MLGaze: Machine Learning-Based Analysis of Gaze Error Patterns in Consumer Eye Tracking Systems

Analyzing the gaze accuracy characteristics of an eye tracker is a critical task as its gaze data is frequently affected by non-ideal operating conditions in various consumer eye tracking applications. In this study, gaze error patterns produced by a commercial eye tracking device were studied with the help of machine learning algorithms, such as classifiers and regression models. Gaze data were collected from a group of participants under multiple conditions that commonly affect eye trackers operating on desktop and handheld platforms. These conditions (referred here as error sources) include user distance, head pose, and eye-tracker pose variations, and the collected gaze data were used to train the classifier and regression models. It was seen that while the impact of the different error sources on gaze data characteristics were nearly impossible to distinguish by visual inspection or from data statistics, machine learning models were successful in identifying the impact of the different error sources and predicting the variability in gaze error levels due to these conditions. The objective of this study was to investigate the efficacy of machine learning methods towards the detection and prediction of gaze error patterns, which would enable an in-depth understanding of the data quality and reliability of eye trackers under unconstrained operating conditions. Coding resources for all the machine learning methods adopted in this study were included in an open repository named MLGaze to allow researchers to replicate the principles presented here using data from their own eye trackers.Comment: https://github.com/anuradhakar49/MLGaz

A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms

In this paper a review is presented of the research on eye gaze estimation techniques and applications, that has progressed in diverse ways over the past two decades. Several generic eye gaze use-cases are identified: desktop, TV, head-mounted, automotive and handheld devices. Analysis of the literature leads to the identification of several platform specific factors that influence gaze tracking accuracy. A key outcome from this review is the realization of a need to develop standardized methodologies for performance evaluation of gaze tracking systems and achieve consistency in their specification and comparative evaluation. To address this need, the concept of a methodological framework for practical evaluation of different gaze tracking systems is proposed.Comment: 25 pages, 13 figures, Accepted for publication in IEEE Access in July 201

Clinical Correlates of Hepatitis B or Hepatitis C Coinfections in People Living with HIV/AIDS (PLHIV)

Introduction: Hepatitis B virus (HBV) coinfected HIV patients are likely to have chronic hepatitis B infection and associated severe liver disease, however effect of hepatitis B on HIV has not been proven to be off any effect. Similarly in HIV/HCV co-infection majority of the studies have shown no significant influenceof hepatitis C on the course of HIV infection, although some studies have demonstrated an association between HCV infection and faster HIV disease progression.14,15 Therefore, further studies are needed to study the impact of HBV/HCV co-infection on course of HIV, specially, in India.Aims and Objectives: To study the clinical, biochemical and immunological profile of PLHIV co-infected with either hepatitis B or hepatitis C virus, the severity of liver disease and hepatitis B and hepatitis C viral loads in these co-infected PLHIV and the association of WHO stage of HIV and immunosuppression withhepatitis B and hepatitis C viral loads as well as severity of liver disease.Method: It was an observational cross-sectional study, involving 30 PLHIV co-infected with either hepatitis B or C. A detailed history and physical examination was done. Complete Haemogram, Liver function tests, kidney function tests, Ultrasonography abdomen, CD4 cell counts, hepatitis B surface antigen (HBsAg),hepatitis B envelope antigen (HBeAg), hepatitis B Viral DNA (HBV DNA) and HCV RNA levels were done. Severity of liver disease was assessed by FIB 4 SCORE.Results: Among the 30 PLHIV subjects 30% were co-infected with HCV 70% were co-infected with HBV (HBsAg positive). All the subjects were asymptomatic for their liver disease. All the subjects were on Anti-Retroviral Therapy (ART) and 80% were in Early WHO stage (T1 and T2) and 20% were in Advanced WHO stage (T3 and T4). It was similar in both HBV and HCV co-infected group. The mean CD4 count of the subjects was 416.70±189.50 cells/mm3 with the range of 69 – 909 cells/mm3. Five subjects (16.67%) had a CD4 count 3.25). In HCV co-infected subjects 3 of 9 (33.33%) had severe liver fibrosis and only 1 of 21 (4.7%) among HBV co-infected had severe liver fibrosis.Among the 9 HCV co-infected subjects, 3 (33.33%) had undetectable HCV RNA. More number of subjects with detectable hepatitis C viral load had severe liver disease as compared to undetectable viral load.In HIV and HBV co-infected subjects the HBeAg positivity was seen in 42.86% subjects and 38.1% subjects had detectable HBV DNA load. Significant correlation was seen between HBeAg positivity and HBV DNA load. No correlation could be found between FIB 4 score and hepatitis B envelope antigen (HBeAg) positivity or HBV DNA load.No correlation between severity of liver disease (FIB 4) score and WHO staging or CD4 count could be seen. WHO staging and CD4 count also did not correlated with HCV RNA load, HBeAg positivity and HBV DNA load.Conclusions: There is no correlation of CD4 count and WHO stage with liver disease severity or hepatitis viral load in patients on HAART. In HIV and HBV co-infected patients high prevalence of HBeAg positivity is seen. Thus it becomes important to look for deranged liver enzymes and HBeAg positivity in PLHIV coinfected with hepatitis B so that ART can be initiated in these patients irrespective of CD4 count. Hepatitis C co-infected subjects are more likely to have severe liver disease inspite of good CD4 count, so specific treatment for hepatitis C virus should be considered

Depression and suicidal behavior in South Asia: a systematic review and meta-analysis

Abstract Background Estimates of depression in suicidal behavior in South Asia would help to formulate suicide prevention strategies in the region that hasn't been assessed yet. Objectives We aimed to systematically assess the prevalence of depression in fatal and non-fatal attempts of suicide in eight South Asian countries. Methods We searched Medline, Embase, and PsychINFO by specific search terms to identify articles assessing depression in fatal and non-fatal attempts of suicide in South Asian countries published between 2001 and 2020. Two separate meta-analyses were conducted for fatal and non-fatal attempts. Due to the high heterogeneity of studies (96–98%), random-effects models were used to calculate pooled prevalence rates. Results A total of 38 studies was identified from five south Asian countries (India [27], Pakistan [6], Sri Lanka [3], Nepal [1], and Bangladesh [1]). The majority of studies (n = 27) were published after 2010. Twenty-two studies reported non-fatal attempts, and sixteen reported suicide. The prevalence of depression among non-fatal attempts ranged from 14% to 78% where the pooled prevalence rate was 32.7% [95% CI 26–39.3%]. The prevalence of depression among suicides ranged from 8% to 79% where the pooled prevalence estimate was 37.3% [95% CI 26.9–47.6%]. Conclusions This review revealed the pooled prevalence of depression among fatal and non-fatal suicidal attempts in South Asian countries, which seems to be lower when comparedto the Western countries. However, a cautious interpretation is warranted due to the heterogeneity of study methods, sample size, and measurement of depression

Genetic diversity and differentiation among populations of the Indian eri silkworm, Samia cynthia ricini, revealed by ISSR markers

Samia cynthia ricini (Lepidoptera:Saturniidae), the Indian eri silkworm, contributes significantly to the production of commercial silk and is widely distributed in the Brahmaputra river valley in North-Eastern India. Due to over exploitation coupled with rapid deforestation, most of the natural populations of S. cynthia ricini are dwindling rapidly and its preservation has become an important goal. Assessment of the genetic structure of each population is a prerequisite for a sustainable conservation program. DNA fingerprinting to detect genetic variation has been used in different insect species not only between populations, but also between individuals within a population. Since, information on the genetic basis of phenotypic variability and genetic diversity within the S. cynthia ricini populations is scanty, inter simple sequence repeat (ISSR) system was used to assess genetic diversity and differentiation among six commercially exploited S. cynthia ricini populations. Twenty ISSR primers produced 87% of inter population variability among the six populations. Genetic distance was lowest between the populations Khanapara (E5) and Mendipathar (E6) (0.0654) and highest between Dhanubhanga (E4) and Titabar (E3) (0.3811). Within population, heterozygosity was higher in Borduar (E2) (0.1093) and lowest in Titabar (E3) (0.0510). Highest gene flow (0.9035) was between E5 and E6 and the lowest (0.2172) was between E3 and E5. Regression analysis showed positive correlation between genetic distance and geographic distance among the populations. The high GST value (0.657) among the populations combined with low gene flow contributes significantly to the genetic differentiation among the S. cynthia ricini populations. Based on genetic diversity, these populations can be considered as different ecotypes and in situ conservation of them is recommended

GazeVisual: A practical software tool and web application for performance evaluation of eye tracking systems

The concept and functionalities of a software tool developed for in depth performance evaluation of eye gaze estimation systems is presented. The software, GazeVisual has capabilities for quantitative, statistical, and visual analysis of eye gaze data as well as generation of static and dynamic visual stimuli for sample gaze data collection. This is a first of its kind cross-platform tool for gaze data analysis and evaluation. This software is made freely available to the eye gaze research and development community to provide a common framework for estimating the quality and reliability of data from eye tracking systems, especially those implemented in consumer electronics (CE) applications. The feasibility of using this software is tested through case studies which show that the software can handle eye gaze datasets obtained from several different consumer grade eye trackers. GazeVisual operates consistently, irrespective of the platform, algorithm or hardware of the eye trackers. In addition, the GazeVisual software capabilities are also made accessible via a Web-based application enabling performance evaluation of eye tracker data over a cloud-based platform.peer-reviewe

Design and development of a performance evaluation framework for remote eye gaze estimation systems

In this dissertation, a comprehensive evaluation framework for remote eye gaze estimation systems that are implemented in consumer electronics applications is developed. For this, firstly, a detailed literature review was made which helped to gain deep insights about the current state-of-the-art in eye gaze estimation algorithms and applications, by categorizing eye gaze research works into different consumer use cases. The wide range of existing gaze estimation algorithms were classified and their applications in interdisciplinary areas such as human computer interactions, cognitive studies and consumer electronics platforms like automotive, handheld devices, augmented and virtual reality were summarised. The review further identified the major challenges faced by contemporary remote gaze estimation systems, which include variable operating conditions such as user distance from tracker, viewing angle, head pose and platform movements that have significant impact on a gaze tracker’s performance. Other issues include deficit of common evaluation methodologies, standard metrics or any comprehensive tools or software which may be used for quantitatively evaluating gaze data quality and studying impact of the various challenging operating conditions on gaze estimation accuracy. Based on the outcomes of this review, the concept of a dedicated performance evaluation framework for remote eye gaze estimation systems was formulated. This framework was implemented in this thesis work through the following steps: a) defining new experimental protocols for collection of data from a remote eye tracker operating under several challenging operating conditions b) collection of gaze data from a number of participants using a commercial remote eye tracker under variable operating conditions c) development of a set of numerical metrics and visualization methods using the collected data to express gaze tracking accuracy in homogeneous units and quantitatively explore gaze data characteristics and quality d) implementing machine learning models using the collected gaze datasets to identify and predict error patterns produced in gaze data by different operating conditions e) development of a software and web-application that incorporates the developed metrics and visualization methods into user-friendly graphical interfaces f) creation of open source code and data repositories containing the performance evaluation tools and methods developed in this thesis, so that they can be used by researchers and engineers working with remote gaze estimation systems. The aim of this dissertation is to present a set of methods, data, tools and algorithms as analytical resources for the eye gaze research community to use for better understanding of eye tracking data quality, detection of anomalous gaze data and prediction of possible error levels under various operating conditions of an eye tracker. Overall, these methods are envisioned to improve the quality and reliability of eye tracking systems operating under practical and challenging scenarios in current and future consumer applications

Design and development of a performance evaluation framework for remote eye gaze estimation systems

In this dissertation, a comprehensive evaluation framework for remote eye gaze estimation systems that are implemented in consumer electronics applications is developed. For this, firstly, a detailed literature review was made which helped to gain deep insights about the current state-of-the-art in eye gaze estimation algorithms and applications, by categorizing eye gaze research works into different consumer use cases. The wide range of existing gaze estimation algorithms were classified and their applications in interdisciplinary areas such as human computer interactions, cognitive studies and consumer electronics platforms like automotive, handheld devices, augmented and virtual reality were summarised. The review further identified the major challenges faced by contemporary remote gaze estimation systems, which include variable operating conditions such as user distance from tracker, viewing angle, head pose and platform movements that have significant impact on a gaze tracker’s performance. Other issues include deficit of common evaluation methodologies, standard metrics or any comprehensive tools or software which may be used for quantitatively evaluating gaze data quality and studying impact of the various challenging operating conditions on gaze estimation accuracy. Based on the outcomes of this review, the concept of a dedicated performance evaluation framework for remote eye gaze estimation systems was formulated. This framework was implemented in this thesis work through the following steps: a) defining new experimental protocols for collection of data from a remote eye tracker operating under several challenging operating conditions b) collection of gaze data from a number of participants using a commercial remote eye tracker under variable operating conditions c) development of a set of numerical metrics and visualization methods using the collected data to express gaze tracking accuracy in homogeneous units and quantitatively explore gaze data characteristics and quality d) implementing machine learning models using the collected gaze datasets to identify and predict error patterns produced in gaze data by different operating conditions e) development of a software and web-application that incorporates the developed metrics and visualization methods into user-friendly graphical interfaces f) creation of open source code and data repositories containing the performance evaluation tools and methods developed in this thesis, so that they can be used by researchers and engineers working with remote gaze estimation systems. The aim of this dissertation is to present a set of methods, data, tools and algorithms as analytical resources for the eye gaze research community to use for better understanding of eye tracking data quality, detection of anomalous gaze data and prediction of possible error levels under various operating conditions of an eye tracker. Overall, these methods are envisioned to improve the quality and reliability of eye tracking systems operating under practical and challenging scenarios in current and future consumer applications

Development of Open-source Software and Gaze Data Repositories for Performance Evaluation of Eye Tracking Systems

In this paper, a range of open-source tools, datasets, and software that have been developed for quantitative and in-depth evaluation of eye gaze data quality are presented. Eye tracking systems in contemporary vision research and applications face major challenges due to variable operating conditions such as user distance, head pose, and movements of the eye tracker platform. However, there is a lack of open-source tools and datasets that could be used for quantitatively evaluating an eye tracker’s data quality, comparing performance of multiple trackers, or studying the impact of various operating conditions on a tracker’s accuracy. To address these issues, an open-source code repository named GazeVisual-Lib is developed that contains a number of algorithms, visualizations, and software tools for detailed and quantitative analysis of an eye tracker’s performance and data quality. In addition, a new labelled eye gaze dataset that is collected from multiple user platforms and operating conditions is presented in an open data repository for benchmark comparison of gaze data from different eye tracking systems. The paper presents the concept, development, and organization of these two repositories that are envisioned to improve the performance analysis and reliability of eye tracking systems