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| <b>Visual3D</b> is an incredibly accurate, flexible, and useful tool to process motion capture data. | | <b>Inspect3D</b> is the software tool for biomechanics researchers with large motion capture data sets. |
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| However, it is primarily session based and users often assume the responsibility of grouping data and calculating metrics. This is where <b><span style="font-family:Garamond;color:darkred;font-size:120%">Inspect3D</span></b> comes in - it allows users to easily group and plot data, exclude "bad" cycles of data, and export metrics.
| | Inspect3D allows users to: load multiple [[C3D_Format|C3D]] files, detect and remove outliers, group signals based on custom conditions, analyse data, and produce visualizations every step of the way. |
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| [[File:Example.mp4|1000px|start=2|end=6]] | | Inspect3D is designed to integrate seamlessly with [[Visual3D_Overview|Visual3D]]. Where Visual3D is primarily a session-based tool for processing motion capture data, Inspect3D lets researchers take their Visual3D results and process them at study-level. |
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| The video above describes how to load an existing CMO library, normalize all signals in the Link Model Based folder (ex. Joint Angles, Moments, etc.), average left and right sides together, then calculate, plot and export the mean data, all done in under five minutes.
| | At its heart, Inspect3D is all about helping researchers through the knowledge discovery process: <b>collecting</b>, <b>cleaning</b>, <b>shaping</b>, and <b>analysing</b> their data before <b>communicating</b> their results. |
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| =Loading Data: CMO Library=
| | :<b>Collecting data</b>: Inspect3D lets you load [[CMO_Format|CMO]] files containing all of the [[C3D_Format|C3D]] files associated with your study. |
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| Inspect3D uses the CMO Library to read data. So each subject/session should have a CMZ file created by Visual3D, and Inspect3D should point to the root directory.
| | :<b>Cleaning data</b>: Your data can be visualized easily as individual traces, workspace means, or group means. You can click on specific cycles and choose to exclude them from analysis. Early data visualization and formal outlier detection techniques help you ensure that only valid data is used for your analysis. |
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| {| class="wikitable mw-collapsible mw-collapsed" width="100%"
| | :<b>Shaping data</b>: A single study can contain multiple questions, each looking at the underlying data in different ways. Inspect3D can automatically group signals for you or you can define your own custom queries based on tags, events, or expressions. Common signal groups include Left and Right signals, Affected and Unaffected sides, and Pitching vs. Non-pitching sides. |
| ! style="text-align:left;" | Example Video
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| | [[File:Inspect3DIntroduction_CMO_Library.mp4|1000px]]
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| =Manually Grouping Data=
| | :<b>Performing analysis</b>: Inspect3D implements common data analysis techniques such as [[Metrics|summary statistics]] calculation, [[principal component analysis]], and [[clustering]] algorithms. |
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| Once the CMO Library path is defined, you can define groups to indicate what signals you want to analyze, and how you want them grouped (ex. average all right/left sagittal ankle angles for treadmill trials). Data can be grouped based on tags, events, signals or expressions.
| | :<b>Communicating results</b>: Analysis results can be exported to a number of different text formats including [[Visual3D_ASCII_Format|Visual3D ASCII]], [[P2D_Format|P2D]], and SPSS. Each of the different visualization tools also give you complete control over colours used, line styles, and axis labels to allow you to produce the figures that you want. |
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| Examples of common groupings are:
| | Ready to see how you can used Inspect3D in your research? [[Get Started]]! |
| * Right & Left signals (ex. control data base)
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| ** Right Ankle Angle + Left Ankle Angle
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| * Affected/Unaffected
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| ** Affected Right Ankle Angle + Affected Left Ankle Angle vs Unaffected Right Ankle Angle + Unaffected Left Ankle Angle
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| * Pitching
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| ** Pitching Side vs Non pitching side
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| {| class="wikitable mw-collapsible mw-collapsed" width="100%"
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| ! style="text-align:left;" | Example Video
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| | [[File:Inspect3DIntroduction_DefiningGroups.mp4|1000px]]
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| =Plotting and Inspecting Data=
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| Once you have defined the signals you are interested in viewing, you can plot them easily as individual traces, workspace means, or group means. You can click on specific cycles and choose to exclude them from analysis.
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| This is an incredibly important <b>quality assurance step</b>.
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| {| class="wikitable mw-collapsible mw-collapsed" width="100%"
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| ! style="text-align:left;" | Example Video
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| | [[File:Inspect3DIntroduction_PlottingData.mp4|1000px]]
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| =Exporting Data=
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| Normalized signal data or metrics such as max/min can be exported to a text file. Text data can be exported in the default Visual3D text file format, but other formats are available as well.
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| {| class="wikitable mw-collapsible mw-collapsed" width="100%"
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| ! style="text-align:left;" | Example Video
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| | [[File:Inspect3DIntroduction_Exporting.mp4|1000px]]
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| =Autogenerating Groups=
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| Defining the signals you are interested in analyzing may be tedious, so an automatic signal generation tool is available. Group names can be modified or removed after they've been defined.
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| {| class="wikitable mw-collapsible mw-collapsed" width="100%"
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| ! style="text-align:left;" | Example Video
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| | [[File:Inspect3DIntroduction_AutoGroups.mp4|1000px]]
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| =Working with Metrics=
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| Inspect3D also allows you to view and analyze metric data calculated in Visual3D on bar charts. | |
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| {| class="wikitable mw-collapsible mw-collapsed" width="100%"
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| ! style="text-align:left;" | Example Video
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| | [[File:Inspect3DIntroduction_Metrics.mp4|1000px]]
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| =Principal Component Analysis (PCA)=
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| The original intention of Inspect3D was actually to perform waveform based Principal Component Analysis (PCA). PCA was developed in Collaboration with Dr. Kevin Deluzio at Queen's University, and follows the explanation of the analysis described in [http://www.c-motion.com/textbook Research methods in Biomechanics].
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| !Principal Component Analysis (more....) | |
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| For a step-by-step example of how to use Inspect3D PCA see our [[Inspect3D_Tutorial_3| Principal Components Analysis Tutorial]].
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| From each signal we could identify many isolated metrics (e.g. maximum and minimum values) and compare the signals based on a comparison of these metrics. Given that there is a common underlying shape to all of these signals, it might be better to use a multivariate statistical technique that can capture this basic shape and compare the shape of each signal to this underlying shape.
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| [[File:I3DCleanData2.png|275px|caption]]
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| Principal component analysis (PCA) is a multi-variate statistical analysis that reduces the high-dimensional matrix of correlated, time varying signals into a low-dimensional and statistically uncorrelated set of principal components (PC). The PC scores represent the most important features of the data, e.g. the overall magnitude or the shape of the time series at a particular point in the stride cycle. The value of each particular subject’s score represents how strongly that feature was present in the data. Each of the principal components explains variance in the original signals
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| Our implementation of Principal Component Analysis is based on the following article:
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| :Deluzio KJ and Astephen JL (2007) Biomechanical features of gait waveform data associated with knee osteoarthritis. An application of principal component anslysis. Gait & Posture 23. 86-93 ([http://m.me.queensu.ca/People/Deluzio/files/PublishedArticle.pdf pdf])
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| :'''Abstract'''
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| :This study compared the gait of 50 patients with end-stage knee osteoarthritis to a group of 63 age-matched asymptomatic control subjects. The analysis focused on three gait waveform measures that were selected based on previous literature, demonstrating their relevance to knee osteoarthritis (OA): the knee flexion angle, flexion moment, and adduction moment. The objective was to determine the biomechanical features of these gait measures, related to knee osteoarthritis. Principal component analysis was used as a data reduction tool, as well as a preliminary step for further analyses to determine gait pattern differences between the OA and the control groups. These further analyses included statistical hypothesis testing to detect group differences, and discriminant analysis to quantify overall group separation and to establish a hierarchy of discriminatory ability among the gait waveform features at the knee. The two groups were separated with a misclassification rate (estimated by cross-validation) of 8%. The discriminatory features of the gait waveforms were, in order of their discriminatory ability: the amplitude of the flexion moment, the range of motion of the flexion angle, the magnitude of the flexion moment during early stance, and the magnitude of the adduction moment during stance.
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| [[Category:Inspect3D]] | | [[Category:Inspect3D]] |
Inspect3D.
Analytics and Data Exploration for Biomechanics.
Inspect3D is the software tool for biomechanics researchers with large motion capture data sets.
Inspect3D allows users to: load multiple C3D files, detect and remove outliers, group signals based on custom conditions, analyse data, and produce visualizations every step of the way.
Inspect3D is designed to integrate seamlessly with Visual3D. Where Visual3D is primarily a session-based tool for processing motion capture data, Inspect3D lets researchers take their Visual3D results and process them at study-level.
At its heart, Inspect3D is all about helping researchers through the knowledge discovery process: collecting, cleaning, shaping, and analysing their data before communicating their results.
- Collecting data: Inspect3D lets you load CMO files containing all of the C3D files associated with your study.
- Cleaning data: Your data can be visualized easily as individual traces, workspace means, or group means. You can click on specific cycles and choose to exclude them from analysis. Early data visualization and formal outlier detection techniques help you ensure that only valid data is used for your analysis.
- Shaping data: A single study can contain multiple questions, each looking at the underlying data in different ways. Inspect3D can automatically group signals for you or you can define your own custom queries based on tags, events, or expressions. Common signal groups include Left and Right signals, Affected and Unaffected sides, and Pitching vs. Non-pitching sides.
- Performing analysis: Inspect3D implements common data analysis techniques such as summary statistics calculation, principal component analysis, and clustering algorithms.
- Communicating results: Analysis results can be exported to a number of different text formats including Visual3D ASCII, P2D, and SPSS. Each of the different visualization tools also give you complete control over colours used, line styles, and axis labels to allow you to produce the figures that you want.
Ready to see how you can used Inspect3D in your research? Get Started!