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

Daniels, Jena; Haber, Nick; Voss, Catalin; Schwartz, Jessey; Tamura, Serena; Fazel, Azar; Kline, Aaron; Washington, Peter; Phillips, Jennifer M.; Winograd, Terry; Feinstein, Carl; Wall, Dennis P.

doi:10.1055/s-0038-1626727

Cited by 55 publications

(37 citation statements)

References 63 publications

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“…Several papers have focussed on using computer vision to develop assistive technologies for ASD children [40][41][42][43] . For example, researchers 40,42,43 developed and evaluated a wearable assistive technology to help ASD children with emotion recognition. Vahabzadeh et al 44 .…”

Section: Facial Expression/emotionmentioning

confidence: 99%

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

et al. 2020

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The current state of computer vision methods applied to autism spectrum disorder (ASD) research has not been well established. Increasing evidence suggests that computer vision techniques have a strong impact on autism research. The primary objective of this systematic review is to examine how computer vision analysis has been useful in ASD diagnosis, therapy and autism research in general. A systematic review of publications indexed on PubMed, IEEE Xplore and ACM Digital Library was conducted from 2009 to 2019. Search terms included [‘autis*’ AND (‘computer vision’ OR ‘behavio* imaging’ OR ‘behavio* analysis’ OR ‘affective computing’)]. Results are reported according to PRISMA statement. A total of 94 studies are included in the analysis. Eligible papers are categorised based on the potential biological/behavioural markers quantified in each study. Then, different computer vision approaches that were employed in the included papers are described. Different publicly available datasets are also reviewed in order to rapidly familiarise researchers with datasets applicable to their field and to accelerate both new behavioural and technological work on autism research. Finally, future research directions are outlined. The findings in this review suggest that computer vision analysis is useful for the quantification of behavioural/biological markers which can further lead to a more objective analysis in autism research.

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Section: Facial Expression/emotionmentioning

confidence: 99%

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

et al. 2020

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“…1–5 Children with ASD have demonstrated deficits in facial processing abilities, such as distinguishing fear from surprise and identifying subtler emotions. 6–9 Children also struggle with facial engagement and eye contact. 10,11 Teaching these skills to children with autism is important for social development and is closely linked with empathy.…”

Section: Introductionmentioning

confidence: 99%

“…To address the complications associated with accessing the clinical setting and to expedite children’s access to therapy, we have begun development of a system to deliver therapy at home using a machine-learning-assisted software system that runs on Google Glass paired with an Android smartphone, designed for use in the child’s natural environment during social interactions with friends and family members. 30–32 It recognizes eight emotions, as described in detail by Ekman et al: happiness, sadness, anger, disgust, surprise, fear, neutral, and contempt (named “meh” in child-friendly terms), which are recognized as theoretically universal emotions. 33–35 The Glass provides audiovisual feedback to the wearer that corresponds to which of the eight emotions the Glass recognizes during social interactions through its outward-facing camera.…”

Section: Introductionmentioning

confidence: 99%

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Exploratory study examining the at-home feasibility of a wearable tool for social-affective learning in children with autism

et al. 2018

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Although standard behavioral interventions for autism spectrum disorder (ASD) are effective therapies for social deficits, they face criticism for being time-intensive and overdependent on specialists. Earlier starting age of therapy is a strong predictor of later success, but waitlists for therapies can be 18 months long. To address these complications, we developed Superpower Glass, a machine-learning-assisted software system that runs on Google Glass and an Android smartphone, designed for use during social interactions. This pilot exploratory study examines our prototype tool’s potential for social-affective learning for children with autism. We sent our tool home with 14 families and assessed changes from intake to conclusion through the Social Responsiveness Scale (SRS-2), a facial affect recognition task (EGG), and qualitative parent reports. A repeated-measures one-way ANOVA demonstrated a decrease in SRS-2 total scores by an average 7.14 points ( F (1,13) = 33.20, p = <.001, higher scores indicate higher ASD severity). EGG scores also increased by an average 9.55 correct responses ( F (1,10) = 11.89, p = <.01). Parents reported increased eye contact and greater social acuity. This feasibility study supports using mobile technologies for potential therapeutic purposes.

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“…Beyond single time-point diagnostics, the results of the present study can inform areas of focus for future digital phenotyping 25 efforts for ASD. At-home digital therapies for ASD 5,6,26 could benefit from targeted tracking of behavioral features in order to provide customized digital therapies to the child. Dual-purpose digital therapies aimed at simultaneous data capture and intervention 27–30 could focus the area of target towards capturing salient behaviors.…”

Section: Future Outlookmentioning

confidence: 99%

Feature Selection and Dimension Reduction of Social Autism Data

et al. 2019

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Autism Spectrum Disorder (ASD) is a complex neuropsychiatric condition with a highly heterogeneous phenotype. Following the work of Duda et al., which uses a reduced feature set from the Social Responsiveness Scale, Second Edition (SRS) to distinguish ASD from ADHD, we performed item-level question selection on answers to the SRS to determine whether ASD can be distinguished from non-ASD using a similarly small subset of questions. To explore feature redundancies between the SRS questions, we performed filter, wrapper, and embedded feature selection analyses. To explore the linearity of the SRS-related ASD phenotype, we then compressed the 65-question SRS into low-dimension representations using PCA, t-SNE, and a denoising autoencoder. We measured the performance of a multi-layer perceptron (MLP) classifier with the top-ranking questions as input. Classification using only the top-rated question resulted in an AUC of over 92% for SRS-derived diagnoses and an AUC of over 83% for dataset-specific diagnoses. High redundancy of features have implications towards replacing the social behaviors that are targeted in behavioral diagnostics and interventions, where digital quantification of certain features may be obfuscated due to privacy concerns. We similarly evaluated the performance of an MLP classifier trained on the low-dimension representations of the SRS, finding that the denoising autoencoder achieved slightly higher performance than the PCA and t-SNE representations.

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

Abstract: 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.

Cited by 55 publications

References 63 publications

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

Exploratory study examining the at-home feasibility of a wearable tool for social-affective learning in children with autism

Feature Selection and Dimension Reduction of Social Autism Data

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