An experimental wearable IoT for data-driven management of autism

This paper reports early developments on an wearable IoT system that can be used for collecting quantified data about interactions among children with autism spectrum disorders (ASD) in classroom settings. The overall objective of the project is to perform data-driven detection, therapy, intervention, and progress monitoring for children with Autism. Early intervention can help support the development of social skills for children with ASD. To perform intervention, teachers monitor the social interaction progress of children with ASD through observational methods, which can often be lengthy and subjective, therefore requiring extensive teacher training to ensure accuracy. Although new technologies have begun to emerge, no existing technologies can provide quantitative and automatically-collected data about peer interactions for children with ASD. We address this critical need through a wearable sensor system that can provide real-time quantitative data about peer interactions in classroom settings. The long-term objective of the project is to use this system for data-driven detection, therapy, intervention, and progress monitoring for children with Autism.