Combining Automated and Interactive Visual Analysis of Biomechanical Motion Data

Abstract. 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.

• Type: Journal
• Year: 2010
• Where: ISVC
• Authors: Scott Spurlock, Remco Chang, Xiaoyu Wang, George Arceneaux, Daniel F. Keefe, Richard Souvenir
• Keywords: Applied Computing, Complex 3D Motion, ISVC 2010, Motion Segment, Nonlinear Dimensionality Reduction