We present a series of multi-modal spatial interfaces and virtual environments that can be implemented with widely accessible virtual reality (VR) technologies. The results demonstrate and evaluate the new degree to which rich virtual experiences involving motion sensing, physiological inputs, stereoscopic imagery, sound, and haptic feedback can now be created using low-cost (e.g., mobile phone based) VR environments. Adapting spatial interfaces to these new platforms can open up exciting new application areas for VR. This is demonstrated through a series of prototype systems aimed at delivering in-home VR therapies to patients suffering from persistent pain conditions (e.g. arthritis pain, cancer pain). A rich spatial interface and visual aesthetic is particularly important for the success of these applications; thus an interdisciplinary team with expertise in technology, design, meditation, and the psychology of pain worked together to iteratively develop and evaluate the current prototypes.

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.

In biomechanics studies, researchers collect, via experiments or simulations, datasets with hundreds or thousands of trials, each describing the same type of motion (e.g., a neck flexion-extension exercise) but under different conditions (e.g., different patients, different disease states, pre- and post-treatment). Analyzing similarities and differences across all of the trials in these collections is a major challenge. Visualizing a single trial at a time does not work, and the typical alternative of juxtaposing multiple trials in a single visual display leads to complex, difficult-to-interpret visualizations. We address this problem via a new strategy that organizes the analysis around motion trends rather than trials. This new strategy matches the cognitive approach that scientists would like to take when analyzing motion collections. We introduce several technical innovations making trend-centric motion visualization possible. First, an algorithm detects a motion collection’s trends via time-dependent clustering. Second, a 2D graphical technique visualizes how trials leave and join trends. Third, a 3D graphical technique, using a median 3D motion plus a visual variance indicator, visualizes the biomechanics of the set of trials within each trend. These innovations are combined to create an interactive exploratory visualization tool, which we designed through an iterative process in collaboration with both domain scientists and a traditionally-trained graphic designer. We report on insights generated during this design process and demonstrate the tool’s effectiveness via a validation study with synthetic data and feedback from expert musculoskeletal biomechanics researchers who used the tool to analyze the effects of disc degeneration on human spinal kinematics.

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