Classifying Individuals with ASD Through Facial Emotion Recognition and Eye-Tracking

Jiang, Ming; Francis, Sunday M.; Srishyla, Diksha; Conelea, Christine A.; Zhao, Qi; Jacob, Suma

doi:10.1109/embc.2019.8857005

Cited by 59 publications

(35 citation statements)

References 20 publications

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“…This may be because the initial stage of viewing emotional faces is the stage at which emotions are recognized, and many studies of people with autistic characteristics and first-degree relatives with ASD demonstrated abnormalities in the emotion recognition abilities of these groups. [38][39][40] Our research is consistent with previous studies, which may indicate that parents with ASD may be able to recognize dynamic threatening emotions that are a closer representation of the real world. Once they identify threatening information, they will choose to avoid looking at the threatening information.…”

Section: Discussionsupporting

confidence: 91%

Eye Avoidance of Threatening Facial Expressions in Parents of Children with ASD

Yang¹,

Li²,

Zhang³

et al. 2021

NDT

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Previous research found that autism spectrum disorder (ASD) was characterized by eye avoidance of threatening facial expressions. However, it still remains unclear as to whether these abnormalities are present in parents of children with ASD. Our study aimed to investigate the gaze patterns of parents of children with ASD in the threatening facial expressions. Methods: Thirty-four parents of children with ASD and 35 parents of typically developing (TD) children participated in our study. We investigated the total fixation time of participants when they viewed different facial expression (eg, happy, fearful, angry, sad) videos and examined changes in the fixation duration over time. Results: We observed the following: a) the total fixation time of the parents of children with ASD on the eyes of fearful faces was significantly shorter than that of the normal group, and the difference lasted for five seconds (four to six seconds, eight to nine seconds) throughout the process; and b) The parents of children with ASD avoided the eyes of angry expression faces at around five seconds after the stimulus onset. Conclusion:We concluded that parents of children with ASD tended to avoid the eyes of threatening expression faces while viewing the dynamic emotions video.

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Section: Discussionsupporting

confidence: 91%

Eye Avoidance of Threatening Facial Expressions in Parents of Children with ASD

Yang¹,

Li²,

Zhang³

et al. 2021

NDT

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“…Additionally, a high prediction accuracy was achieved in [30] regarding social impairment and restricted, repetitive, and stereotyped behaviours and interests. On the other hand, although there was a high classification accuracy in [32], there were no differences between ASD and TD participants when emotion recognition accuracy was taken into account. Finally, Elastic net models hold promises for future research when some of the constraints encountered in [35] are overcome.…”

Section: Emotion Recognition Studiesmentioning

confidence: 88%

“…Four eye-tracking and machine learning studies which distributed emotion recognition tasks were identified. In [32] a facial emotion recognition task was employed. Random Forest was applied to classify eye fixations of ASD and TD participants according to their task performance, gaze information, and face features.…”

Section: Emotion Recognition Studiesmentioning

confidence: 99%

The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review

et al. 2021

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Early and objective autism spectrum disorder (ASD) assessment, as well as early intervention are particularly important and may have long term benefits in the lives of ASD people. ASD assessment relies on subjective rather on objective criteria, whereas advances in research point to up-to-date procedures for early ASD assessment comprising eye-tracking technology, machine learning, as well as other assessment tools. This systematic review, the first to our knowledge of its kind, provides a comprehensive discussion of 30 studies irrespective of the stimuli/tasks and dataset used, the algorithms applied, the eye-tracking tools utilised and their goals. Evidence indicates that the combination of machine learning and eye-tracking technology could be considered a promising tool in autism research regarding early and objective diagnosis. Limitations and suggestions for future research are also presented.

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“…Since the diagnosis of autism is challenging and no biomarker is available [ 51 ], the development of computational models based on early abnormalities such as the differences in gaze processing might be of substantial help to improve and anticipate the diagnosis, thus, making it possible to initiate treatment at an earlier stage, when it is most effective [ 52 ]. Eye tracking measurements that might prove to be useful as early biomarkers include dysregulations in pupil dilation [ 53 , 54 , 55 ], changes in saccadic behavior, differences in gaze patterns during vision exposure to social stimuli [ 56 , 57 , 58 ] and analysis of scan paths or gaze patterns [ 59 , 60 , 61 , 62 , 63 , 64 ]. Some studies combined eye tracking data with other measurements such as resting-state EEG data [ 65 , 66 , 67 ].…”

Section: Discussionmentioning

confidence: 99%

Application of Eye Tracking Technology in Medicine: A Bibliometric Analysis

Zammarchi

Conversano

2021

Vision

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Eye tracking provides a quantitative measure of eye movements during different activities. We report the results from a bibliometric analysis to investigate trends in eye tracking research applied to the study of different medical conditions. We conducted a search on the Web of Science Core Collection (WoS) database and analyzed the dataset of 2456 retrieved articles using VOSviewer and the Bibliometrix R package. The most represented area was psychiatry (503, 20.5%) followed by neuroscience (465, 18.9%) and psychology developmental (337, 13.7%). The annual scientific production growth was 11.14% and showed exponential growth with three main peaks in 2011, 2015 and 2017. Extensive collaboration networks were identified between the three countries with the highest scientific production, the USA (35.3%), the UK (9.5%) and Germany (7.3%). Based on term co-occurrence maps and analyses of sources of articles, we identified autism spectrum disorders as the most investigated condition and conducted specific analyses on 638 articles related to this topic which showed an annual scientific production growth of 16.52%. The majority of studies focused on autism used eye tracking to investigate gaze patterns with regards to stimuli related to social interaction. Our analysis highlights the widespread and increasing use of eye tracking in the study of different neurological and psychiatric conditions.

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Classifying Individuals with ASD Through Facial Emotion Recognition and Eye-Tracking

Cited by 59 publications

References 20 publications

Eye Avoidance of Threatening Facial Expressions in Parents of Children with ASD

Eye Avoidance of Threatening Facial Expressions in Parents of Children with ASD

The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review

Application of Eye Tracking Technology in Medicine: A Bibliometric Analysis

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