Explainable Artificial Intelligence for Quantifying Interfering and High-Risk Behaviors in Autism Spectrum Disorder in a Real-World Classroom Environment Using Privacy-Preserving Video Analysis

TL;DR

This paper presents an explainable, privacy-preserving video analysis method that accurately quantifies interfering and high-risk behaviors in children with ASD in real-world classroom settings, aiding intervention and resource allocation.

Contribution

It introduces the first real-world application of video-based group activity recognition for ASD behavior quantification, with an explainable model achieving 77% F1-score.

Findings

Achieved 77% F1-score in detecting problem behaviors.

Demonstrated effectiveness of privacy-preserving video analysis in real classrooms.

First to validate behavior quantification in real-world ASD educational environments.

Abstract

Rapid identification and accurate documentation of interfering and high-risk behaviors in ASD, such as aggression, self-injury, disruption, and restricted repetitive behaviors, are important in daily classroom environments for tracking intervention effectiveness and allocating appropriate resources to manage care needs. However, having a staff dedicated solely to observing is costly and uncommon in most educational settings. Recently, multiple research studies have explored developing automated, continuous, and objective tools using machine learning models to quantify behaviors in ASD. However, the majority of the work was conducted under a controlled environment and has not been validated for real-world conditions. In this work, we demonstrate that the latest advances in video-based group activity recognition techniques can quantify behaviors in ASD in real-world activities in classroom environments while preserving privacy. Our explainable model could detect the episode of problem behaviors with a 77% F1-score and capture distinctive behavior features in different types of behaviors in ASD. To the best of our knowledge, this is the first work that shows the promise of objectively quantifying behaviors in ASD in a real-world environment, which is an important step toward the development of a practical tool that can ease the burden of data collection for classroom staff.