Detection for joint attention based on a multi-sensor visual system

Wanqi Zhang; Zhiyong Wang; Haibin Cai; Honghai Liu

doi:10.1109/M2VIP.2018.8600897

Detection for joint attention based on a multi-sensor visual system

Wanqi Zhang, Zhiyong Wang, Haibin Cai, Honghai Liu

Shanghai Jiao Tong University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Autism Spectrum Disorder (ASD) is one of the most common mental disorders in childhood, with a wide range and high risk. At present, there is no cure for autism. The symptoms associated with autism can be improved through early diagnosis and intervention. Joint Attention is an important paradigm in the diagnosis and intervention of ASD, the JA performance of child refers to the skill of following the eyes and fingers of others. This paper proposes an algorism based on a multi-sensor visual system, which gains the gaze of the child and transforms the Joint Attention detection into a geometric problem and proposes a solution. We conducted 20 rounds of Joint Attention testing on 10 non-ASD adults through this system and algorism, and achieved an accuracy of 97.94%.

Original language	English
Title of host publication	2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)
Editors	Peter Xu, Akos Csiszar, Alexander Verl
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	978-1-5386-7544-1
ISBN (Print)	978-1-5386-7545-8
DOIs	https://doi.org/10.1109/M2VIP.2018.8600897
Publication status	Published - 7 Jan 2019
Event	25th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2018 - Stuttgart, Germany Duration: 20 Nov 2018 → 22 Nov 2018

Conference

Conference	25th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2018
Country/Territory	Germany
City	Stuttgart
Period	20/11/18 → 22/11/18

Keywords

autism
computer vision
Joint Attention
multi-sensor

Access to Document

10.1109/M2VIP.2018.8600897

Cite this

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title = "Detection for joint attention based on a multi-sensor visual system",

abstract = "Autism Spectrum Disorder (ASD) is one of the most common mental disorders in childhood, with a wide range and high risk. At present, there is no cure for autism. The symptoms associated with autism can be improved through early diagnosis and intervention. Joint Attention is an important paradigm in the diagnosis and intervention of ASD, the JA performance of child refers to the skill of following the eyes and fingers of others. This paper proposes an algorism based on a multi-sensor visual system, which gains the gaze of the child and transforms the Joint Attention detection into a geometric problem and proposes a solution. We conducted 20 rounds of Joint Attention testing on 10 non-ASD adults through this system and algorism, and achieved an accuracy of 97.94%.",

keywords = "autism, computer vision, Joint Attention, multi-sensor",

author = "Wanqi Zhang and Zhiyong Wang and Haibin Cai and Honghai Liu",

year = "2019",

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doi = "10.1109/M2VIP.2018.8600897",

language = "English",

isbn = "978-1-5386-7545-8",

editor = "Peter Xu and Akos Csiszar and Alexander Verl",

booktitle = "2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "25th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2018 ; Conference date: 20-11-2018 Through 22-11-2018",

}

Zhang, W, Wang, Z, Cai, H & Liu, H 2019, Detection for joint attention based on a multi-sensor visual system. in P Xu, A Csiszar & A Verl (eds), 2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). Institute of Electrical and Electronics Engineers Inc., 25th International Conference on Mechatronics and Machine Vision in Practice, M2VIP 2018, Stuttgart, Germany, 20/11/18. https://doi.org/10.1109/M2VIP.2018.8600897

Detection for joint attention based on a multi-sensor visual system. / Zhang, Wanqi; Wang, Zhiyong; Cai, Haibin et al.
2018 25th International Conference on Mechatronics and Machine Vision in Practice (M2VIP). ed. / Peter Xu; Akos Csiszar; Alexander Verl. Institute of Electrical and Electronics Engineers Inc., 2019.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Detection for joint attention based on a multi-sensor visual system

AU - Zhang, Wanqi

AU - Wang, Zhiyong

AU - Cai, Haibin

AU - Liu, Honghai

PY - 2019/1/7

Y1 - 2019/1/7

N2 - Autism Spectrum Disorder (ASD) is one of the most common mental disorders in childhood, with a wide range and high risk. At present, there is no cure for autism. The symptoms associated with autism can be improved through early diagnosis and intervention. Joint Attention is an important paradigm in the diagnosis and intervention of ASD, the JA performance of child refers to the skill of following the eyes and fingers of others. This paper proposes an algorism based on a multi-sensor visual system, which gains the gaze of the child and transforms the Joint Attention detection into a geometric problem and proposes a solution. We conducted 20 rounds of Joint Attention testing on 10 non-ASD adults through this system and algorism, and achieved an accuracy of 97.94%.

AB - Autism Spectrum Disorder (ASD) is one of the most common mental disorders in childhood, with a wide range and high risk. At present, there is no cure for autism. The symptoms associated with autism can be improved through early diagnosis and intervention. Joint Attention is an important paradigm in the diagnosis and intervention of ASD, the JA performance of child refers to the skill of following the eyes and fingers of others. This paper proposes an algorism based on a multi-sensor visual system, which gains the gaze of the child and transforms the Joint Attention detection into a geometric problem and proposes a solution. We conducted 20 rounds of Joint Attention testing on 10 non-ASD adults through this system and algorism, and achieved an accuracy of 97.94%.

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KW - computer vision

KW - Joint Attention

KW - multi-sensor

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A2 - Csiszar, Akos

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Y2 - 20 November 2018 through 22 November 2018

ER -

Detection for joint attention based on a multi-sensor visual system

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Keywords

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