Show simple item record

AuthorAlban, Ahmad Qadeib
AuthorAlhaddad, Ahmad Yaser
AuthorAl-Ali, Abdulaziz
AuthorSo, Wing Chee
AuthorConnor, Olcay
AuthorAyesh, Malek
AuthorAhmed Qidwai, Uvais
AuthorCabibihan, John John
Available date2023-11-21T10:37:24Z
Publication Date2023-04-01
Publication NameRobotics
Identifierhttp://dx.doi.org/10.3390/robotics12020055
CitationAlban, A. Q., Alhaddad, A. Y., Al-Ali, A., So, W. C., Connor, O., Ayesh, M., ... & Cabibihan, J. J. (2023). Heart Rate as a Predictor of Challenging Behaviours among Children with Autism from Wearable Sensors in Social Robot Interactions. Robotics, 12(2), 55.‏
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85153765430&origin=inward
URIhttp://hdl.handle.net/10576/49566
AbstractChildren with autism face challenges in various skills (e.g., communication and social) and they exhibit challenging behaviours. These challenging behaviours represent a challenge to their families, therapists, and caregivers, especially during therapy sessions. In this study, we have investigated several machine learning techniques and data modalities acquired using wearable sensors from children with autism during their interactions with social robots and toys in their potential to detect challenging behaviours. Each child wore a wearable device that collected data. Video annotations of the sessions were used to identify the occurrence of challenging behaviours. Extracted time features (i.e., mean, standard deviation, min, and max) in conjunction with four machine learning techniques were considered to detect challenging behaviors. The heart rate variability (HRV) changes have also been investigated in this study. The XGBoost algorithm has achieved the best performance (i.e., an accuracy of 99%). Additionally, physiological features outperformed the kinetic ones, with the heart rate being the main contributing feature in the prediction performance. One HRV parameter (i.e., RMSSD) was found to correlate with the occurrence of challenging behaviours. This work highlights the importance of developing the tools and methods to detect challenging behaviors among children with autism during aided sessions with social robots.
Languageen
PublisherMDPI
Subjectautism
challenging behaviours
machine learning
social robots
wearables
TitleHeart Rate as a Predictor of Challenging Behaviours among Children with Autism from Wearable Sensors in Social Robot Interactions
TypeArticle
Issue Number2
Volume Number12
dc.accessType Open Access


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record