Inspect3D Overview

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Inspect3D. Analytics and Data Exploration for Biomechanics.

Visual3D is an incredibly accurate, flexible, and useful tool to process motion capture data.

However, it is primarily session based and users often assume the responsibility of grouping data and calculating metrics. This is where Inspect3D comes in - it allows users to easily group and plot data, exclude "bad" cycles of data, and export metrics.

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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.

Loading Data: CMO Library

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.

Example Video
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Manually Grouping Data

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.

Examples of common groupings are:

  • Right & Left signals (ex. control data base)
    • Right Ankle Angle + Left Ankle Angle
  • Affected/Unaffected
    • Affected Right Ankle Angle + Affected Left Ankle Angle vs Unaffected Right Ankle Angle + Unaffected Left Ankle Angle
  • Pitching
    • Pitching Side vs Non pitching side
Example Video
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Plotting and Inspecting Data

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.

This is an incredibly important quality assurance step.

Example Video
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Exporting Data

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.

Example Video
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Autogenerating Groups

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.

Example Video
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Working with Metrics

Inspect3D also allows you to view and analyze metric data calculated in Visual3D on bar charts.

Example Video
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Principal Component Analysis (PCA)

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 Research methods in Biomechanics.

Principal Component Analysis (more....)

For a step-by-step example of how to use Inspect3D PCA see our Principal Components Analysis Tutorial.



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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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


Our implementation of Principal Component Analysis is based on the following article:


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 (pdf)


Abstract
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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