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======Dataminer======
The datamining tool determines and extracts specific defined parameters (single values) from timeseries data, e.g. maximum, minimum or mean values of specific phases or at specific positions (events). Details are described in the [[http://nimue-platform.de/doku.php?id=analysistools:motiondataminer|Dataminer platform documentation]].

=====Features=====
For a long time in the Gaitlab Heidelberg the matlab based tools "GDMiner" and "Parametersuche" is used. Last one is part of the Database "HeiDataProVIT". The Datmining functionality of the GaitlabHdTool
======Gait indizes======
=====Gait profile score======
=====Gait deviation index======
======Generic data mining======
This datamining tool was orginally implemented for the application "Upperlimb" and is now available also for gait data.
=====Advantages in comparison to GDMiner=====
   * GDMiner can work only with Heidelberg-ASCII average files (gla) and assumes that all timeseries are time-normalized and have a prefix "Mean" or "Std". The dataminer tool of the Nimue-Platform can work with aribtray files: gla, glx, glm, c3d, d3d, ... 
   * Its own robust xml-file with path lists, no problems with non printable characters (import function for old csv-file is available)
   * More flexibility in definition of phases. Phases can start with RTO. This was needed for Evas project.
======Working with Session Sets======
A Session Set defines a list of paths to data sessions and can include additional columns with allocated data. The data is organized as a table with rows and columns. 
=====Import of csv-files======

A csv-file as it can be exported via Excel or HeiDataProVIT can be imported in a Nimue-Platform Project. It is saved as an xml-file in the subfolder "Data Sets" in the project.

Gait- and Motionlab Heidelberg Tools

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