# SCH: Enhancing Automated Prediction of Challenging Behavior in Individuals with Autism Using Biosensor Data and Machine Learning

> **NIH NIH R01** · NORTHEASTERN UNIVERSITY · 2024 · $294,332

## Abstract

PROJECT SUMMARY (See instructions): 
Autism Spectrum Disorder (ASD) is one of the most common childhood disorders (1 in 44 ). Individuals 
with ASD have a higher prevalence of challenging behavior (e.g., aggression, self-injury, emotion 
dysregulation) that interferes with adaptive development, ranks among the most common causes for 
referral to behavioral healthcare services, and incurs high healthcare costs. Over the past four years, the 
team at Northeastern University has made significant research progress developing machine learning 
procedures that automate the detection of challenging behavior onsets in individuals with ASD using 
wearable biosensor data (cardiovascular, electrodermal, and physical activity). Despite our promising 
results, issues still need to be addressed to enable practical daily use in real-world contexts. This includes 
the need for extensively labeled data, individual calibration from population models to specific individuals, 
and handling the non-stationary nature of challenging behavior and physiological data. This project aims 
to advance fundamental machine learning theory and techniques that facilitate rapid model 
individualization and continuous online model adaptation with little or no labeled data. To this end, we will 
contribute to areas including domain adaptation, transfer learning, lifelong learning, and robust modeling 
and inference. Three Specific Aims guide the project: (1) Rapid physiological and behavioral data model 
individualization; (2) Continuous lifelong physiological and behavioral data model adaptation; and (3) 
Validation of model individualization and adaptation techniques with prospective data collected in a clinical 
setting from our partners at the Marcus Autism Center at Emory University who specialize in functional 
analysis of challenging behavior in individuals with ASD. Across these Aims, we will explore applications 
of semi-supervised learning theory, data importance weighting, Support Vector Machines, neural network 
models, Hierarchical Markov-Modulated Point Process Models, and Bayesian evidence fusion. The 
modeling and inference techniques we develop will be of general applicability to other health application 
contexts involving event prediction (e.g., seizure detection) and human action/decision-making (e.g., 
intensive care unit triage).

## Key facts

- **NIH application ID:** 10895601
- **Project number:** 5R01LM014191-03
- **Recipient organization:** NORTHEASTERN UNIVERSITY
- **Principal Investigator:** Matthew Scott Goodwin
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $294,332
- **Award type:** 5
- **Project period:** 2022-09-16 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10895601

## Citation

> US National Institutes of Health, RePORTER application 10895601, SCH: Enhancing Automated Prediction of Challenging Behavior in Individuals with Autism Using Biosensor Data and Machine Learning (5R01LM014191-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10895601. Licensed CC0.

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