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. © 2010 Springer-Verlag.
CITATION STYLE
Spurlock, S., Chang, R., Wang, X., Arceneaux IV, G., Keefe, D. F., & Souvenir, R. (2010). Combining automated and interactive visual analysis of biomechanical motion data. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6454 LNCS, pp. 564–573). https://doi.org/10.1007/978-3-642-17274-8_55
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