Analysis of eye gaze pattern of infants at risk of autism spectrum disorder using Markov Models

David Alie, Mohammad H. Mahoor, Whitney I. Mattson, Daniel R. Anderson, Daniel S Messinger

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

This paper presents the possibility of using pattern recognition algorithms of infant gaze patterns at six months of age among children at high risk for an autism spectrum disorder (ASD). ASDs, which must be diagnosed by 3 years of age, are characterized by communication and interaction impairments which frequently involve disturbances of visual attention and gaze patterning. We used video cameras to record the face-to-face interactions of 32 infant subjects with their parents. The video was manually coded to determine the eye gaze pattern of infants by marking where the infant was looking in each frame (either at their parent's face or away from their parent's face). In order to identify infants ASD diagnosis at three years, we analyzed infant eye gaze patterns at six months. Variable-order Markov Models (VMM) were used to create models for typically developing comparison children as well as children with an ASD. The models correctly classified infants who did and did not develop an ASD diagnosis with an accuracy rate of 93.75 percent. Employing an assessment tool at a very young age offers the hope of early intervention, potentially mitigating the effects of the disorder throughout the rest of the child's life.

Original languageEnglish
Title of host publication2011 IEEE Workshop on Applications of Computer Vision, WACV 2011
Pages282-287
Number of pages6
DOIs
StatePublished - Mar 16 2011
Event2011 IEEE Workshop on Applications of Computer Vision, WACV 2011 - Kona, HI, United States
Duration: Jan 5 2011Jan 7 2011

Other

Other2011 IEEE Workshop on Applications of Computer Vision, WACV 2011
CountryUnited States
CityKona, HI
Period1/5/111/7/11

Fingerprint

Video cameras
Pattern recognition
Communication

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Alie, D., Mahoor, M. H., Mattson, W. I., Anderson, D. R., & Messinger, D. S. (2011). Analysis of eye gaze pattern of infants at risk of autism spectrum disorder using Markov Models. In 2011 IEEE Workshop on Applications of Computer Vision, WACV 2011 (pp. 282-287). [5711515] https://doi.org/10.1109/WACV.2011.5711515

Analysis of eye gaze pattern of infants at risk of autism spectrum disorder using Markov Models. / Alie, David; Mahoor, Mohammad H.; Mattson, Whitney I.; Anderson, Daniel R.; Messinger, Daniel S.

2011 IEEE Workshop on Applications of Computer Vision, WACV 2011. 2011. p. 282-287 5711515.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Alie, D, Mahoor, MH, Mattson, WI, Anderson, DR & Messinger, DS 2011, Analysis of eye gaze pattern of infants at risk of autism spectrum disorder using Markov Models. in 2011 IEEE Workshop on Applications of Computer Vision, WACV 2011., 5711515, pp. 282-287, 2011 IEEE Workshop on Applications of Computer Vision, WACV 2011, Kona, HI, United States, 1/5/11. https://doi.org/10.1109/WACV.2011.5711515
Alie D, Mahoor MH, Mattson WI, Anderson DR, Messinger DS. Analysis of eye gaze pattern of infants at risk of autism spectrum disorder using Markov Models. In 2011 IEEE Workshop on Applications of Computer Vision, WACV 2011. 2011. p. 282-287. 5711515 https://doi.org/10.1109/WACV.2011.5711515
Alie, David ; Mahoor, Mohammad H. ; Mattson, Whitney I. ; Anderson, Daniel R. ; Messinger, Daniel S. / Analysis of eye gaze pattern of infants at risk of autism spectrum disorder using Markov Models. 2011 IEEE Workshop on Applications of Computer Vision, WACV 2011. 2011. pp. 282-287
@inproceedings{7b96a94c171b4507a64daf047eda4072,
title = "Analysis of eye gaze pattern of infants at risk of autism spectrum disorder using Markov Models",
abstract = "This paper presents the possibility of using pattern recognition algorithms of infant gaze patterns at six months of age among children at high risk for an autism spectrum disorder (ASD). ASDs, which must be diagnosed by 3 years of age, are characterized by communication and interaction impairments which frequently involve disturbances of visual attention and gaze patterning. We used video cameras to record the face-to-face interactions of 32 infant subjects with their parents. The video was manually coded to determine the eye gaze pattern of infants by marking where the infant was looking in each frame (either at their parent's face or away from their parent's face). In order to identify infants ASD diagnosis at three years, we analyzed infant eye gaze patterns at six months. Variable-order Markov Models (VMM) were used to create models for typically developing comparison children as well as children with an ASD. The models correctly classified infants who did and did not develop an ASD diagnosis with an accuracy rate of 93.75 percent. Employing an assessment tool at a very young age offers the hope of early intervention, potentially mitigating the effects of the disorder throughout the rest of the child's life.",
author = "David Alie and Mahoor, {Mohammad H.} and Mattson, {Whitney I.} and Anderson, {Daniel R.} and Messinger, {Daniel S}",
year = "2011",
month = "3",
day = "16",
doi = "10.1109/WACV.2011.5711515",
language = "English",
isbn = "9781424494965",
pages = "282--287",
booktitle = "2011 IEEE Workshop on Applications of Computer Vision, WACV 2011",

}

TY - GEN

T1 - Analysis of eye gaze pattern of infants at risk of autism spectrum disorder using Markov Models

AU - Alie, David

AU - Mahoor, Mohammad H.

AU - Mattson, Whitney I.

AU - Anderson, Daniel R.

AU - Messinger, Daniel S

PY - 2011/3/16

Y1 - 2011/3/16

N2 - This paper presents the possibility of using pattern recognition algorithms of infant gaze patterns at six months of age among children at high risk for an autism spectrum disorder (ASD). ASDs, which must be diagnosed by 3 years of age, are characterized by communication and interaction impairments which frequently involve disturbances of visual attention and gaze patterning. We used video cameras to record the face-to-face interactions of 32 infant subjects with their parents. The video was manually coded to determine the eye gaze pattern of infants by marking where the infant was looking in each frame (either at their parent's face or away from their parent's face). In order to identify infants ASD diagnosis at three years, we analyzed infant eye gaze patterns at six months. Variable-order Markov Models (VMM) were used to create models for typically developing comparison children as well as children with an ASD. The models correctly classified infants who did and did not develop an ASD diagnosis with an accuracy rate of 93.75 percent. Employing an assessment tool at a very young age offers the hope of early intervention, potentially mitigating the effects of the disorder throughout the rest of the child's life.

AB - This paper presents the possibility of using pattern recognition algorithms of infant gaze patterns at six months of age among children at high risk for an autism spectrum disorder (ASD). ASDs, which must be diagnosed by 3 years of age, are characterized by communication and interaction impairments which frequently involve disturbances of visual attention and gaze patterning. We used video cameras to record the face-to-face interactions of 32 infant subjects with their parents. The video was manually coded to determine the eye gaze pattern of infants by marking where the infant was looking in each frame (either at their parent's face or away from their parent's face). In order to identify infants ASD diagnosis at three years, we analyzed infant eye gaze patterns at six months. Variable-order Markov Models (VMM) were used to create models for typically developing comparison children as well as children with an ASD. The models correctly classified infants who did and did not develop an ASD diagnosis with an accuracy rate of 93.75 percent. Employing an assessment tool at a very young age offers the hope of early intervention, potentially mitigating the effects of the disorder throughout the rest of the child's life.

UR - http://www.scopus.com/inward/record.url?scp=79952520659&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=79952520659&partnerID=8YFLogxK

U2 - 10.1109/WACV.2011.5711515

DO - 10.1109/WACV.2011.5711515

M3 - Conference contribution

SN - 9781424494965

SP - 282

EP - 287

BT - 2011 IEEE Workshop on Applications of Computer Vision, WACV 2011

ER -