Papers › Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning Study
Classification of Abnormal Hand Movement for Aiding in Autism Detection: Machine Learning Study
Anish Lakkapragada, Aaron Kline, Onur Cezmi Mutlu, Kelley Paskov, Brianna Chrisman, Nate Stockham, Peter Washington, Dennis Wall
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.
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Activity Recognition | Self-Stimulatory Behavior Dataset | all-landmark-model | Activity Recognition | 0.76 | #1 of 1 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections