TY - JOUR
T1 - Trend-centric motion visualization
T2 - Designing and applying a new strategy for analyzing scientific motion collections
AU - Schroeder, David
AU - Korsakov, Fedor
AU - Knipe, Carissa Mai Ping
AU - Thorson, Lauren
AU - Ellingson, Arin M.
AU - Nuckley, David
AU - Carlis, John
AU - Keefe, Daniel F.
N1 - Publisher Copyright:
© 1995-2012 IEEE.
PY - 2014/12/31
Y1 - 2014/12/31
N2 - 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.
AB - 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.
KW - Design studies
KW - biomedical and medical visualization
KW - focus + context techniques
KW - integrating spatial and non-spatial data visualization
KW - visual design
UR - http://www.scopus.com/inward/record.url?scp=84910095422&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84910095422&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2014.2346451
DO - 10.1109/TVCG.2014.2346451
M3 - Article
C2 - 26356978
AN - SCOPUS:84910095422
SN - 1077-2626
VL - 20
SP - 2644
EP - 2653
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
IS - 12
M1 - 6875981
ER -