TY - GEN
T1 - A Multi-Modal Behavior Quantitative Analysis Model for Autism Early Screening
AU - Lei, Jiayi
AU - Zhang, E.
AU - She, Yingying
AU - Wang, Xin
AU - Liao, Yuhan
AU - Hu, Bin
AU - Wu, Hang
AU - Yang, Minqiang
AU - Tian, Jiajia
AU - Wang, Yong
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Human-Computer Interaction (HCI) and Machine Learning (ML) technologies have potential for the behavioral screening of autistic children but how to design a tool and analyse behavior reliably is challenging. Based on psychophysiological computation, this paper proposes an interactive behavior perception analytical model for autism screening. We presented the multi-scenario reactive behavior paradigms that designed based on the atypical characteristics of autistic children. We recorded the eye movement data and facial data of 91 participants, and performed multi-modal feature extraction, used machine learning to train classification model. We conducted comparative experiments, and the experimental results verified the advantages of multi-scenario paradigms and multi-modal feature groups, which indicates that our analysis methods and screening models are effective and reliable and have real research significance.
AB - Human-Computer Interaction (HCI) and Machine Learning (ML) technologies have potential for the behavioral screening of autistic children but how to design a tool and analyse behavior reliably is challenging. Based on psychophysiological computation, this paper proposes an interactive behavior perception analytical model for autism screening. We presented the multi-scenario reactive behavior paradigms that designed based on the atypical characteristics of autistic children. We recorded the eye movement data and facial data of 91 participants, and performed multi-modal feature extraction, used machine learning to train classification model. We conducted comparative experiments, and the experimental results verified the advantages of multi-scenario paradigms and multi-modal feature groups, which indicates that our analysis methods and screening models are effective and reliable and have real research significance.
UR - http://www.scopus.com/inward/record.url?scp=85187294353&partnerID=8YFLogxK
U2 - 10.1109/SMC53992.2023.10394247
DO - 10.1109/SMC53992.2023.10394247
M3 - Conference contribution
AN - SCOPUS:85187294353
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 5132
EP - 5139
BT - 2023 IEEE International Conference on Systems, Man, and Cybernetics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023
Y2 - 1 October 2023 through 4 October 2023
ER -