Sift - Tutorials: Difference between revisions

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(Renamed tutorial links in the "Getting Started" section.)
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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.
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: [[Sift Tutorial - Load and View Data|Load signals into Inspect3D and view them]]'''
*'''Tutorial 1: [[Sift Tutorial - Load and View Data|Load signals into Sift and view them]]'''
*'''Tutorial 2: [[Sift Tutorial - Clean Your Data]]'''
*'''Tutorial 2: [[Sift Tutorial - Clean Your Data|Clean your data]]'''
*'''Tutorial 3: [[Sift Tutorial: Perform Principal Component Analysis|Perform Principal Component Analysis]]'''
*'''Tutorial 3: [[Sift Tutorial: Perform Principal Component Analysis|Perform Principal Component Analysis]]'''
*'''Tutorial 4: [[Sift Tutorial: Export Results|Export your results]]'''
*'''Tutorial 4: [[Sift Tutorial: Export Results|Export your results]]'''

Revision as of 19:11, 16 April 2024

Language:  English  • français • italiano • português • español 

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.

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.

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.

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