This SBIR Phase I project intends to improve the outcomes of children diagnosed with autism spectrum disorder (ASD) by providing critical data from non-clinical settings that are currently unavailable to autism treatment providers. The project also focuses on accelerating the analysis of that data. It is estimated that the costs associated with ASD will be more than $2 million over a patient's life. However, studies have shown that the costs of lifetime care for a child diagnosed with ASD can be reduced by 2/3 with early intervention. Designing an appropriate treatment program, and then monitoring its efficacy, requires the collection of a range of data to avoid dependence on inference and anecdotes. Treatment providers often collect detailed data while working with an individual diagnosed with ASD, but they count on parents and caregivers to provide them with data regarding the child's progress outside the clinical setting. The data submitted are usually incomplete and inadequate (if submitted at all). This SBIR Phase I project will result in a prototype that will rapidly provide autism treatment providers with critical data and analysis, reducing the amount of time it takes to design an optimal treatment program, and thereby reducing the long-term costs associated with a diagnosis of ASD.
This Phase I project will result in a prototype that will make the process of logging data almost frictionless for parents. That narrative data will be combined with video data, biometric data, and additional data collected passively from external sources. All the data will be merged, and then analyzed via machine learning to locate patterns and provide a complete picture to the child's behavior analyst. Developing a camera that will be unobtrusive enough for a child with ASD to tolerate will present a challenge, as will developing the associated software to tag items within the camera's view and save only relevant clips (such as the times immediately before, during and after a meltdown). It will also be challenging to develop a wearable device that a child with ASD will tolerate, along with the associated sensors and software to track metrics associated with arousal and stress. The most significant technical challenge, however, will be to develop the software that combines the multiple data streams and outputs a standardized ABC report (Antecedent, Behavior, Consequence) that would be judged as high quality by a Board Certified Behavior Analyst.
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.
