Analyzing Head Orientation of Neurotypical and Autistic Individuals in
  Triadic Conversations
Onur N. Tepencelik Wenchuan Wei Pamela C. Cosman Sujit Dey
We propose a system that estimates people's body and head orientations using
low-resolution point cloud data from two LiDAR sensors. Our models make
accurate estimations in real-world conversation settings where the subject
moves naturally with varying head and body poses. The body orientation
estimation model uses ellipse fitting while the head orientation estimation
model is a pipeline of geometric feature extraction and an ensemble of neural
network regressors. Compared with other body and head orientation estimation
systems using RGB cameras, our proposed system uses LiDAR sensors to preserve
user privacy, while achieving comparable accuracy. Unlike other body/head
orientation estimation systems, our sensors do not require a specified
placement in front of the subject. Our models achieve a mean absolute
estimation error of 5.2 degrees for body orientation and 13.7 degrees for head
orientation. We use our models to quantify behavioral differences between
neurotypical and autistic individuals in triadic conversations. Tests of
significance show that people with autism spectrum disorder display
significantly different behavior compared to neurotypical individuals in terms
of distributing attention between participants in a conversation, suggesting
that the approach could be a component of a behavioral analysis or coaching
system.