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Movement Quality

Visualization
For Wheelchair Dance


inclusive design, motion capture, movement recognition, visualization, interaction, laban movement analysis


Wheelchair dance is a significant form of disability art, but it faces ableism and exclusion. This research project develops a visualization system based on Laban Movement Analysis (LMA) to address these challenges. The system uses video with machine learning (MediaPipe) post-processing to identify body landmarks and create visualizations of LMA Effort quality. Evaluation shows effectiveness with both general public and experts. Implementation of such system can support the artistic legitimization of wheelchair dance in education, communication, performance, and documentation.

The resulting paper has been awarded as best art paper at ACM SIGGRAPH 2023 (access).

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