Inspect3D Tutorial 3: Difference between revisions
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4.2 After clicking Run PCA on Selected Groups, the results should appear in the PCA Graphs on on the far right side of the screen. | 4.2 After clicking Run PCA on Selected Groups, the results should appear in the PCA Graphs on on the far right side of the screen. | ||
[[Image:Figure2_varianceExplained_2.png]] | |||
4.3 If these are not visible then open the [[Image:I3D_PCAOptions2.png|30px]] <b>PCA Options</b> dropdown menu and then select [[Image:I3DShowGraphs2.png|30px]] <b>Show PCA Graphs</b>. | 4.3 If these are not visible then open the [[Image:I3D_PCAOptions2.png|30px]] <b>PCA Options</b> dropdown menu and then select [[Image:I3DShowGraphs2.png|30px]] <b>Show PCA Graphs</b>. | ||
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Revision as of 18:20, 28 October 2022
Language: | English • français • italiano • português • español |
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This tutorial will show you how to use Inspect3D in order to perform Principal Component Analysis (PCA) using data from a CMO library. For a full treatment of waveform-based PCA to find differences in waveform data, see the explanation presented in Research Methods in Biomechanics.
For this tutorial, we will be comparing the the knee flexion angles between participants with osteoarthritis and the normal control group. Our problem is to provide an explanation for differences in knee flexion angles between osteoarthritic walking versus normal walking. We can accomplish this by defining two groups that meet these signal definitions, performing PCA, and interpreting the results.
Data
This tutorial uses overground walking data from roughly 100 subjects. The data is divided into two conditions, normal control and osteoarthritis (moderate to severe). This data set is included in the download for Inspect3D in the demo folder.
Load and query the CMO library
As with previous tutorials, we begin by loading the CMO library and defining the queries relevant to our question. In this case we will manually create two queries based on tags.
We begin by defining a query for subjects with the OA tag (indicating osteoarthritis).
1. Select Query Dialog
2. Add Query
3. Select Modify Name
4. Enter OA as the name of the Query
5. Select Save and Select Perform Query (Both with the same button). There is only one Condition in this tutorial, so the default names are fine.
6. Select Condition Zero
7. Select the Modify Button
8. Select the Signals Tab (There are no events in this data set)
9. Select the RKnee_Angle Signal (The only signal in this data set)
10. Select the Refinement Tab
11. Select OA (This is a TAG for subjects with Osteoarthritis)
12. Select SAVE
Next we will define a query for subjects with the NC tag (indicating Normal Control). In this case we can easily modify our previous query rather than starting from scratch.
1. Add A New Query
2. Select Modify Name
3. Rename to NC
4. Change the TAG from OA to NC
5. Select Save
6. Select Perform Query
7. Close the Query Dialog
There will now be two Query Groups in the main window's Groups widget. Selecting either of these will display the associated workspaces in the Workspace widget below.
Exploring the data
We can verify the queries that we produced in the first section by visualizing our traces.
1. Select All Groups
2. Select All Signals
3. Select Plot Subject Mean
4. Select Refresh Plot
Inspecting this plot shows that although the participants for the two groups walk very differently (when watching them in person) their knee flexion angles are quite similar. Because the traces overlap significantly between the groups throughout the entire gait cycle, conventional statistics will likely not be useful for describing the differences between these two groups.
This is one of the motivations behind PCA: by transforming our original data into a coordinate system based on principal components we will end up with a few dimensions that explain most of the variance in the data set. This, in turn, will help us to explain and detect the differences between the groups.
Running Principal Component Analysis
Interpreting PCA results
The results from the PCA are described in 6 windows: Variance Explained, Loading Vector, Subject Scores, Group Scores, Extreme Plot, and PC Reconstruction. The views provided by each of these windows are described in full here.