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Issue title: Special section: Soft Computing and Intelligent Systems: Techniques and Applications
Guest editors: Sabu M. Thampi, El-Sayed M. El-Alfy and Ljiljana Trajkovic
Article type: Research Article
Authors: Jyotsna, C.a; * | Amudha, J.a | Rao, Raghavendrab | Nayar, Ravib
Affiliations: [a] Department of Computer Science & Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India | [b] Health Care Global Enterprises Ltd (HCG) Hospitals, Bengaluru, India
Correspondence: [*] Corresponding author. C. Jyotsna, Department of Computer Science & Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India. E-mail: [email protected].
Abstract: Trail making test is a cognitive impairment test used for understanding the visual attention during the visual search task. The classical paper pencil method measures the completion time of the participant and there was no mechanism for comparison across the participant with similar feature. The psychologist has to observe the reactions of the participants during the trial process and there is no mechanism to capture it. This study made an attempt to resolve the above problem and tried to infer additional parameters which can support psychologist to understand the participant performance in trail making test. The insight provided by the approach is to extract various features which helps a psychologist by providing individual profiling and group profiling of a person and can understand the group of people who show similar cognitive impairment while performing trail Making Test. The proposed Intelligent Gaze Tracking approach could classify the participant into three different groups like low, high and medium cognitive impairment based on the extracted gaze features. The proposed approach has been compared across existing literature survey to significantly show the advantage of the system in terms of identifying the people with similar characteristics in terms of cognitive impairment.
Keywords: Eye tracking, cognitive impairment, trail making test, area of interest, scanpath, fixation, adaptive neuro fuzzy inference system, k-means clustering
DOI: 10.3233/JIFS-179711
Journal: Journal of Intelligent & Fuzzy Systems, vol. 38, no. 5, pp. 6299-6310, 2020
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