  Advances in machine learning and contactless sensors have enabled the
understanding complex human behaviors in a healthcare setting. In particular,
several deep learning systems have been introduced to enable comprehensive
analysis of neuro-developmental conditions such as Autism Spectrum Disorder
(ASD). This condition affects children from their early developmental stages
onwards, and diagnosis relies entirely on observing the child's behavior and
detecting behavioral cues. However, the diagnosis process is time-consuming as
it requires long-term behavior observation, and the scarce availability of
specialists. We demonstrate the effect of a region-based computer vision system
to help clinicians and parents analyze a child's behavior. For this purpose, we
adopt and enhance a dataset for analyzing autism-related actions using videos
of children captured in uncontrolled environments (e.g. videos collected with
consumer-grade cameras, in varied environments). The data is pre-processed by
detecting the target child in the video to reduce the impact of background
noise. Motivated by the effectiveness of temporal convolutional models, we
propose both light-weight and conventional models capable of extracting action
features from video frames and classifying autism-related behaviors by
analyzing the relationships between frames in a video. Through extensive
evaluations on the feature extraction and learning strategies, we demonstrate
that the best performance is achieved with an Inflated 3D Convnet and
Multi-Stage Temporal Convolutional Networks, achieving a 0.83 Weighted F1-score
for classification of the three autism-related actions, outperforming existing
methods. We also propose a light-weight solution by employing the ESNet
backbone within the same system, achieving competitive results of 0.71 Weighted
F1-score, and enabling potential deployment on embedded systems.
