"Analyzing dance moves across cultures using AI-driven movement classification," presented by Angelos Barmpoutis, Qudus Onikeku, and Wenbin Guo

In this presentation, an interdisciplinary team from from the Digital Worlds Institute, the Center for Arts in Medicine, and the Center for the Arts, Migration, and Entrepreneurship will present the results of the project "AI-driven Movement Classification and Analysis across Clinical and Cultural Applications" funded by the UF 2020 AI Catalyst Seed fund.

Human movement has been studied in multiple disciplines, including health sciences (biomechanics, kinesiology, neurology, sports medicine) and the Arts (theater and dance, cultural studies) as well as their intersection (arts in medicine, dance therapy), resulting in a large but disparate assortment of multi-modal datasets, including video, skeletal motion capture, manual annotations, and clinical metadata. Traditional data collection processes often include Laban movement analysis, a standardized form of human movement annotation that parameterizes the observed motion changes in a pre-defined 4-dimensional feature space (effort, space, shape, body timing/phrasing). Such analysis requires lengthy manual input from professionals who annotate the recorded data through a time-consuming "watch and pause" process, which is also prone to human errors. In this project, we propose to use AI to fully automate the annotation process involved in Laban analysis by using deep learning algorithms on existing human motion datasets of video and skeletal sequences. The trained model will then be tested in Laban-annotating existing video and skeletal sequences and validated by arts in medicine practitioners and experts in Laban analysis. This AI-driven project will have a significant impact as it will enable automated classification and understanding of human motion across a spectrum of movement-centered disciplines, including clinical and telehealth settings, orthopedic centers, choreographic practice, and cross-cultural movement analysis.

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