https://digitalcommons.unmc.edu/surp2022/1003/thumbnail.jp

Arce, Walker

Davies, Toluwa

DeBrine, Jordan

Gehringer, James E.

Malick, Katelyn

Walker, Seth

Welch, Carter

Zangrillo, Amanda

English

University of Nebraska Medical Center Research: DigitalCommons@UNMC

University of Nebraska Medical Center DigitalCommons@UNMC Posters: 2022 Summer Undergraduate Research Program Summer Undergraduate Research Program Summer 8-10-2022 Coding Severe Behaviors in Children with Autism Spectrum Disorder to Train Machine Learning Algorithms Toluwa Davies Munroe-Meyer Institute Walker Arce Munroe-Meyer Institute Jordan DeBrine Munroe-Meyer Institute Katelyn Malick University of Nebraska Medical Center Carter Welch Munroe-Meyer Institute See next page for additional authors Follow this and additional works at: https://digitalcommons.unmc.edu/surp2022 Recommended Citation Davies, Toluwa; Arce, Walker; DeBrine, Jordan; Malick, Katelyn; Welch, Carter; Gehringer, James E.; Walker, Seth; and Zangrillo, Amanda, "Coding Severe Behaviors in Children with Autism Spectrum Disorder to Train Machine Learning Algorithms" (2022). Posters: 2022 Summer Undergraduate Research Program. 3. https://digitalcommons.unmc.edu/surp2022/3 This Poster is brought to you for free and open access by the Summer Undergraduate Research Program at DigitalCommons@UNMC. It has been accepted for inclusion in Posters: 2022 Summer Undergraduate Research Program by an authorized administrator of DigitalCommons@UNMC. For more information, please contact digitalcommons@unmc.edu. Author Toluwa Davies, Walker Arce, Jordan DeBrine, Katelyn Malick, Carter Welch, James E. Gehringer, Seth Walker, and Amanda Zangrillo This poster is available at DigitalCommons@UNMC: https://digitalcommons.unmc.edu/surp2022/3 MethodsPurposeExpected Results and Conclusion• One in 44 children are diagnosed with autism spectrum disorder (ASD)3, which can result in challenging behaviors that negatively affect both them and their caregivers4.• Function-based treatment (FBT) is effective but rare, due to intense training and time requirements.2 This translates to limited access for families.• At the Munroe-Meyer Institute (MMI), 30-40% of staff time is allocated to collecting observational data alone, limiting potential availability for clinical treatment.• Machine learning algorithms (MLAs) have been used for automation in a variety of fields; however, data on their efficacy in observing challenging behaviors is limited.BackgroundCoding Severe Behaviors in Children with Autism Spectrum Disorder to Train Machine Learning AlgorithmsToluwa Davies, SURP1, Walker Arce1, Jordan DeBrine M.A., BCBA3, Katelyn Malick, SURP1, Carter Welch, SURP3, James E. Gehringer, PhD1,2, Seth Walker, PhD, BCBA-D3, Amanda Zangrillo, PsyD, BCBA-D3Virtual Reality Lab, Munroe-Meyer Institute1. Physical Therapy Department, Munroe-Meyer Institute2. Severe Behavior Department, Munroe-Meyer Institute3.References1. Arce, W. S., Walker, S. G., Hurtz, M. L., & Gehringer, J. E. (2022). cometrics (Version v1.1.4) [Computer software]. https://github.com/Munroe-Meyer-Institute-VR-Laboratory/cometrics2. Ghaemmaghami, M., Hanley, G. P., & Jessel, J. (2021). Functional Communication Training: From Efficacy to Effectiveness. Journal of Applied Behavior Analysis, 54(1), 122-143.3. Maenner, M. J., Shaw, K. A., Bakian, A. V., Bilder, D. A., Durkin, M. S., Esler, A., ... & Cogswell, M. E. (2021). Prevalence and characteristics of autism spectrum disorder among children aged 8 years—autism and developmental disabilities monitoring network, 11 sites, United States, 2018. MMWR Surveillance Summaries, 70(11), 1.4. Newcomb, E. T., & Hagopian, L. P. (2018). Treatment of Severe Problem Behaviour in Children with Autism Spectrum Disorder and Intellectual Disabilities. International Review of Psychiatry, 30(1), 96-109.Next StepsContact: james.gehringer@unmc.eduTest MLA performance for accuracy• MLA will analyze short videos featuring novel instances of the behaviors it’s been trained for.• MLA will provide binary output on either the presence or absence of those behaviors every two seconds.• Results will be compared to actual number of behaviors predetermined by human analysis.2Use the coded datasets to train MLAs• Multi-Class Classification: allows for training of one MLA to detect several behaviors, rather than one MLA per behavior1Participants• Nine male youths, ages 4-18• Diagnosis of ASD or other intellectual/developmental disability (IDD)• Participants were patients receiving FBT in the Severe Behavior Department at MMIData Extraction For Development of MLAs• Series of 20-minute videos featuring recorded footage of participants during delivery of FBT.• A custom software ,“cometrics”, developed to facilitate and streamline video coding/data collection process1• Coding performed by two researchers per video• Coders looked for 21 challenging behaviors from the perspective of MLACoding Process in “cometrics”• Coder fills out “Patient Information” section (Fig. 1)• Coder loads video and starts session (Fig. 2)• Coder refers to key bindings listed on left side of screen to tag behaviors as they happen in the video (Fig.3)Figure 2: Screen once video for coding is selectedFigure 3: Screen during coding session in “cometrics”This study aims to train machine-learning algorithms to code severe behaviors automatically, giving staff more time to engage with patients directly.Figure 1: Initial screen once inside “cometrics”The goal of this study is to train MLAs to identify challenging behaviors and to assess their accuracy within- and between-subjects.• If MLAs can identify severe behaviors at a level close to that of humans, the task can be reassigned from human to machine.• By automating the behavioral coding process, applied behavioral analyst staff will be have more time they can dedicate to serving more patients.• The more data we process through “cometrics”, the more successful the MLA training process will be.• Eventually, we plan to apply these technologies to real-time monitoring during FBT sessions as well.• Benefits include increased safety for staff, as well as reducing the number of staff needed on standby during sessions (automatic alerting system).Thank you to all who helped with the video coding process. Thank you to the MMI Severe Behavior Department for supporting this project. Toluwa Davies was supported by the MMI Summer Undergraduate Research Program.Acknowledgements

Coding Severe Behaviors in Children with Autism Spectrum Disorder to Train Machine Learning Algorithms

https://digitalcommons.unmc.edu/cgi/viewcontent.cgi?article=1003&context=surp2022

Coding Severe Behaviors in Children with Autism Spectrum Disorder to Train Machine Learning Algorithms

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