:orphan:

.. _revalidation:

Revalidation
============

This page shows the most up-to-date results on the validation of all algorithms implemented in mobgap.
The validation is performed based on the Mobilise-D TVS dataset, which can be downloaded from `Zenodo <https://zenodo.org/records/13987963>`_.

As most mobgap algorithms are re-implementations of algorithms that were originally available only in Matlab, the revalidation contains comparisons with these original implementations.
For each algorithm/algorithmic block, two types of subpages are available on this page:
First, an overview of the analytic results and second, a document that contains the code to reproduce these results.
Both files are computational notebooks that can be downloaded either from Github or by using the available download button on the respective pages.

In case you are interested in performing additional analysis, we recommend starting by downloading these notebooks as a starting point.
The notebooks will automatically download the required files containing the results from the `result repository <https://github.com/mobilise-d/mobgap_validation>`_ and are mostly self-contained and only require mobgap to be installed to run them.
They are further written in a way that all relevant code is directly available in the notebook and not deeply hidden in some library, which should allow easy modification.
The code available to generate the results can also be used as a starting point to run the same validation on any other dataset.

These results will be regularly updated when there are major updates to the algorithms and new releases of the TVS dataset.
The current results are generated using the TVS dataset version 1.0.2 (released at 2025-07-11).

To view older versions of this documentation and results, use the version selector in the bottom right of this page.



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Full Pipeline
-------------



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    <div class="sphx-glr-thumbcontainer" tooltip="The following provides an analysis and comparison of the Mobilise-D algorithm pipeline on the Mobilise-D Technical Validation Study (TVS) dataset for the estimation of walking speed (free-living). In this example, we look into the performance of the Python implementation of the pipeline compared to the reference data. We also compare the actual performance to that obtained by the original Matlab-based implementation  [1]_.">

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  .. image:: /auto_revalidation/full_pipeline/images/thumb/sphx_glr__01_pipeline_analysis_thumb.png
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  :ref:`sphx_glr_auto_revalidation_full_pipeline__01_pipeline_analysis.py`

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      <div class="sphx-glr-thumbnail-title">Walking speed estimation</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="The following provides an analysis and comparison of the Mobilise-D algorithm pipeline on the Mobilise-D Technical Validation Study (TVS) dataset for the estimation of cadence (free-living). In this example, we look into the performance of the Python implementation of the pipeline compared to the reference data. We also compare the actual performance to that obtained by the original Matlab-based implementation  [1]_.">

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  .. image:: /auto_revalidation/full_pipeline/images/thumb/sphx_glr__02_pipeline_cad_thumb.png
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  :ref:`sphx_glr_auto_revalidation_full_pipeline__02_pipeline_cad.py`

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      <div class="sphx-glr-thumbnail-title">Cadence estimation</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="The following provides an analysis and comparison of the Mobilise-D algorithm pipeline on the Mobilise-D Technical Validation Study (TVS) dataset for the estimation of stride length (free-living). In this example, we look into the performance of the Python implementation of the pipeline compared to the reference data. We also compare the actual performance to that obtained by the original Matlab-based implementation  [1]_.">

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  .. image:: /auto_revalidation/full_pipeline/images/thumb/sphx_glr__03_pipeline_sl_thumb.png
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  :ref:`sphx_glr_auto_revalidation_full_pipeline__03_pipeline_sl.py`

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      <div class="sphx-glr-thumbnail-title">Stride length estimation</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="The following analysis tries to awnser the question how much specific components of the pipeline contribute to the improvements that we see in the new implementation of the full pipeline.">

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  .. image:: /auto_revalidation/full_pipeline/images/thumb/sphx_glr__04_additional_experiments_thumb.png
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  :ref:`sphx_glr_auto_revalidation_full_pipeline__04_additional_experiments.py`

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      <div class="sphx-glr-thumbnail-title">Additional Full Pipeline Experiments</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This script reproduces the validation results on TVS dataset for the Mobilise-D algorithm pipeline. It loads the raw data and calculates three DMOs of interest (cadence, stride length and walking speed) using the full pipeline, including the following blocks:">

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  .. image:: /auto_revalidation/full_pipeline/images/thumb/sphx_glr__99_pipeline_result_generation_no_exc_thumb.png
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  :ref:`sphx_glr_auto_revalidation_full_pipeline__99_pipeline_result_generation_no_exc.py`

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      <div class="sphx-glr-thumbnail-title">Revalidation of the Mobilise-D algorithm pipeline for cadence, stride length and walking speed estimation</div>
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Gait Sequences
--------------



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    <div class="sphx-glr-thumbcontainer" tooltip="The following provides an analysis and comparison of the GSD performance on the TVS dataset (lab and free-living). We look into the actual performance of the algorithms compared to the reference data and compare these results with the performance of the original matlab algorithm.">

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  .. image:: /auto_revalidation/gait_sequences/images/thumb/sphx_glr__01_gsd_analysis_thumb.png
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  :ref:`sphx_glr_auto_revalidation_gait_sequences__01_gsd_analysis.py`

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      <div class="sphx-glr-thumbnail-title">Performance of the gait sequences algorithm on the TVS dataset</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This script reproduces the validation results on TVS dataset for the gait sequence detection algorithms. It load results from the old matlab algorithms and runs the new algorithms on the same data. Performance metrics are calculated on a per-trial/per-recording basis and aggregated (mean for most metrics) over the whole dataset. The raw detected gait sequences and all performance metrics are saved to disk.">

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  .. image:: /auto_revalidation/gait_sequences/images/thumb/sphx_glr__02_gsd_result_generation_no_exc_thumb.png
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  :ref:`sphx_glr_auto_revalidation_gait_sequences__02_gsd_result_generation_no_exc.py`

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      <div class="sphx-glr-thumbnail-title">Revalidation of the gait sequence detection algorithms</div>
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Initial Contacts
----------------



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    <div class="sphx-glr-thumbcontainer" tooltip="The following provides an analysis and comparison of the icd performance on the TVS dataset (lab and free-living). We look into the actual performance of the algorithms compared to the reference data and compare these results with the performance of the original matlab algorithm.">

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  .. image:: /auto_revalidation/initial_contacts/images/thumb/sphx_glr__01_icd_analysis_thumb.png
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  :ref:`sphx_glr_auto_revalidation_initial_contacts__01_icd_analysis.py`

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      <div class="sphx-glr-thumbnail-title">Performance of the initial contact algorithms on the TVS dataset</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This script reproduces the validation results on TVS dataset for the initial contact detection algorithms. It load results from the old matlab algorithms and runs the new algorithms on the same data. Performance metrics are calculated on a per-trial/per-recording basis and aggregated (mean for most metrics) over the whole dataset. The raw detected initial contacts and all performance metrics are saved to disk.">

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  .. image:: /auto_revalidation/initial_contacts/images/thumb/sphx_glr__02_icd_result_generation_no_exc_thumb.png
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  :ref:`sphx_glr_auto_revalidation_initial_contacts__02_icd_result_generation_no_exc.py`

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      <div class="sphx-glr-thumbnail-title">Revalidation of the initial contact detection algorithms</div>
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Laterality Classification
-------------------------



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    <div class="sphx-glr-thumbcontainer" tooltip="The following provides an analysis and comparison of the stride length algorithms on the TVS dataset (lab and free-living). We look into the actual performance of the algorithms compared to the reference data.">

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  .. image:: /auto_revalidation/laterality/images/thumb/sphx_glr__01_lrc_analysis_thumb.png
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  :ref:`sphx_glr_auto_revalidation_laterality__01_lrc_analysis.py`

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      <div class="sphx-glr-thumbnail-title">Performance of the laterality classification algorithms on the TVS dataset</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This script reproduces the validation results on TVS dataset for the laterality detection algorithms.">

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  .. image:: /auto_revalidation/laterality/images/thumb/sphx_glr__02_lrc_result_generation_no_exc_thumb.png
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  :ref:`sphx_glr_auto_revalidation_laterality__02_lrc_result_generation_no_exc.py`

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      <div class="sphx-glr-thumbnail-title">Revalidation of the laterality classification algorithms</div>
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Cadence
-------



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    <div class="sphx-glr-thumbcontainer" tooltip="The following provides an analysis and comparison of the cadence algorithms on the TVS dataset (lab and free-living). We look into the actual performance of the algorithms compared to the reference data. Note, that at the time of writing, comparison with the original Matlab results is not possible, as these algorithms were not run on the same version of the TVS dataset.">

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  .. image:: /auto_revalidation/cadence/images/thumb/sphx_glr__01_cadence_analysis_thumb.png
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  :ref:`sphx_glr_auto_revalidation_cadence__01_cadence_analysis.py`

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      <div class="sphx-glr-thumbnail-title">Performance of the cadence algorithms on the TVS dataset</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This script reproduces the validation results on TVS dataset for the cadence. It load results from the old matlab algorithms and runs the new algorithms on the same data. Performance metrics are calculated on a per-trial/per-recording basis and aggregated (mean for most metrics) over the whole dataset. The raw per second cadence and all performance metrics are saved to disk.">

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  .. image:: /auto_revalidation/cadence/images/thumb/sphx_glr__02_cadence_result_generation_no_exc_thumb.png
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  :ref:`sphx_glr_auto_revalidation_cadence__02_cadence_result_generation_no_exc.py`

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      <div class="sphx-glr-thumbnail-title">Revalidation of the cadence algorithms</div>
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Stride Length
-------------



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    <div class="sphx-glr-thumbcontainer" tooltip="The following provides an analysis and comparison of the stride length algorithms on the TVS dataset (lab and free-living). We look into the actual performance of the algorithms compared to the reference data and compare the results to the previous results generated by the matlab pipeline.">

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  .. image:: /auto_revalidation/stride_length/images/thumb/sphx_glr__01_sl_analysis_thumb.png
    :alt:

  :ref:`sphx_glr_auto_revalidation_stride_length__01_sl_analysis.py`

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      <div class="sphx-glr-thumbnail-title">Performance of the stride length algorithms on the TVS dataset</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This script reproduces the validation results on TVS dataset for the stride length. It load results from the old matlab algorithms and runs the new algorithms on the same data. Performance metrics are calculated on a per-trial/per-recording basis and aggregated (mean for most metrics) over the whole dataset. The raw per second stride length and all performance metrics are saved to disk.">

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  .. image:: /auto_revalidation/stride_length/images/thumb/sphx_glr__02_sl_result_generation_no_exc_thumb.png
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  :ref:`sphx_glr_auto_revalidation_stride_length__02_sl_result_generation_no_exc.py`

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      <div class="sphx-glr-thumbnail-title">Revalidation of the stride length algorithms</div>
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.. toctree::
   :hidden:
   :includehidden:


   /./auto_revalidation/full_pipeline/index.rst
   /./auto_revalidation/gait_sequences/index.rst
   /./auto_revalidation/initial_contacts/index.rst
   /./auto_revalidation/laterality/index.rst
   /./auto_revalidation/cadence/index.rst
   /./auto_revalidation/stride_length/index.rst


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      :download:`Download all examples in Python source code: auto_revalidation_python.zip </auto_revalidation/auto_revalidation_python.zip>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download all examples in Jupyter notebooks: auto_revalidation_jupyter.zip </auto_revalidation/auto_revalidation_jupyter.zip>`


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