  A formal autism diagnosis can be an inefficient and lengthy process. Families
may wait months or longer before receiving a diagnosis for their child despite
evidence that earlier intervention leads to better treatment outcomes. Digital
technologies which detect the presence of behaviors related to autism can scale
access to pediatric diagnoses. This work aims to demonstrate the feasibility of
deep learning technologies for detecting hand flapping from unstructured home
videos as a first step towards validating whether models and digital
technologies can be leveraged to aid with autism diagnoses. We used the
Self-Stimulatory Behavior Dataset (SSBD), which contains 75 videos of hand
flapping, head banging, and spinning exhibited by children. From all the hand
flapping videos, we extracted 100 positive and control videos of hand flapping,
each between 2 to 5 seconds in duration. Utilizing both
landmark-driven-approaches and MobileNet V2's pretrained convolutional layers,
our highest performing model achieved a testing F1 score of 84% (90% precision
and 80% recall) when evaluating with 5-fold cross validation 100 times. This
work provides the first step towards developing precise deep learning methods
for activity detection of autism-related behaviors.
