Student Scholar Symposium Abstracts and Posters

Document Type

Poster

Publication Date

Fall 12-5-2024

Faculty Advisor(s)

Dr. Franceli Cibrian

Abstract

The structure of dance classrooms has remained largely unchanged for years, with minimal integration of technology to enhance teaching. This has motivated our research project, which aims to capture dance movements using wearable sensors and translate the information into meaningful visualizations to help dancers improve their skills. As the first step in addressing the research question—can data from commercial wearables differentiate between the movements of dancers and non-dancers?—we developed DANCETAG (Data Analytics and Notation with Captured Event Tagging), a platform designed for data collection and movement annotation. We utilized Sony’s Mocopi sensors, a motion capture system with six sensors attached via velcro straps. These sensors connect to Sony’s app, transmitting data in Biovision Hierarchy (BVH) format for analysis. To answer the research question, we conducted a user study with 34 participants performing 30 movements. A professional dancer served as a baseline for comparing movements against other participants using the Dynamic Time Warping algorithm. Our initial visualization method generated heatmaps depicting these differences. Afterward, we grouped participants based on their dancing abilities: dancers (n=13), physically active individuals (n=18), and non-dancers (n=21). T-tests revealed significant differences between these groups, indicating that movements involving weight shifts showed greater similarity to the baseline than movements with more variation, such as walking. These findings suggest that wearable data analysis can effectively reveal movement similarities and differences among varying skill levels. We are currently developing alternative visualizations to provide practical insights for both instructors and students. Our goal is not to replace instructors with technology but to use it as a supplement to enhance feedback. This approach improves traditional teaching methods and introduces new possibilities for dance education. Future work will focus on refining visualizations and exploring additional technological applications.

Acknowledgment: Fowler Undergraduate Research Grant, SURF, NSF 2245495, Hector, Daisy, and participants.

Comments

Presented at the Fall 2024 Student Scholar Symposium at Chapman University.

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