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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Combining Automated and Interactive Visual Analysis of Biomechanical Motion Data
verfasst von : Scott Spurlock, Remco Chang, Xiaoyu Wang, George Arceneaux IV, Daniel F. Keefe, Richard Souvenir
Erschienen in: Advances in Visual Computing
Verlag: Springer Berlin Heidelberg
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We present a framework for combining automated and interactive visual analysis techniques for use on high-resolution biomechanical data. Analyzing the complex 3D motion of, e.g., pigs chewing or bats flying, can be enhanced by providing investigators with a multi-view interface that allows interaction across multiple modalities and representations. In this paper, we employ nonlinear dimensionality reduction to automatically learn a low-dimensional representation of the data and hierarchical clustering to learn patterns inherent within the motion segments. Our multi-view framework allows investigators to simultaneously view a low-dimensional embedding, motion segment clustering, and 3D visual representation of the data side-by-side. We describe an application to a dataset containing thousands of frames of high-speed, 3D motion data collected over multiple experimental trials.