprzyk, Vladimir Jotsov
Place of PublicationBerlin
Number of pages24
ISBN (Electronic)9783319272665
ISBN (Print)9783319272665
<mark>Original language</mark>English

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X


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.