Stimming Behavior Dataset - Unifying Stereotype Behavior Dataset in the Wild

Non-intrusive vision-assisted methods that can recognize complex human behaviors can help the diagnosis and treatment of neurological disorders where stimming behaviors are prominent, such as Autism Spectrum Disorder (ASD). Machine learning methods, especially those related to computer vision are a promising direction for this kind of application, however, the effectiveness of these methods depends on the existence of good-quality datasets. Building a dataset in this domain is challenging due to privacy matters and also to the need to conduct experiments mostly with children. Therefore, we propose a consolidated, annotated, and standardized Stimming Behavior Dataset. This unified dataset can enable researchers to have access to a larger pool of data, classified and annotated for the stimming behaviors, enabling a faster and more streamlined approach when working with this kind of video collection. The Autism Stimming Behavior Dataset - ASBD
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https://github.com/OckerGui/Stimming-Behavior-Dataset can be used for the development of machine learning algorithms that can detect behavioral patterns associated with autism spectrum disorder and other applications where this kind of analysis is important. The dataset is comprised of 165 annotated short duration clips, based on excerpts from 155 distinct publicly accessible Youtube videos., with 154 unique subjects of ages spanning from toddlers to young adults and divided into 48 female and 106 male individuals. The average duration of the clips is 10.6 seconds. The annotated clips show not only the URL and class of the video but the moment where the relevant behavior starts and its duration. In addition to providing a unified dataset, this paper also includes predictions of each category based on a classic I3D model trained in the Kinetics 400 dataset, which serves as a baseline for the action-recognition methods and analysis on the previous source datasets and models, providing insights for future action-recognition-based systems that can be used to assist in decisions regarding ASD diagnosis and treatment.