Sift - Tutorials: Difference between revisions
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[[Sift - Principal Component Analysis|PCA]] is a key analytical feature in Sift, allowing you to represent complex biomechanicals waveforms in low-dimensional spaces while maintaining most of the waveforms' information. This tutorial will provide you with an overview of how to perform PCA in Sift and of your options for follow-on analysis. | [[Sift - Principal Component Analysis|PCA]] is a key analytical feature in Sift, allowing you to represent complex biomechanicals waveforms in low-dimensional spaces while maintaining most of the waveforms' information. This tutorial will provide you with an overview of how to perform PCA in Sift and of your options for follow-on analysis. | ||
*'''[[ | *'''[[Sift Tutorial: Perform_Principal Component Analysis|Perform Principal Component Analysis]]''': This tutorial provides an overview of performing PCA. This tutorial is the same as the PCA tutorial in the "Getting Started" section. | ||
*'''[[Sift Tutorial: Run K-Means|Run K-Means]]''': This tutorial shows how you can use the k-means algorithms to cluster the results of PCA analysis. | |||
*'''[[Sift Tutorial: Outlier Detection with PCA|PCA Outlier Detection]]''': This tutorial shows how you can use outlier detection methods to find outliers from your PCA analysis. | |||
==Public Data Sets== | ==Public Data Sets== | ||
Latest revision as of 18:12, 1 May 2024
| Language: | English • français • italiano • português • español |
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Get more comfortable with all that Sift has to offer by working through the following tutorials.
Tutorial Data Files
Sift's tutorials use real data sets wherever possible in order to demonstrate realistic scenarios and avoid overly simplistic examples. Download links to the tutorial datasets can be found here.
Getting Started
Working through these four tutorials will provide you with an overview of how Sift lets you analyse your motion capture data sets from start to finish.
- Tutorial 1: Load signals into Sift and view them
- Tutorial 2: Clean your data
- Tutorial 3: Perform Principal Component Analysis
- Tutorial 4: Export your results
Principal Component Analysis
PCA is a key analytical feature in Sift, allowing you to represent complex biomechanicals waveforms in low-dimensional spaces while maintaining most of the waveforms' information. This tutorial will provide you with an overview of how to perform PCA in Sift and of your options for follow-on analysis.
- Perform Principal Component Analysis: This tutorial provides an overview of performing PCA. This tutorial is the same as the PCA tutorial in the "Getting Started" section.
- Run K-Means: This tutorial shows how you can use the k-means algorithms to cluster the results of PCA analysis.
- PCA Outlier Detection: This tutorial shows how you can use outlier detection methods to find outliers from your PCA analysis.
Public Data Sets
Exploring publicly available data with Sift is a great way to understand the original paper, learn about Sift's features, and get ideas for your own work.
- Treadmill Walking In Healthy Individuals: This tutorial shows how you can use Visual3D and Sift to automate the processing of large-scale data sets.
- OpenBiomechanics Project: Build CMZs Files: This tutorial shows how you can combine .c3d files and metadata into CMZ files for analysis in Sift.
- Analysis of Baseball Hitters at Different Levels of Competition: This tutorial shows how you can use Sift to automate the processing of large-scale data sets, and how metadata can be used to help you refine queries.