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--- sift:tutorials:tutorial_overview [2024/07/17 15:42] – removed sgranger
+++ sift:tutorials:tutorial_overview [2025/03/14 15:16] (current) – Merged Command Line and Directory Watchers sections. wikisysop
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+====== Tutorial Overview ======
+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 [[Sift:Tutorials:Tutorial_Files|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: [[Sift:Tutorials:Load_and_View_Data|Load signals into Sift and view them]]**
+  * **Tutorial 2: [[Sift:Tutorials:Clean_your_Data|Clean your data]]**
+  * **Tutorial 3: [[Sift:Tutorials:Perform_Principal_Component_Analysis|Perform Principal Component Analysis]]**
+  * **Tutorial 4: [[Sift:Tutorials:Export_Results|Export your results]]**
+==== Principal Component Analysis ====
+[[Sift:Principal_Component_Analysis:Using_Principal_Component_Analysis_in_Biomechanics|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:Tutorials: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:Tutorials: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:Tutorials:Outlier_Detection_with_PCA|PCA Outlier Detection]]**: This tutorial shows how you can use outlier detection methods to find outliers from your PCA analysis.
+==== Statistical Parametric Mapping ====
+  * **[[sift:tutorials:perform_statistical_parametric_mapping|Perform Statistical Parametric Mapping]]**: This tutorial explores the uses of SPM in Sift, and how you can use it to draw useful analysis.
+==== Building a Normal Database ====
+  * **[[sift:tutorials:build_normal_database|Build a Normal Database]]**: This tutorial explores the uses of the Normal Database builder in Sift to generate a reference dataset.
+  * **[[sift:tutorials:compute_GPS_and_GDI|Compute Gait Profile Score (GPS) and Gait Deviation Index (GDI)]]**: This tutorial explores how to leverage Sift's Normal Database files to compute GPS and GDI for individual subjects.
+  * **[[Sift:Tutorials:OpenBiomechanics_Project:Analysis_of_Shoulder_Angular_Velocity_Baseball_Pitching|Analysis of Shoulder Angular Velocity between Elite Level and Average Collegiate Pitchers]]**: This tutorial shows you how to compare two groups using the normal database feature.
+==== Gait Scores ====
+  * **[[sift:tutorials:compute_GPS_and_GDI|Compute Gait Profile Score (GPS) and Gait Deviation Index (GDI)]]**: This tutorial explores how to leverage Sift's Normal Database files to compute GPS and GDI for individual subjects.
+==== Command Line Interface and Console Application ====
+  * **[[sift:tutorials:command_line|Batch Processing through the Command Line]]**: This tutorial demonstrates how Sift's command line interface can be used to automate analysis tasks and how these tasks can be automated using the Windows operating system.
+  * **[[sift:tutorials:using_directory_watchers| Automating Work Flow With Directory Watchers]]**: This tutorial demonstrates how Sift's directory watchers can be used to automate an entire processing pipeline via the command line.
+==== 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.
+  * **[[Sift:Tutorials:Treadmill_Walking_in_Healthy_Individuals|Treadmill Walking In Healthy Individuals]]**: This tutorial shows how you can use Visual3D and Sift to automate the processing of large-scale data sets.
+  * **[[Sift:Tutorials:OpenBiomechanics_Project:Build_CMZ_Files|OpenBiomechanics Project: Build CMZs Files]]**: This tutorial shows how you can combine .c3d files and metadata into CMZ files for analysis in Sift.
+  * **[[Sift:Tutorials:OpenBiomechanics_Project:Analysis_of_Baseball_Hitters_at_Different_Levels_of_Competition|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.
+  * **[[Sift:Tutorials:OpenBiomechanics_Project:Analysis_of_Shoulder_Angular_Velocity_Baseball_Pitching|Analysis of Shoulder Angular Velocity between Elite Level and Average Collegiate Pitchers]]**: This tutorial shows you how to compare two groups using the normal database feature.