:orphan:

.. _examples:

Examples
========

Examples that show how to work with mobgap.



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Data Loading and Handling
-------------------------



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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the loader functions to load example data.">

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  .. image:: /auto_examples/data/images/thumb/sphx_glr__01_loading_example_data_thumb.png
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  :ref:`sphx_glr_auto_examples_data__01_loading_example_data.py`

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      <div class="sphx-glr-thumbnail-title">Loading example data</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Often you want to test an algorithmic step in isolation or validate the output of an algorithm. In both cases, you need reference data - either as input or as a comparison.">

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  .. image:: /auto_examples/data/images/thumb/sphx_glr__02_working_with_ref_data_thumb.png
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  :ref:`sphx_glr_auto_examples_data__02_working_with_ref_data.py`

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      <div class="sphx-glr-thumbnail-title">Working with reference data</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Besides raw data, mobgap also has utilities to load pre-calculated DMO data in the format published by Mobilise-D. Specifically, this is the data of the TVS (=technical validation study) and the individual CVS (=clinical validation study) visits. For each CVS visit data is available as a single CSV file, which contains the DMO data for all walking bouts of all participants.">

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  .. image:: /auto_examples/data/images/thumb/sphx_glr__03_dmo_data_thumb.png
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  :ref:`sphx_glr_auto_examples_data__03_dmo_data.py`

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      <div class="sphx-glr-thumbnail-title">Loading Digital Mobility Outcome (DMO) data</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="As part of the Mobilise-D technical validation study an extensive dataset containing 115 participants (5 different indications + healthy adults) wearing a lower back sensor in the lab and during a 2.5 hour free-living period was collected. In the lab all trials were recorded with a synchronized motion capture system and the multi-modal wearable INDIP system as reference. During the 2.5 hour free-living period only the INDIP system was used as reference [1]_.">

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  .. image:: /auto_examples/data/images/thumb/sphx_glr__04_tvs_data_no_exc_thumb.png
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  :ref:`sphx_glr_auto_examples_data__04_tvs_data_no_exc.py`

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      <div class="sphx-glr-thumbnail-title">Mobilise-D TVS Dataset</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="While it is nice to have the example data and access to the TVS and some other datasets right through mobgap, let&#x27;s be honest: you will probably want to use your own data at some point. Let&#x27;s discuss how to approach this.">

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  .. image:: /auto_examples/data/images/thumb/sphx_glr__05_custom_datasets_thumb.png
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  :ref:`sphx_glr_auto_examples_data__05_custom_datasets.py`

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      <div class="sphx-glr-thumbnail-title">Custom Data and Datasets</div>
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Full Pipelines and Pipeline Helper
----------------------------------



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    <div class="sphx-glr-thumbcontainer" tooltip="As part of most pipelines, we need to iterate over the gait sequences to apply all further algorithms to them individually. This can be a bit cumbersome, as we need to iterate over the data and aggregate the results at the same time. Hence, we provide some helpers for that.">

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  .. image:: /auto_examples/pipeline/images/thumb/sphx_glr__01_gs_iterator_thumb.png
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  :ref:`sphx_glr_auto_examples_pipeline__01_gs_iterator.py`

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      <div class="sphx-glr-thumbnail-title">Gait Sequence Iterator</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to build a full gait analysis pipeline using the mobgap package. Note, that we provide pre-built pipelines for common use-cases. Checkout the examples for those, if you want to understand how to use them.">

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  .. image:: /auto_examples/pipeline/images/thumb/sphx_glr__02_step_by_step_mobilised_pipeline_thumb.png
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  :ref:`sphx_glr_auto_examples_pipeline__02_step_by_step_mobilised_pipeline.py`

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      <div class="sphx-glr-thumbnail-title">The Mobilise-D pipeline: Step-by-Step Breakdown</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to evaluate the performance of parameters on a walking bout (WB) level by comparing against a reference. On this level, we usually need to deal with the issue that the WB identified by the algorithm pipeline might not match the reference WBs. This makes comparing the parameters within them difficult. In general, two approaches can be taken here [1]_:">

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  .. image:: /auto_examples/pipeline/images/thumb/sphx_glr__03_dmo_evaluation_on_wb_level_thumb.png
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  :ref:`sphx_glr_auto_examples_pipeline__03_dmo_evaluation_on_wb_level.py`

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      <div class="sphx-glr-thumbnail-title">Evaluation of final walking bout level DMOs</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="As part of the Mobilise-D project two separate pipelines have been developed depending on the patient characteristics. The first pipeline MobilisedPipelineHealthy (P1 in [1]_) is designed for people that likely still have a somewhat normal gait pattern. In Mobilise-D, this pipeline is used for healthy controls and patients with &quot;COPD&quot; and &quot;CHF&quot;. The second pipeline MobilisedPipelineImpaired (P2 in [1]_) is designed for patients with likely significantly impaired gait patterns. In Mobilise-D, this pipeline is used for patients with &quot;PD&quot;, &quot;PFF&quot; and &quot;MS&quot;.">

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  .. image:: /auto_examples/pipeline/images/thumb/sphx_glr__03_preconfigured_mobilised_pipelines_thumb.png
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  :ref:`sphx_glr_auto_examples_pipeline__03_preconfigured_mobilised_pipelines.py`

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      <div class="sphx-glr-thumbnail-title">Preconfigured Mobilised Pipelines</div>
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Gait Sequence Detection
-----------------------



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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the GSD Iluz algorithm and some examples on how the results compare to the original matlab implementation.">

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  .. image:: /auto_examples/gait_sequences/images/thumb/sphx_glr__01_gsd_iluz_thumb.png
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  :ref:`sphx_glr_auto_examples_gait_sequences__01_gsd_iluz.py`

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      <div class="sphx-glr-thumbnail-title">GSD Iluz</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the two variants of the Gait Sequence Detection (GSD) algorithm by Paraschiv-Ionescu et al. The normal version called GsdIonescu uses a fixed signal activity threshold and a simplified filter chain and the adaptive version called GsdAdaptiveIonescu, uses an &quot;automatic&quot; threshold calculation and a more complex filter chain.">

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  .. image:: /auto_examples/gait_sequences/images/thumb/sphx_glr__02_gsd_ionescu_thumb.png
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  :ref:`sphx_glr_auto_examples_gait_sequences__02_gsd_ionescu.py`

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      <div class="sphx-glr-thumbnail-title">GSD Paraschiv-Ionescu</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to apply evaluation algorithms to GSD and thus how to rate the performance of a GSD algorithm.">

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  .. image:: /auto_examples/gait_sequences/images/thumb/sphx_glr__03_gsd_evaluation_thumb.png
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  :ref:`sphx_glr_auto_examples_gait_sequences__03_gsd_evaluation.py`

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      <div class="sphx-glr-thumbnail-title">GSD Evaluation</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="With that you could set up a custom evaluation pipeline to run and then score the output of a GSD algorithm multiple datapoints and then aggregate the results. To make this process easier, we set up opinionated evaluation challenges that can be used to quickly perform the same evaluation with multiple algorithms and datasets.">

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  .. image:: /auto_examples/gait_sequences/images/thumb/sphx_glr__04_gsd_evaluation_challenges_thumb.png
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  :ref:`sphx_glr_auto_examples_gait_sequences__04_gsd_evaluation_challenges.py`

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      <div class="sphx-glr-thumbnail-title">GSD Evaluation Challenges</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="GSD TVS Evaluation">

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  .. image:: /auto_examples/gait_sequences/images/thumb/sphx_glr__05_tvs_evaluation_no_exc_thumb.png
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  :ref:`sphx_glr_auto_examples_gait_sequences__05_tvs_evaluation_no_exc.py`

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      <div class="sphx-glr-thumbnail-title">GSD TVS Evaluation</div>
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Initial Contact Detection
-------------------------



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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the initial_contacts Ionescu algorithm and how its results compare to the original matlab implementation.">

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  .. image:: /auto_examples/initial_contacts/images/thumb/sphx_glr__01_icd_ionescu_thumb.png
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  :ref:`sphx_glr_auto_examples_initial_contacts__01_icd_ionescu.py`

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      <div class="sphx-glr-thumbnail-title">ICD Ionescu</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the improved Shin algorithm and some examples on how the results compare to the original matlab implementation.">

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  .. image:: /auto_examples/initial_contacts/images/thumb/sphx_glr__02_shin_algo_thumb.png
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  :ref:`sphx_glr_auto_examples_initial_contacts__02_shin_algo.py`

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      <div class="sphx-glr-thumbnail-title">Shin Algo</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the improved HKLee algorithm and some examples on how the results compare to the original matlab implementation.">

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  .. image:: /auto_examples/initial_contacts/images/thumb/sphx_glr__03_hklee_algo_thumb.png
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  :ref:`sphx_glr_auto_examples_initial_contacts__03_hklee_algo.py`

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      <div class="sphx-glr-thumbnail-title">HKLee algo</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to apply evaluation algorithms to ICD and thus how to rate the performance of an ICD algorithm.">

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  .. image:: /auto_examples/initial_contacts/images/thumb/sphx_glr__04_icd_evaluation_thumb.png
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  :ref:`sphx_glr_auto_examples_initial_contacts__04_icd_evaluation.py`

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      <div class="sphx-glr-thumbnail-title">ICD Evaluation</div>
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Left/Right Classification
-------------------------



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    <div class="sphx-glr-thumbcontainer" tooltip="The McCamley L/R classifier is a simple algorithm to detect the laterality of initial contacts based on the sign of the angular velocity signal. We use a modified version of the original McCamley algorithm, which includes a smoothing filter to reduce the influence of noise on the detection.">

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  .. image:: /auto_examples/laterality/images/thumb/sphx_glr__01_lrc_mccamley_thumb.png
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  :ref:`sphx_glr_auto_examples_laterality__01_lrc_mccamley.py`

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      <div class="sphx-glr-thumbnail-title">McCamley L/R Classifier</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="The Ullrich L/R classifier is a general approach of differentiating left from right foot contacts using signal features extracted from the gyroscopic data of a single IMU sensor placed on the lower back. The feature vectors at the timepoints of the pre-detected initial contacts are then used in a typical binary classification pipeline to predict the left/right label of each initial contact.">

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  .. image:: /auto_examples/laterality/images/thumb/sphx_glr__02_lrc_ullrich_thumb.png
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  :ref:`sphx_glr_auto_examples_laterality__02_lrc_ullrich.py`

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      <div class="sphx-glr-thumbnail-title">Ullrich L/R Classifier</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="The BenMansour L/R classifier is a simple algorithm to detect the laterality of initial contacts based on the sign of the derivative of the mediolateral acceleration signal.">

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  .. image:: /auto_examples/laterality/images/thumb/sphx_glr__03_lrc_benmansour_thumb.png
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  :ref:`sphx_glr_auto_examples_laterality__03_lrc_benmansour.py`

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      <div class="sphx-glr-thumbnail-title">BenMansour L/R Classifier</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example demonstrates how to evaluate an LRC algorithm. As left-right classification, is a balanced binary classification problem, we can apply simple metrics like accuracy to evaluate the performance of the algorithm.">

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  .. image:: /auto_examples/laterality/images/thumb/sphx_glr__99_lrc_evaluation_thumb.png
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  :ref:`sphx_glr_auto_examples_laterality__99_lrc_evaluation.py`

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      <div class="sphx-glr-thumbnail-title">LRC Evaluation</div>
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Cadence Estimation
------------------



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    <div class="sphx-glr-thumbcontainer" tooltip="The most obvious way to calculate cadence is to use the detected initial contacts. However, initial contact (IC) detection from a single lower back sensor is never perfect. If we naively calculate cadence via a step time derived by &quot;diffing&quot; the initial contacts, missing ICs or even actual breaks in the gait sequence will lead to wrong cadence values (likely underestimating the cadence). We need to be robust to these types of errors.">

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  .. image:: /auto_examples/cadence/images/thumb/sphx_glr__01_cad_from_ic_thumb.png
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  :ref:`sphx_glr_auto_examples_cadence__01_cad_from_ic.py`

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      <div class="sphx-glr-thumbnail-title">Cadence from Initial Contacts</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to apply evaluation algorithms to cadence results and thus how to rate the performance of a cadence calculation algorithm.">

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  .. image:: /auto_examples/cadence/images/thumb/sphx_glr__02_cad_evaluation_thumb.png
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  :ref:`sphx_glr_auto_examples_cadence__02_cad_evaluation.py`

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      <div class="sphx-glr-thumbnail-title">Cadence Evaluation</div>
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Stride Length Calculation
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the Zijlstra algorithm for calculating stride length and how its results compare to the original matlab implementation.">

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  .. image:: /auto_examples/stride_length/images/thumb/sphx_glr__01_sl_zijlstra_thumb.png
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  :ref:`sphx_glr_auto_examples_stride_length__01_sl_zijlstra.py`

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      <div class="sphx-glr-thumbnail-title">SL Zijlstra</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to apply evaluation algorithms to stride length results and thus how to rate the performance of a stride length calculation algorithm.">

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  .. image:: /auto_examples/stride_length/images/thumb/sphx_glr__02_sl_evaluation_thumb.png
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  :ref:`sphx_glr_auto_examples_stride_length__02_sl_evaluation.py`

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      <div class="sphx-glr-thumbnail-title">Stride Length Evaluation</div>
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Detecting Turns
---------------



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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the ElGohary turning algorithm. It uses the angular velocity around the SI axis of a lower back IMU to detect turns.">

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  .. image:: /auto_examples/turning/images/thumb/sphx_glr__01_td_elgohary_thumb.png
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  :ref:`sphx_glr_auto_examples_turning__01_td_elgohary.py`

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      <div class="sphx-glr-thumbnail-title">ElGohary Turning Algo</div>
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Creating final Walking Bouts
----------------------------



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    <div class="sphx-glr-thumbcontainer" tooltip="An important part of the Mobilise-D pipeline is that we output WBs based on a fixed set of definitions. These definitions are build based on a consensus process within the Mobilise-D consortium [1]_ and additional normative data from the literature to define realistic ranges for stride and WB level parameters.">

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  .. image:: /auto_examples/wba/images/thumb/sphx_glr__01_assembling_wbs_thumb.png
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  :ref:`sphx_glr_auto_examples_wba__01_assembling_wbs.py`

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      <div class="sphx-glr-thumbnail-title">Assembling WBs</div>
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DMO Aggregation
---------------



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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the .MobilisedAggregator class to aggregate DMOs over multiple walking bouts.">

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  .. image:: /auto_examples/aggregation/images/thumb/sphx_glr__01_mobilised_aggregator_thumb.png
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  :ref:`sphx_glr_auto_examples_aggregation__01_mobilised_aggregator.py`

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      <div class="sphx-glr-thumbnail-title">Mobilised Aggregator</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to use the functions apply_thresholds to check if a DMO falls within the thresholds for a specific cohort and height.">

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  .. image:: /auto_examples/aggregation/images/thumb/sphx_glr__02_threshold_check_thumb.png
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  :ref:`sphx_glr_auto_examples_aggregation__02_threshold_check.py`

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      <div class="sphx-glr-thumbnail-title">Threshold Checker</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how the raw CVS per walking bout results are combined with the weartime reports and aggregated to a daily and weekly level.">

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  .. image:: /auto_examples/aggregation/images/thumb/sphx_glr__99_cvs_agg_pipeline_no_exc_thumb.png
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  :ref:`sphx_glr_auto_examples_aggregation__99_cvs_agg_pipeline_no_exc.py`

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      <div class="sphx-glr-thumbnail-title">CVS Official Aggregation Script</div>
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Data Transformations and Filtering
----------------------------------



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    <div class="sphx-glr-thumbcontainer" tooltip="Besides explicit gait specific algorithms, we also provide a set of generic data transforms, which can be used as part of an algorithm or to pre-process the data before applying a gait specific algorithm.">

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  .. image:: /auto_examples/data_transform/images/thumb/sphx_glr__01_generic_data_transforms_thumb.png
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  :ref:`sphx_glr_auto_examples_data_transform__01_generic_data_transforms.py`

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      <div class="sphx-glr-thumbnail-title">Generic Data Transforms</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="One of the most common data transformations is filtering. In mobgap various types of filters are used to clean data and enhance signal features. Important filters are implemented using the BaseFilter class, which supports a consistent interface for filtering data. See the generic data transforms example &lt;generic_data_transforms&gt; for more information.">

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  .. image:: /auto_examples/data_transform/images/thumb/sphx_glr__02_filter_thumb.png
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  :ref:`sphx_glr_auto_examples_data_transform__02_filter.py`

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      <div class="sphx-glr-thumbnail-title">General Filter Introduction</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Sometimes you may want to resample your data to a different sampling rate. We provide a utility class to do this. It wraps scipy.signal.resample and provides a standardized transform interface, so that it can be chained and used with other transforms in mobgap.">

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  .. image:: /auto_examples/data_transform/images/thumb/sphx_glr__03_resample_thumb.png
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  :ref:`sphx_glr_auto_examples_data_transform__03_resample.py`

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      <div class="sphx-glr-thumbnail-title">Resampling data</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Continuous wavelet transform (CWT) is a time-frequency analysis method that can provide frequency information localized in both time and frequency. For this, wavelets of different scales, where each scale corresponds to a different frequency, are used to analyze the signal. When performing the CWT with just a single scale, the CWT becomes equivalent to a narrow bandpass filter, enhancing the frequency content of the signal around the scale of the wavelet.">

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  .. image:: /auto_examples/data_transform/images/thumb/sphx_glr__04_cwt_filter_thumb.png
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  :ref:`sphx_glr_auto_examples_data_transform__04_cwt_filter.py`

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      <div class="sphx-glr-thumbnail-title">Continuous Wavelet Transform (CWT) - Filter</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Gaussian Smoothing">

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  .. image:: /auto_examples/data_transform/images/thumb/sphx_glr__05_gaussian_smoothing_thumb.png
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  :ref:`sphx_glr_auto_examples_data_transform__05_gaussian_smoothing.py`

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      <div class="sphx-glr-thumbnail-title">Gaussian Smoothing</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="We provide a class based implementation of the Savitzky-Golay filter in the SavgolFilter class. Compared to the version provided with scipy (scipy.signal.savgol_filter), our implementation allows to specify all parameters independently of the sampling rate of the data. This should allow to apply the same filter to different datasets, while maintaining the same filter characteristics.">

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  .. image:: /auto_examples/data_transform/images/thumb/sphx_glr__06_savgol_filter_thumb.png
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  :ref:`sphx_glr_auto_examples_data_transform__06_savgol_filter.py`

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      <div class="sphx-glr-thumbnail-title">Savitzky-Golay Filter Example</div>
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Development Guides
------------------

Some recipies that should help during development of mobgap and might also be helpful, if you want to extend mobgap
using new algorithms.


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    <div class="sphx-glr-thumbnails">

.. thumbnail-parent-div-open

.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="All mobgap algorithms are implemented as tpcp classes (see tpcp.Algorithm). Hence, all there configuration is stored in the parameters of the class.">

.. only:: html

  .. image:: /auto_examples/dev_guides/images/thumb/sphx_glr__01_pretrained_models_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_dev_guides__01_pretrained_models.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Pretrained models and predefined Parameters</div>
    </div>


.. thumbnail-parent-div-close

.. raw:: html

    </div>


.. toctree::
   :hidden:
   :includehidden:


   /./auto_examples/data/index.rst
   /./auto_examples/pipeline/index.rst
   /./auto_examples/gait_sequences/index.rst
   /./auto_examples/initial_contacts/index.rst
   /./auto_examples/laterality/index.rst
   /./auto_examples/cadence/index.rst
   /./auto_examples/stride_length/index.rst
   /./auto_examples/turning/index.rst
   /./auto_examples/wba/index.rst
   /./auto_examples/aggregation/index.rst
   /./auto_examples/data_transform/index.rst
   /./auto_examples/dev_guides/index.rst


.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-gallery

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download all examples in Python source code: auto_examples_python.zip </auto_examples/auto_examples_python.zip>`

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

      :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip </auto_examples/auto_examples_jupyter.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_