Differences

This shows you the differences between two versions of the page.

--- sift:documentation:knowledge_discovery_for_biomechanical_data [2026/04/13 20:53] – updated pca link wikisysop
+++ sift:documentation:knowledge_discovery_for_biomechanical_data [2026/04/13 20:55] (current) – updating curve registration link wikisysop
@@ Line 29: / Line 29: @@
 Sift lets you shape your data by [[Sift:Documentation:Query|querying]] the library. A query defines which signals and traces you want to extract from the library and executing a query produces a group in the queried group widget. Sift can automatically define queries for you or you can save these as [[Other:Inspect3D:Documentation:Files:Q3D_Query_File|.q3d files]] for easy reuse and to help you track exactly how you performed you analysis. If you decide that you can't quite get the data you want from you queries or for some reason you can't work with it, try going back to the collection and cleaning stages of the process. Read about Sift's [[Sift:Application:Query_Builder_Dialog|Query Builder]] dialog to learn more about how you can define and refine groups of signals and read about how to query [[Sift:Documentation:Subject#Querying_Based_on_Subject|multi-subject]] data.
-Beyond deciding which traces should be assigned to which group, we can also shape our data by deciding if and how these traces should be transformed. Sift's default behaviour is to time-normalize queried traces to 101 points, but users can also register their traces to intermediate events and points of interest using Sift's [[sift:curve_registration:curve_registration_for_biomechanical_waveforms|curve registration]] feature.
+Beyond deciding which traces should be assigned to which group, we can also shape our data by deciding if and how these traces should be transformed. Sift's default behaviour is to time-normalize queried traces to 101 points, but users can also register their traces to intermediate events and points of interest using Sift's [[sift:curve_registration|curve registration]] feature.
 ===== Analysing Data =====
 Having queried and shaped your clean dataset to get exactly the traces and metrics that you wanted, now you're finally ready to start analyzing. We learn a lot about analytical techniques in our courses and throughout our formal training, but it's only one of the five steps here. Even though this is what we often think of as the difficult work of research, you've already put in a lot of effort to get through the first three steps and get to this point!
-The type of analysis you perform is obviously going to depend on the question you're trying to answer and the dataset that you have. Sift implements a range of common data analysis techniques such as summary statistics calculation, [[Sift:Principal_Component_Analysis|Principal Component Analysis (PCA)]], [[sift:statistical_parametric_mapping:the_math_of_statistical_parametric_mapping_spm|Statistical Parametric Mapping]], [[Sift:Dynamic_Time_Warping:Dynamic_Time_Warping|Dynamic Time Warping (DTW)]], [[Sift:Gait_Measures:Global_Gait_Asymmetry|Global Gait Asymmetry (GGA)]], [[Sift:Gait_Measures:Gait_Profile_Score|Gait Profile Score (GPS)]], and clustering algorithms, with new techniques being added. Sometimes your analysis will prompt new questions or new ways of looking at your data. Don't be afraid to go back to collecting or shaping you data to see what else you might find.
+The type of analysis you perform is obviously going to depend on the question you're trying to answer and the dataset that you have. Sift implements a range of common data analysis techniques such as summary statistics calculation, [[Sift:Principal_Component_Analysis|Principal Component Analysis (PCA)]], [[sift:statistical_parametric_mapping|Statistical Parametric Mapping]], [[Sift:Dynamic_Time_Warping:Dynamic_Time_Warping|Dynamic Time Warping (DTW)]], [[Sift:Gait_Measures:Global_Gait_Asymmetry|Global Gait Asymmetry (GGA)]], [[Sift:Gait_Measures:Gait_Profile_Score|Gait Profile Score (GPS)]], and clustering algorithms, with new techniques being added. Sometimes your analysis will prompt new questions or new ways of looking at your data. Don't be afraid to go back to collecting or shaping you data to see what else you might find.
-Complete the [[Sift:Tutorials:Perform_Principal_Component_Analysis|tutorial]] for performing PCA and the [[Sift:Tutorials:Perform_Statistical_Parametric_Mapping|tutorial]] for performing SPM to see some of the different ways you can analyze your data.
+Complete the [[Sift:Tutorials:Perform_Principal_Component_Analysis|performing PCA tutorial]] and the [[Sift:Tutorials:Perform_Statistical_Parametric_Mapping|performing SPM tutorial]] to see some of the different ways you can analyze your data.
 ===== Communicating Results =====