TY - GEN
T1 - Detecting attention in pivotal response treatment video probes
AU - Heath, Corey D.C.
AU - Demakethepalli Venkateswara, Hemanth
AU - McDaniel, Troy
AU - Panchanathan, Sethuraman
N1 - Funding Information:
Acknowledgement. The authors thank Arizona State University and National Science Foundation for their funding support. This material is partially based upon work supported by the National Science Foundation under Grant No. 1069125.
Publisher Copyright:
© 2018, Springer Nature Switzerland AG.
PY - 2018
Y1 - 2018
N2 - The benefits of caregivers implementing Pivotal Response Treatment (PRT) with children on the Autism spectrum is empirically supported in current Applied Behavior Analysis (ABA) research. Training caregivers in PRT practices involves providing instruction and feedback from trained professional clinicians. As part of the training and evaluation process, clinicians systematically score video probes of the caregivers implementing PRT in several categories, including if an instruction was given when the child was paying adequate attention to the caregiver. This paper examines how machine learning algorithms can be used to aid in classifying video probes. The primary focus of this research explored how attention can be automatically inferred through video processing. To accomplish this, a dataset was created using video probes from PRT sessions and used to train machine learning models. The ambiguity inherent in these videos provides a substantial set of challenges for training an intelligence feedback system.
AB - The benefits of caregivers implementing Pivotal Response Treatment (PRT) with children on the Autism spectrum is empirically supported in current Applied Behavior Analysis (ABA) research. Training caregivers in PRT practices involves providing instruction and feedback from trained professional clinicians. As part of the training and evaluation process, clinicians systematically score video probes of the caregivers implementing PRT in several categories, including if an instruction was given when the child was paying adequate attention to the caregiver. This paper examines how machine learning algorithms can be used to aid in classifying video probes. The primary focus of this research explored how attention can be automatically inferred through video processing. To accomplish this, a dataset was created using video probes from PRT sessions and used to train machine learning models. The ambiguity inherent in these videos provides a substantial set of challenges for training an intelligence feedback system.
KW - Attention detection
KW - Autism spectrum disorder
KW - Dyadic behavior
KW - Pivotal response treatment
KW - Untrimmed video
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U2 - 10.1007/978-3-030-04375-9_21
DO - 10.1007/978-3-030-04375-9_21
M3 - Conference contribution
AN - SCOPUS:85058522678
SN - 9783030043742
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 248
EP - 259
BT - Smart Multimedia - 1st International Conference, ICSM 2018, Revised Selected Papers
A2 - Berretti, Stefano
A2 - Basu, Anup
PB - Springer Verlag
T2 - 1st International Conference on Smart Multimedia, ICSM 2018
Y2 - 24 August 2018 through 26 August 2018
ER -