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| Funder | National Science Foundation (US) |
|---|---|
| Recipient Organization | Michigan State University |
| Country | United States |
| Start Date | Aug 01, 2023 |
| End Date | Jul 31, 2026 |
| Duration | 1,095 days |
| Number of Grantees | 2 |
| Roles | Principal Investigator; Co-Principal Investigator |
| Data Source | National Science Foundation (US) |
| Grant ID | 2306499 |
Individuals with autism and other developmental disabilities show marked difficulty establishing and maintaining social peer engagements. These difficulties result in limited friendships, high rates of social rejection and isolation, and increased risk of anxiety, depression, and suicidal ideation. Individuals with autism and developmental disabilities also experience lower rates of employment, post-secondary participation, and independent living in comparison to their peers.
Despite their social challenges, children with autism and developmental disabilities desire to engage socially but require support. Early social skills interventions such as proximity training can help such children improve social engagement and mental health. This project develops an assistive technology for aiding such corrective social skills interventions by teachers in classroom settings.
This project develops human sensing and AI-enabled processing infrastructure for children’s peer-engagement behavior monitoring. Teachers use the sensed behavior and AI-inferred mood information to administer targeted behavioral interventions to children diagnosed with autism and other related behavioral disorders. A key technology enabler in this project is a Wearable Human Interaction Tracker (WHET) tag, which measures pair-wise human interaction between individuals.
When two people, each wearing the nametag-sized tag, interact, their interaction/engagement dynamics are tracked and quantitatively captured by the WHET sensors for subsequent wireless upload to an access point connected to cloud storage. This assistive technology for teachers helps improve the quality of intervention, as well as the number of children that a teacher can simultaneously and effectively provide interventions to.
The newly-developed technology and algorithms will also improve support for children with many other disorders including depression, anxiety, and ADHD.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Michigan State University
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