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MaD GUI: An Open-Source Python Package for Annotation and Analysis of Time-Series Data.

Malte Ollenschläger1,2, Arne Küderle1, Wolfgang Mehringer1

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Summary

A new open-source Python package, Machine Learning and Data Analytics (MaD) GUI, streamlines time-series data annotation. It enhances usability and user experience for both developers and domain experts, significantly reducing annotation time.

Keywords:
annotationgait analysisgraphical user interfacepythontime series analysis

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Manual annotation of time-series data is crucial for developing machine learning algorithms.
  • Existing Python packages lack adaptability, usability, and user experience.
  • Graphical User Interfaces (GUIs) are essential tools for data annotation.

Purpose of the Study:

  • To develop a generic, open-source Python package for time-series data annotation and analysis.
  • To enhance adaptability, usability, and user experience in GUI development for machine learning.
  • To enable both developers and non-programmers (domain experts) to efficiently annotate and analyze time-series data.

Main Methods:

  • Development of the Machine Learning and Data Analytics (MaD) GUI package.
  • Testing the adaptability of MaD GUI by developers.
  • Evaluating the user interface of MaD GUI with clinicians as domain experts.
  • Comparative analysis against a state-of-the-art package.

Main Results:

  • MaD GUI significantly reduces GUI creation time for developers, saving up to 75% compared to existing tools.
  • Developers and clinicians showed a preference for MaD GUI regarding usability and user experience.
  • MaD GUI facilitates efficient time-series data annotation for domain experts without programming skills.

Conclusions:

  • MaD GUI offers a flexible and user-friendly solution for time-series data analysis.
  • The package effectively lowers the barrier for developing and utilizing machine learning models on time-series data.
  • MaD GUI improves the overall workflow for both developers and domain experts in machine learning projects.