Navigation Through Eye-Tracking for Human–Computer Interface

Pavani, Movva; Prakash, Aditya; Koushik, Mani; Amudha, J.; Jyotsna, C.

doi:10.1007/978-981-13-1747-7_56

Cited by 14 publications

(2 citation statements)

References 7 publications

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“…Eye Tracking helps to measure cognitive load, concentration, focus, drowsiness, consciousness, and other mental states [50], [59]. Technologies to track the eye have become efficient, cheap, and compact and are increasing use in many fields, including gaming, driver safety, military, education [60], product recommendation system, psychology research, cognitive studies [51], market analysis, medical research [52], [61] [62], advertising [63], and healthcare [64], [65]. [66], [67].…”

Section: A Data and Feature Engineeringmentioning

confidence: 99%

PredictEYE: Personalized Time Series Model for Mental State Prediction Using Eye Tracking

Jyotsna,

Amudha,

Ram

et al. 2023

IEEE Access

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Mental health is vital for emotional, psychological, and social well-being. Mental illness can affect thoughts, feelings, and behaviors. Early intervention and specialized care can manage major mental illnesses. Predicting mental state accurately can facilitate behavioral changes and promote overall wellbeing. The paper proposes a novel personalized time series model called PredictEYE, which aims to predict a person's mental state and identify the specific scene responsible for that mental state. The model achieves this by analyzing individuals' eye-tracking time series data while watching calm and stressful videos. The model utilizes deep learning time-series univariate regression model based on Long Short-Term Memory for predicting the future sequence of each feature and a machine learning-based Random Forest algorithm for the mental state prediction. The model's performance was compared across the state-of-the-art literature survey.The predictEYE model could achieve an accuracy of 86.4% accuracy in predicting mental state. Tailoring eye tracking models to individual differences is more effective in comprehending mental states than models that make comparisons across multiple participants, given eye tracking data's unique and distinctive idiosyncratic nature. The eye tracking features play a crucial role in predicting the mental state, and the model is adaptable to work with webcam-based eye tracking and can relate to applications where continuous and non-invasive monitoring is required.

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Section: A Data and Feature Engineeringmentioning

confidence: 99%

PredictEYE: Personalized Time Series Model for Mental State Prediction Using Eye Tracking

Jyotsna,

Amudha,

Ram

et al. 2023

IEEE Access

Self Cite

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“…Hence, fashion retailers and researchers are exploring and developing state-of-the-art recommendation models to improve the accessibility, navigability and consumers' overall purchasing experience. One of the prime elements that has been continuously researched in these articles was the improvement of existing and the development of new algorithms relevant to the filtering techniques [4,15,33,[39][40][41][42][43][44][45][46][47][48][49][50][51]. This review paper has identified state-of-the art algorithms and filtering techniques that have high potential to become more popular in the future.…”

Section: Introductionmentioning

confidence: 99%

Fashion Recommendation Systems, Models and Methods: A Review

et al. 2021

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In recent years, the textile and fashion industries have witnessed an enormous amount of growth in fast fashion. On e-commerce platforms, where numerous choices are available, an efficient recommendation system is required to sort, order, and efficiently convey relevant product content or information to users. Image-based fashion recommendation systems (FRSs) have attracted a huge amount of attention from fast fashion retailers as they provide a personalized shopping experience to consumers. With the technological advancements, this branch of artificial intelligence exhibits a tremendous amount of potential in image processing, parsing, classification, and segmentation. Despite its huge potential, the number of academic articles on this topic is limited. The available studies do not provide a rigorous review of fashion recommendation systems and the corresponding filtering techniques. To the best of the authors’ knowledge, this is the first scholarly article to review the state-of-the-art fashion recommendation systems and the corresponding filtering techniques. In addition, this review also explores various potential models that could be implemented to develop fashion recommendation systems in the future. This paper will help researchers, academics, and practitioners who are interested in machine learning, computer vision, and fashion retailing to understand the characteristics of the different fashion recommendation systems.

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