Details

Autor(en) / Beteiligte

• Alban, Ahmad Qadeib
• Ayesh, Malek
• Alhaddad, Ahmad Yaser
• Khalid Al-Ali, Abdulaziz
• So, Wing Chee
• Connor, Olcay
• Cabibihan, John-John

Titel

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

Ist Teil von

• 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 2021, p.852-857

Ort / Verlag

IEEE

Erscheinungsjahr

2021

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• 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.