Studies of motion are fundamental to science. For centuries, pictures of motion have factored importantly in making scientific discoveries possible. Today, there is perhaps no tool more powerful than interactive virtual reality (VR) for conveying complex space-time data to scientists, doctors, and others; however, relatively little is known about how to design virtual environments in order to best facilitate these analyses. In designing virtual environments for presenting scientific motion data (e.g., 4D data captured via medical imaging or motion tracking) our intuition is most often to “reanimate” these data in VR, displaying moving virtual bones and other 3D structures in virtual space as if the viewer were watching the data being collected in a biomechanics lab. However, recent research in other contexts suggests that although animated displays are effective for presenting known trends, static displays are more effective for data analysis.

Abstract We present a study of interactive virtual reality visualizations of scientific motions as found in biomechanics experiments. Our approach is threefold. First, we define a taxonomy of motion visualizations organized by the method (animation, interaction, or static presentation) used to depict both the spatial and temporal dimensions of the data. Second, we design and implement a set of eight example visualizations suggested by the taxonomy and evaluate their utility in a quantitative user study. Third, together with biomechanics collaborators, we conduct a qualitative evaluation of the eight example visualizations applied to a current study of human spinal kinematics. Results suggest that visualizations in this style that use interactive control for the time dimension of the data are preferable to others. Within this category, quantitative results support the utility of both animated and interactive depictions for space; however, qualitative feedback suggest that animated depictions for space should be avoided in biomechanics applications.

We present an interactive framework for exploring space-time and form-function relationships in experimentally collected high-resolution biomechanical data sets. These data describe complex 3D motions (e.g. chewing, walking, flying) performed by animals and humans and captured via high-speed imaging technologies, such as biplane fluoroscopy. In analyzing these 3D biomechanical motions, interactive 3D visualizations are important, in particular, for supporting spatial analysis. However, as researchers in information visualization have pointed out, 2D visualizations can also be effective tools for multi-dimensional data analysis, especially for identifying trends over time. Our approach, therefore, combines techniques from both 3D and 2D visualizations. Specifically, it utilizes a multi-view visualization strategy including a small multiples view of motion sequences, a parallel coordinates view, and detailed 3D inspection views. The resulting framework follows an overview first, zoom and filter, then details-on-demand style of analysis, and it explicitly targets a limitation of current tools, namely, supporting analysis and comparison at the level of a collection of motions rather than sequential analysis of a single or small number of motions. Scientific motion collections appropriate for this style of analysis exist in clinical work in orthopedics and physical rehabilitation, in the study of functional morphology within evolutionary biology, and in other contexts. An application is described based on a collaboration with evolutionary biologists studying the mechanics of chewing motions in pigs. Interactive exploration of data describing a collection of more than one hundred experimentally captured pig chewing cycles is described.