  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.
