TY - GEN
T1 - Using multimodal data for automated fidelity evaluation in pivotal response treatment videos
AU - Heath, Corey D.C.
AU - Venkateswara, Hemanth
AU - McDaniel, Troy
AU - Panchanathan, Sethuraman
PY - 2019/11
Y1 - 2019/11
N2 - Research has shown that caregivers implementing pivotal response treatment (PRT) with their child with autism spectrum disorder (ASD) helps the child develop social and communication skills. Evaluation of caregiver fidelity to PRT in training programs and research studies relies on the evaluation of video probes depicting the caregiver interacting with his or her child. These video probes are reviewed by behavior analysts and are dependent on manual processing to extract data metrics. Using multimodal data processing techniques and machine learning could alleviate the human cost of evaluating the video probes by automating data analysis tasks.Creating an 'Opportunity to Respond' is one of the categories used to evaluate caregiver fidelity to PRT implementation. A caregiver is determined to have successfully demonstrated cre-ating an opportunity to respond when they have delivered an appropriate instruction while she or he has the child's attention. Automatically determining when the caregiver has correctly provided an opportunity to respond requires classifying the audio and video data from the probes. Combining the modalities into a single classification task can be undertaken using feature fusion or decision fusion methods. Two decision fusion configurations, and a feature fusion model were evaluated. The decision fusion models achieved higher accuracy, however the feature fusion model had a higher average F1 score, indicating more reliable prediction capability.
AB - Research has shown that caregivers implementing pivotal response treatment (PRT) with their child with autism spectrum disorder (ASD) helps the child develop social and communication skills. Evaluation of caregiver fidelity to PRT in training programs and research studies relies on the evaluation of video probes depicting the caregiver interacting with his or her child. These video probes are reviewed by behavior analysts and are dependent on manual processing to extract data metrics. Using multimodal data processing techniques and machine learning could alleviate the human cost of evaluating the video probes by automating data analysis tasks.Creating an 'Opportunity to Respond' is one of the categories used to evaluate caregiver fidelity to PRT implementation. A caregiver is determined to have successfully demonstrated cre-ating an opportunity to respond when they have delivered an appropriate instruction while she or he has the child's attention. Automatically determining when the caregiver has correctly provided an opportunity to respond requires classifying the audio and video data from the probes. Combining the modalities into a single classification task can be undertaken using feature fusion or decision fusion methods. Two decision fusion configurations, and a feature fusion model were evaluated. The decision fusion models achieved higher accuracy, however the feature fusion model had a higher average F1 score, indicating more reliable prediction capability.
KW - Attention Detection
KW - Autism Spectrum Disorder
KW - Machine Learning
KW - Multimodal Data
KW - Pivotal Response Treatment
UR - http://www.scopus.com/inward/record.url?scp=85079281592&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85079281592&partnerID=8YFLogxK
U2 - 10.1109/GlobalSIP45357.2019.8969089
DO - 10.1109/GlobalSIP45357.2019.8969089
M3 - Conference contribution
T3 - GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings
BT - GlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019
Y2 - 11 November 2019 through 14 November 2019
ER -