SCH: Enhanced detection of impending problem behavior in people with intellectual and developmental disabilities through multimodal sensing and machine learning

Children with Intellectual and Developmental Disabilities (IDD) are at increased risk of showing “problem behavior” that place them at risk of getting hurt, removed from the classroom, or hospitalized. Approximately 1 in 6 children and adolescents in the United States are diagnosed with IDD and half of them experience some form of problem behavior.

Therapists trained in Applied Behavior Analysis, or ABA, can help determine why problem behavior happens and how to prevent it. These therapists watch children, try to evoke problem behaviors by changing a child’s environment, then try things that might change behavior, and see if the behavioral data changes. Because problem behavior can be triggered during this process, this strategy sometimes put them or their patient at risk.

It also takes a lot of time. Wearable technology and advanced computational strategies could help increase the safety and helpfulness of strategies to prevent problem behavior. Specifically, small sensors worn in clothing or on the wrist could provide data about a child’s body, or “physiological responses,” like heart rate or sweat.

Machine learning can then be used to determine what combination of body signals imply a problem behavior is about to happen. This project has two stages. In the first stage, the team will design new sensors that detect biological signals such as sweating, motion, and heart rate.

The team will then measure how well these sensors work. This includes asking people with IDD what they think about the sensors. Based on that input, the team will change the sensors and then use them in a larger study.

The goal is to test whether the system can predict problem behavior, how well it works when used in the real-world with real therapists, and what users think about the system. Results of this study will help researchers and practitioners understand if this kind of wearable technology is helpful and acceptable as part of supporting people with problem behavior and IDD.

This project proposes to integrate transdisciplinary expertise in cutting-edge wearable sensing, affective computing, machine learning, and behavioral and clinical science to enhance and transform existing models of behavioral intervention for problem behaviors in children and adolescents with IDD. Problem behaviors, including self-injury, aggression, property destruction, and wandering not only can cause serious injury or death, but also interfere with the ability to participate in school, home, and other community settings.

In the context of problem behavior and IDD, this project will fundamentally advance the scientific and the technological methodologies of multimodal wearable sensing-based design of predictive machine learning models. The two research thrusts are: (1) Design of multimodal sensor framework; and 2) Real-time precursor prediction. Across these thrusts, the project will make fundamental scientific and technological advancements in: (i) A low-power, open-access, user-centric wearable sensor framework that can sense physiological responses and gestures to be used for affective computing; and (ii) A set of novel, clinically grounded, semi-supervised machine learning models to predict problem behavior that can be used by behavioral interventionists in real-time.

An important novelty of this research that separates it from existing work in the field is that the team proposes to address the detection of problem behavior through its precursors, rather than the behaviors themselves, with the goal of increasing the safety and efficiency of sessions. These scientific and technological advancements will be created within a state-of-the-art clinical and behavioral science framework.

The proposed work will foster interdisciplinary research in engineering and health sciences. The team proposes a number of outreach and educational activities that will have broader impact in STEM education: i) involve individuals with ASD directly in the research through the Frist Center for Autism & Innovation’s Neurodiversity Corps; ii) provide interdisciplinary training opportunities for early stage clinical scientists; iii) provide research opportunity to high school, undergraduate, and graduate students; iv) provide research opportunity to high school teachers; v) bring research into the classroom; and vi) disseminate the research through seminars, presentation, and publication.

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.