Monitoring Dementia with Automatic Eye Movements Analysis

Zhang, Yanxia; Wilcockson, Thomas; Kim, Kwang In; Crawford, Trevor J.; Gellersen, Hans; Sawyer, Pete

doi:10.1007/978-3-319-39627-9_26

Cited by 14 publications

(12 citation statements)

References 20 publications

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“…Indeed, cognitive processes can be observed through eye movements and offer a wealth of information related to internal processes (Itti, 2015;Coutrot, Hsiao, & Chan, 2018). Inference from gaze data consists in deducing subjective characteristics solely from ocular data, such as age (Le Meur et al, 2017b), gender (Coutrot, Binetti, Harrison, Mareschal, & Johnston, 2016;Sammaknejad, Pouretemad, Eslahchi, Salahirad, & Alinejad, 2017), mental states and traits (Liao, Zhang, Zhu, & Ji, 2005;Hoppe, Loetscher, Morey, & Bulling, 2015;Yamada & Kobayashi, 2017;Hoppe, Loetscher, Morey, & Bulling, 2018), expertise and skill proficiency (Eivazi & Bednarik, 2011;Boccignone, Ferraro, Crespi, Robino, & de'Sperati, 2014;Tien et al, 2014;Kolodziej, Majkowski, Francuz, Rak, & Augustynowicz, 2018), and neurological disorders (Kupas, Harangi, Czifra, & Andrassy, 2017;Terao, Fukuda, & Hikosaka, 2017).It has proven useful in identifying autism spectrum disorder (Pierce et al, 2016), fetal alcohol spectrum disorder (Tseng, Paolozza, Munoz, Reynolds, & Itti, 2013), dementia (Zhang et al, 2016;Beltrán, García-Vázquez, Benois-Pineau, Gutierrez-Robledo, & Dartigues, 2018), dyslexia (Benfatto et al, 2016), anxiety (Abbott, Shirali, Haws, & Lack, 2017), mental fatigue (Yamada & Kobayashi, 2017), and other disorders. It has also been applied to task detection (Borji & Itti, 2014;Haji-Abolhassani & Clark, 2014;Kanan, Ray, Bseiso, Hsiao, & Cottrell, 2014;Boisvert & Bruce, 2016).…”

Section: Inference From Gaze Datamentioning

confidence: 99%

Predicting artificial visual field losses: A gaze-based inference study

et al. 2019

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Visual field defects are a worldwide concern, and the proportion of the population experiencing vision loss is ever increasing. Macular degeneration and glaucoma are among the four leading causes of permanent vision loss. Identifying and characterizing visual field losses from gaze alone could prove crucial in the future for screening tests, rehabilitation therapies, and monitoring. In this experiment, 54 participants took part in a free-viewing task of visual scenes while experiencing artificial scotomas (central and peripheral) of varying radii in a gaze-contingent paradigm. We studied the importance of a set of gaze features as predictors to best differentiate between artificial scotoma conditions. Linear mixed models were utilized to measure differences between scotoma conditions. Correlation and factorial analyses revealed redundancies in our data. Finally, hidden Markov models and recurrent neural networks were implemented as classifiers in order to measure the predictive usefulness of gaze features. The results show separate saccade direction biases depending on scotoma type. We demonstrate that the saccade relative angle, amplitude, and peak velocity of saccades are the best features on the basis of which to distinguish between artificial scotomas in a free-viewing task. Finally, we discuss the usefulness of our protocol and analyses as a gaze-feature identifier tool that discriminates between artificial scotomas of different types and sizes.

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Section: Inference From Gaze Datamentioning

confidence: 99%

Predicting artificial visual field losses: A gaze-based inference study

et al. 2019

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“…These characteristics are such as fixation duration, re-fixations, saccade orientation, pupil diameter smooth, pursuit movements and saccadic inhibition [10,26]. They have also designed experiments to detect the cognitive impairment by eye movements [27][28][29]. However, most of the related investigations are carried out in dementia patients and the investigations in MCI patients are insufficient [10].…”

Section: Detecting the Cognitive Impairment With Eye Movementmentioning

confidence: 99%

“…Yanxia et al worked on the linkage between the cognitive situations obtained from the cognitive tests and the eye movement patterns collected when the participants were watching videos. They tried to find the specific eye movement patterns which were related to the cognitive impairment and then used these eye movement patterns for the automatic cognitive assessment [27]. From the experiments, they found that the participants with the cognitive impairment would have slower saccade motion and longer fixation time on average.…”

Section: Detecting the Cognitive Impairment With Eye Movementmentioning

confidence: 99%

A Survey of the State-of-the-Art Techniques for Cognitive Impairment Detection in the Elderly

Fei

Yang

et al. 2017

Communications in Computer and Information Science

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With a growing number of elderly people in the UK, more and more of them suffer from various kinds of cognitive impairment. Cognitive impairment can be divided into different stages such as mild cognitive impairment (MCI) and severe cognitive impairment like dementia. Its early detection can be of great importance. However, it is challenging to detect cognitive impairment in the early stage with high accuracy and low cost, when most of the symptoms may not be fully expressed. This survey paper mainly reviews the state of the art techniques for the early detection of cognitive impairment and compares their advantages and weaknesses. In order to build an effective and low-cost automatic system for detecting and monitoring the cognitive impairment for a wide range of elderly people, the applications of computer vision techniques for the early detection of cognitive impairment by monitoring facial expressions, body movements and eye movements are highlighted in this paper. In additional to technique review, the main research challenges for the early detection of cognitive impairment with high accuracy and low cost are analysed in depth. Through carefully comparing and contrasting the currently popular techniques for their advantages and weaknesses, some important research directions are particularly pointed out and highlighted from the viewpoints of the authors alone.

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“…The gaze avoidance of participants with social anxiety disorder was proportional to their anxiety and fear of the stimuli [ 18 ]. In another study, MCI or dementia patients watched videos according to some given conditions, and it proved possible to infer cognitive functions by recording and analyzing the participants’ eye movements and observing the changes in their gaze patterns [ 19 ]. One research study developed a model that can detect the symptoms of depression using various features, such as head pose, facial expression, eye gaze, and audio features.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Facial and Eye Movements to Screen for Alzheimer’s Disease

Nam

Lee

et al. 2020

Sensors

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Brain disease can be screened using eye movements. Degenerative brain disorders change eye movement because they affect not only memory and cognition but also the cranial nervous system involved in eye movement. We compared the facial and eye movement patterns of patients with mild Alzheimer’s disease and cognitively normal people to analyze the neurological signs of dementia. After detecting the facial landmarks, the coordinate values for the movements were extracted. We used Spearman’s correlation coefficient to examine associations between horizontal and vertical facial and eye movements. We analyzed the correlation between facial and eye movements without using special eye-tracking equipment or complex conditions in order to measure the behavioral aspect of the natural human gaze. As a result, we found differences between patients with Alzheimer’s disease and cognitively normal people. Patients suffering from Alzheimer’s disease tended to move their face and eyes simultaneously in the vertical direction, whereas the cognitively normal people did not, as confirmed by a Mann–Whitney–Wilcoxon test. Our findings suggest that objective and accurate measurement of facial and eye movements can be used to screen such patients quickly. The use of camera-based testing for the early detection of patients showing signs of neurodegeneration can have a significant impact on the public care of dementia.

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Monitoring Dementia with Automatic Eye Movements Analysis

Cited by 14 publications

References 20 publications

Predicting artificial visual field losses: A gaze-based inference study

Predicting artificial visual field losses: A gaze-based inference study

A Survey of the State-of-the-Art Techniques for Cognitive Impairment Detection in the Elderly

Analyzing Facial and Eye Movements to Screen for Alzheimer’s Disease

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