TY - GEN
T1 - Attention Assessment in Children with Autism Using Head Pose and Motion Parameters from Real Videos
AU - Varghese, Elizabeth B.
AU - Qaraqe, Marwa
AU - Al Thani, Dena
AU - Ekenel, Hazim Kemal
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In children with autism spectrum disorders (ASD), attention assessment plays a crucial role in understanding their behavioral and cognitive functioning. Difficulties with attention are a common feature of children with autism and have a significant impact on their ability to learn and socialize. In this paper, we propose a non-invasive and objective method to assess attention in children with autism from real videos by utilizing the head poses and motion parameters. The proposed approach is an ensemble of a deep learning model that extracts head pose parameters, an optical flow approach that extracts motion parameters from consecutive frames, temporal head pose parameters extraction and an autoencoder for attention assessment. The experimental study was conducted on 39 children (ASD = 19, neurotypical children = 20) by giving different attention tasks and capturing their video using an attached webcam. Results are analyzed for participant and task differences, which demonstrate that our approach is successful in measuring a child's attention control and inattention. In particular, the assessment of the head poses and motion parameters will enable the development of real-time attention recognition systems that can be used for both learning and targeted intervention.
AB - In children with autism spectrum disorders (ASD), attention assessment plays a crucial role in understanding their behavioral and cognitive functioning. Difficulties with attention are a common feature of children with autism and have a significant impact on their ability to learn and socialize. In this paper, we propose a non-invasive and objective method to assess attention in children with autism from real videos by utilizing the head poses and motion parameters. The proposed approach is an ensemble of a deep learning model that extracts head pose parameters, an optical flow approach that extracts motion parameters from consecutive frames, temporal head pose parameters extraction and an autoencoder for attention assessment. The experimental study was conducted on 39 children (ASD = 19, neurotypical children = 20) by giving different attention tasks and capturing their video using an attached webcam. Results are analyzed for participant and task differences, which demonstrate that our approach is successful in measuring a child's attention control and inattention. In particular, the assessment of the head poses and motion parameters will enable the development of real-time attention recognition systems that can be used for both learning and targeted intervention.
KW - Attention Assessment
KW - Autism spectrum disorder (ASD)
KW - Deep Learning
KW - Head Pose Estimation
KW - Optical Flow
UR - http://www.scopus.com/inward/record.url?scp=85187325526&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85187325526&partnerID=8YFLogxK
U2 - 10.1109/GLOBECOM54140.2023.10436851
DO - 10.1109/GLOBECOM54140.2023.10436851
M3 - Conference contribution
AN - SCOPUS:85187325526
T3 - Proceedings - IEEE Global Communications Conference, GLOBECOM
SP - 6462
EP - 6468
BT - GLOBECOM 2023 - 2023 IEEE Global Communications Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Global Communications Conference, GLOBECOM 2023
Y2 - 4 December 2023 through 8 December 2023
ER -