# Creating an artificial intelligence therapy-to-data feedback loop for child developmental healthcare

> **NIH NIH R01** · STANFORD UNIVERSITY · 2021 · $649,383

## Abstract

Project Summary
There is a sharp and increasing imbalance between the number of children with autism in need of care and the
availability of specialists certified to treat the disorder in its multi-faceted manifestations. The autism community
faces a dual clinical challenge: how to direct scarce specialist resources to service the diverse array of
phenomes and how to monitor and validate best practices in treatment. Clinicians must now look to solutions
that scale in a decentralized fashion, placing data capture, remote monitoring, and therapy increasingly into the
hands of families. Using artificial intelligence (AI) and large amounts of labeled human emotion computer vision
data, we have developed a solution for automatic facial expression recognition that runs on Google Glasses
and Android smartphones to deliver real time social cues to individuals with autism in the child’s natural
environment. We hypothesize that this informatic system can provide real-time therapy in a way that scales to
meet the demand of the growing population of autism families, including underserved minorities, while growing
data that can be used to measure progress over time and in the development of novel AI.
Our first aim will focus on the development of a deep learning model that enables dynamic emotion recognition
in the real world, and on domain adaptation procedures that enable minimal manual labeling to personalize the
model for optimal accuracy on the individuals with whom the child will interact most regularly at home. Our
second aim will focus on the human computer interface, namely the design of the user experience with the
Android application that controls the sessions run on the Google Glass wearable. We will work our clinical
colleagues and with groups of autism families to develop and enhance a set of games and activity modes that
create social engagements ideal for emotion therapy, including an emotion capture and a charades game. The
third aim will test our central hypothesis that the Glass system can create a therapy-to-data feedback loop that
delivers clinical care while growing data for measurement and model development.
We will work with up to 200 children ages 4-8 who have recent autism diagnoses and do not have access to
standard behavioral therapy. We will build a community of autism families through crowdsourcing techniques,
befitting the mobile paradigm embodied by our work, and through close collaboration with behavioral therapy
providers, the autism outreach organization Autism Speaks, and the digital healthcare company, Cognoa.
The families will work with us on design and refinement of our “Superpower Glass” system for fit, engagement,
and function of use for both therapy and data capture. Importantly, we will send units home with families to use
the device for at least 3 twenty-minute sessions per week for a minimum of 6 weeks. This remote period will
generate a massive database to quantify overall social learning, emotion comprehension, e...

## Key facts

- **NIH application ID:** 10164858
- **Project number:** 5R01LM013083-03
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Dennis Paul Wall
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $649,383
- **Award type:** 5
- **Project period:** 2019-06-01 → 2023-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10164858

## Citation

> US National Institutes of Health, RePORTER application 10164858, Creating an artificial intelligence therapy-to-data feedback loop for child developmental healthcare (5R01LM013083-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10164858. Licensed CC0.

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