Pregled bibliografske jedinice broj: 963337
Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach
Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach // Proceedings of the 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2018)
Split, 2018. str. 1-6 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
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Naslov
Object Classification for Child Behavior Observation in the Context of Autism Diagnostics Using a Deep Learning-based Approach
Autori
Presečan, Mihael ; Petric, Frano ; Kovačić, Zdenko
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the 26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2018)
/ - Split, 2018, 1-6
Skup
26th International Conference on Software, Telecommunications and Computer Networks (SoftCOM 2018)
Mjesto i datum
Split, Hrvatska; Supetar, Hrvatska, 13.09.2018. - 15.09.2018
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
deep learning, object detection, object classification, Faster R-CNN, ADORE, ADOS, ADOSet, free-play, autism
Sažetak
In this paper we demonstrate the effectiveness of a deep learning approach for object detection and classification using a mono-vision feedback of a NAO humanoid robot for assessing the child’s behavior during a free play with standardized toys. The free play is one of the tasks contained in the standard ADOS-2 autism spectrum disorder diagnostic protocol used by clinicians. In order to make an accurate, robust and fast object detector, a new data set for learning and testing has been created to enable a reliable assessment of the child’s behavior while playing with the toys. This has also led to the development of algorithms and mechanism to assess child’s attention based on the toys that the child is playing with. This paper concludes with the discussion about the challenges encountered and their solutions, as well as about the prospective development goals focused on achieving more robust and accurate child attention analyzer.
Izvorni jezik
Engleski
Znanstvena područja
Elektrotehnika, Računarstvo, Temeljne tehničke znanosti
