Using Principal Component Analysis in Biomechanics

Sift provides a number of ways to visualize and interact with the results of PCA. An overview of all PCA visualizations can be found in Sift - Analyse Page. More detail on the mathematics behind PCA can be found on our page: The Math of Principal Component Analysis (PCA).

This page includes:

• Visualizing the variance explained by each PC individually;
• Visualizing the variance explained by each PC at each point in the signal's cycle;
• Scatter-plotting workspace scores in PC-space;
• Showing the distribution of scores by group for each PC;
• Visualizing the mean and extreme values that result from reconstructing the underlying data with each PC; and
• Visualizing how the signals can be reconstructed from the computed PCs.

Sift has several built in modules to take you further with you PCA Analysis:

• Outlier Detection for PCA: An overview of the PCA outlier detection methods built into Sift.
• Mahalanobis Distances: Finding outliers through their Mahalanobis Distances.
• Squared Prediction Error: Finding outliers through their SPE.
• Local Outlier Factors: Finding outliers through the Local Outlier Factor.
• K-Means Analysis: Clustering PCA results through K-Means clustering.

For a step-by-step example of how to use Sift to perform PCA on your data, see the PCA Tutorial.

For a step-by-step example of how to use Sift to perform further k-means clustering on PCA results, see the Tutorial: Run K-Means.

For a step-by-step example of how to use Sift to perform outlier analysis on PCA results, see the Tutorial: Run PCA Outlier Analysis.

For a step-by-step example of processing and analyzing large data sets in Sift and using PCA to distinguish between groups, see the Tutorial: Treadmill Walking In Healthy Individuals and the Tutorial: Analysis of Baseball Hitters.