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Appl Clin Inform 2018; 09(01): 129-140
DOI: 10.1055/s-0038-1626727
Research Article
Schattauer GmbH Stuttgart

Feasibility Testing of a Wearable Behavioral Aid for Social Learning in Children with Autism

Jena Daniels
,
Nick Haber
,
Catalin Voss
,
Jessey Schwartz
,
Serena Tamura
,
Azar Fazel
,
Aaron Kline
,
Peter Washington
,
Jennifer Phillips
,
Terry Winograd
,
Carl Feinstein
,
Dennis P. Wall
 Funding The work was supported in part by funds to D.P.W. from NIH (1R01EB025025-01 & 1R21HD091500-01), The Hartwell Foundation, Bill and Melinda Gates Foundation, Coulter Foundation, Lucile Packard Foundation, and program grants from Stanford's Precision Health and Integrated Diagnostics Center (PHIND), Beckman Center, Bio-X Center, Predictives and Diagnostics Accelerator (SPADA) Spectrum, and Child Health Research Institute. We also acknowledge generous support from David Orr, Imma Calvo, Bobby Dekesyer and Peter Sullivan.
Further Information

Publication History

15 November 2017

01 January 2018

Publication Date:
21 February 2018 (online)

Also available at
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Abstract

Background Recent advances in computer vision and wearable technology have created an opportunity to introduce mobile therapy systems for autism spectrum disorders (ASD) that can respond to the increasing demand for therapeutic interventions; however, feasibility questions must be answered first.

Objective We studied the feasibility of a prototype therapeutic tool for children with ASD using Google Glass, examining whether children with ASD would wear such a device, if providing the emotion classification will improve emotion recognition, and how emotion recognition differs between ASD participants and neurotypical controls (NC).

Methods We ran a controlled laboratory experiment with 43 children: 23 with ASD and 20 NC. Children identified static facial images on a computer screen with one of 7 emotions in 3 successive batches: the first with no information about emotion provided to the child, the second with the correct classification from the Glass labeling the emotion, and the third again without emotion information. We then trained a logistic regression classifier on the emotion confusion matrices generated by the two information-free batches to predict ASD versus NC.

Results All 43 children were comfortable wearing the Glass. ASD and NC participants who completed the computer task with Glass providing audible emotion labeling (n = 33) showed increased accuracies in emotion labeling, and the logistic regression classifier achieved an accuracy of 72.7%. Further analysis suggests that the ability to recognize surprise, fear, and neutrality may distinguish ASD cases from NC.

Conclusion This feasibility study supports the utility of a wearable device for social affective learning in ASD children and demonstrates subtle differences in how ASD and NC children perform on an emotion recognition task.

Keywords

autism spectrum disorder - pilot projects - wearable device and body area network - artificial intelligence - emotion recognition - pediatrics - people with disabilities or special needs

Protection of Human and Animal Subjects

This study was approved by the Institutional Review Board at Stanford University's School of Medicine, IRB Protocol 31817. Participants' assent and parents' informed consent were received before inclusion in the study.


 

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