Detection of Challenging Behaviours of Children with Autism Using Wearable Sensors during Interactions with Social Robots

被引:15
作者
Alban, Ahmad Qadeib [1 ]
Ayesh, Malek [1 ]
Alhaddad, Ahmad Yaser [2 ]
Al-Ali, Abdulaziz Khalid [1 ,3 ]
So, Wing Chee [4 ]
Connor, Olcay [5 ]
Cabibihan, John-John [2 ]
机构
[1] Qatar Univ, Dept Comp Sci & Engn, Doha, Qatar
[2] Qatar Univ, Dept Mech & Ind Engn, Doha, Qatar
[3] Qatar Univ, Coll Engn, KINDI Comp Res Ctr, Doha, Qatar
[4] Chinese Univ Hong Kong, Dept Educ Psychol, Fac Educ, Hong Kong, Peoples R China
[5] Step Step Ctr Special Needs, Doha, Qatar
来源
2021 30TH IEEE INTERNATIONAL CONFERENCE ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (RO-MAN) | 2021年
关键词
SPECTRUM DISORDER; RESPONSES;
D O I
10.1109/RO-MAN50785.2021.9515459
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Autism spectrum disorder is a neurodevelopmental disorder that is characterized by patterns of behaviours and difficulties with social communication and interaction. Children on the spectrum exhibit atypical, restricted, repetitive, and challenging behaviours. In this study, we investigate the feasibility of integrating wearable sensors and machine learning techniques to detect the occurrence of challenging behaviours in real-time. A session of a child with autism interacting with different stimuli groups that included social robots was annotated with observed challenging behaviors. The child wore a wearable device that captured different motion and physiological signals. Different features and machine learning configurations were investigated to identify the most effective combination. Our results showed that physiological signals in addition to typical kinetic measures led to more accurate predictions. The best features and learning model combination achieved an accuracy of 97%. The findings of this work motivate research toward methods of early detection of challenging behaviours, which may enable the timely intervention by caregivers and possibly by social robots.
引用
收藏
页码:852 / 857
页数:6
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