abstract: Parents fulfill a pivotal role in early childhood development of social and communication
skills. In children with autism, the development of these skills can be delayed. Applied
behavioral analysis (ABA) techniques have been created to aid in skill acquisition.
Among these, pivotal response treatment (PRT) has been empirically shown to foster
improvements. Research into PRT implementation has also shown that parents can be
trained to be effective interventionists for their children. The current difficulty in PRT
training is how to disseminate training to parents who need it, and how to support and
motivate practitioners after training.
Evaluation of the parents’ fidelity to implementation is often undertaken using video
probes that depict the dyadic interaction occurring between the parent and the child during
PRT sessions. These videos are time consuming for clinicians to process, and often result
in only minimal feedback for the parents. Current trends in technology could be utilized to
alleviate the manual cost of extracting data from the videos, affording greater
opportunities for providing clinician created feedback as well as automated assessments.
The naturalistic context of the video probes along with the dependence on ubiquitous
recording devices creates a difficult scenario for classification tasks. The domain of the
PRT video probes can be expected to have high levels of both aleatory and epistemic
uncertainty. Addressing these challenges requires examination of the multimodal data
along with implementation and evaluation of classification algorithms. This is explored
through the use of a new dataset of PRT videos.
The relationship between the parent and the clinician is important. The clinician can
provide support and help build self-efficacy in addition to providing knowledge and
modeling of treatment procedures. Facilitating this relationship along with automated
feedback not only provides the opportunity to present expert feedback to the parent, but
also allows the clinician to aid in personalizing the classification models. By utilizing a
human-in-the-loop framework, clinicians can aid in addressing the uncertainty in the
classification models by providing additional labeled samples. This will allow the system
to improve classification and provides a person-centered approach to extracting
multimodal data from PRT video probes. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2019
| Identifer | oai:union.ndltd.org:asu.edu/item:55490 |
| Date | January 2019 |
| Contributors | Copenhaver Heath, Corey D (Author), Panchanathan, Sethuraman (Advisor), McDaniel, Troy (Committee member), Venkateswara, Hemanth (Committee member), Davulcu, Hasan (Committee member), Gaffar, Ashraf (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Doctoral Dissertation |
| Format | 275 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/ |
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