.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/stride_segmentation/barth_dtw_stride_segmentation.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_auto_examples_stride_segmentation_barth_dtw_stride_segmentation.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_stride_segmentation_barth_dtw_stride_segmentation.py:


.. _example_barth_stride_segmentation:

BarthDtw stride segmentation
============================

This example illustrates how subsequent DTW implemented by the :class:`~gaitmap.stride_segmentation.BarthDtw` can be
used to detect strides in a continuous signal of an IMU signal.
The used implementation is based on the work of Barth et al [1]_ and adds a set of postprocessing methods that aim to
reduce the chance of false positives.

.. [1] Barth, J., Oberndorfer, C., Kugler, P., Schuldhaus, D., Winkler, J., Klucken, J., & Eskofier, B. (2013).
   Subsequence dynamic time warping as a method for robust step segmentation using gyroscope signals of daily life
   activities. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology
   Society, EMBS, 6744–6747. https://doi.org/10.1109/EMBC.2013.6611104

.. GENERATED FROM PYTHON SOURCE LINES 17-23

.. code-block:: default


    import matplotlib.pyplot as plt
    import numpy

    numpy.random.seed(0)








.. GENERATED FROM PYTHON SOURCE LINES 24-30

Getting some example data
--------------------------

For this we take some example data that contains the regular walking movement during a 2x20m walk test of a healthy
subject. The IMU signals are already rotated so that they align with the gaitmap SF coordinate system.
The data contains information from two sensors - one from the right and one from the left foot.

.. GENERATED FROM PYTHON SOURCE LINES 30-36

.. code-block:: default

    from gaitmap.example_data import get_healthy_example_imu_data

    data = get_healthy_example_imu_data()
    sampling_rate_hz = 204.8
    data.sort_index(axis=1).head(1)






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead tr th {
            text-align: left;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr>
          <th>sensor</th>
          <th colspan="6" halign="left">left_sensor</th>
          <th colspan="6" halign="left">right_sensor</th>
        </tr>
        <tr>
          <th>axis</th>
          <th>acc_x</th>
          <th>acc_y</th>
          <th>acc_z</th>
          <th>gyr_x</th>
          <th>gyr_y</th>
          <th>gyr_z</th>
          <th>acc_x</th>
          <th>acc_y</th>
          <th>acc_z</th>
          <th>gyr_x</th>
          <th>gyr_y</th>
          <th>gyr_z</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0.0</th>
          <td>0.880811</td>
          <td>2.762208</td>
          <td>9.40865</td>
          <td>-0.112402</td>
          <td>-0.032157</td>
          <td>-0.062261</td>
          <td>0.311553</td>
          <td>-2.398646</td>
          <td>9.513275</td>
          <td>-0.323037</td>
          <td>0.084604</td>
          <td>-0.025288</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 37-43

Selecting a template
--------------------
This library ships with the template that was originally used by Barth et al.
It is generated based on manually segmented strides from healthy participants and PD patients.
This template is used by default by :class:`~gaitmap.stride_segmentation.BarthDtw`, but we will load it manually in
this example.

.. GENERATED FROM PYTHON SOURCE LINES 43-51

.. code-block:: default

    from gaitmap.stride_segmentation import BarthOriginalTemplate

    template = BarthOriginalTemplate()
    template.get_data().plot()
    plt.xlabel("Time [#]")
    plt.ylabel("gyro [deg/s]")
    plt.show()




.. image-sg:: /auto_examples/stride_segmentation/images/sphx_glr_barth_dtw_stride_segmentation_001.png
   :alt: barth dtw stride segmentation
   :srcset: /auto_examples/stride_segmentation/images/sphx_glr_barth_dtw_stride_segmentation_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 52-58

Preparing the data
------------------
The template only makes use of the gyro information.
Further, if you use this template in the DTW, your data is expected to be in the gaitmap BF to be able to use the
same template for the left and the right foot.
Therefore, we need to transform the dataset into the body frame.

.. GENERATED FROM PYTHON SOURCE LINES 58-63

.. code-block:: default

    from gaitmap.utils.coordinate_conversion import convert_to_fbf

    # We use the `..._like` parameters to identify the data of the left and the right foot based on the name of the sensor.
    bf_data = convert_to_fbf(data, left_like="left_", right_like="right_")








.. GENERATED FROM PYTHON SOURCE LINES 64-70

Applying the DTW
----------------
First we need to initialize the DTW.
In most cases it is sufficient to keep all parameters at default.
However, if you experience any issues you should start modifying the parameters, starting by `max_cost`,
as it has the highest influence on the result.

.. GENERATED FROM PYTHON SOURCE LINES 70-76

.. code-block:: default

    from gaitmap.stride_segmentation import BarthDtw

    dtw = BarthDtw(template=template)
    # Apply the dtw to the data
    dtw = dtw.segment(data=bf_data, sampling_rate_hz=sampling_rate_hz)








.. GENERATED FROM PYTHON SOURCE LINES 77-81

Inspecting the results
----------------------
The main output is the `stride_list_`, which contains the start and the end of all identified strides.
As we passed a dataset with two sensors, the output will be a dictionary.

.. GENERATED FROM PYTHON SOURCE LINES 81-85

.. code-block:: default

    stride_list_left = dtw.stride_list_["left_sensor"]
    print(f"{len(stride_list_left)} strides were detected.")
    stride_list_left.head()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    28 strides were detected.


.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>start</th>
          <th>end</th>
        </tr>
        <tr>
          <th>s_id</th>
          <th></th>
          <th></th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>0</th>
          <td>364</td>
          <td>584</td>
        </tr>
        <tr>
          <th>1</th>
          <td>584</td>
          <td>802</td>
        </tr>
        <tr>
          <th>2</th>
          <td>802</td>
          <td>1023</td>
        </tr>
        <tr>
          <th>3</th>
          <td>1023</td>
          <td>1242</td>
        </tr>
        <tr>
          <th>4</th>
          <td>1242</td>
          <td>1458</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 86-97

To get a better understanding of the results, we can plot additional information about the results.
The top row shows the `gyr_ml` axis with the segmented strides plotted on top.
They are postprocessed to snap to the closed data minimum.
In the second row the cost function of the DTW is plotted.
Each minimum marks a potential end of a stride.
The black dotted line indicates the used `max_cost` threshold to search for stride candidates.
The drawn boxes show the raw result of the DTW without the snap-to-min postprocessing.
The third row shows the entire accumulated cost matrix and the path each stride takes through the cost matrix to
achieve minimal cost.

Only the first couple of strides of the left foot are shown.

.. GENERATED FROM PYTHON SOURCE LINES 97-119

.. code-block:: default


    sensor = "left_sensor"
    fig, axs = plt.subplots(nrows=3, sharex=True, figsize=(10, 5))
    dtw.data[sensor]["gyr_ml"].reset_index(drop=True).plot(ax=axs[0])
    axs[0].set_ylabel("gyro [deg/s]")
    axs[1].plot(dtw.cost_function_[sensor])
    axs[1].set_ylabel("dtw cost [a.u.]")
    axs[1].axhline(dtw.max_cost, color="k", linestyle="--")
    axs[2].imshow(dtw.acc_cost_mat_[sensor], aspect="auto")
    axs[2].set_ylabel("template position [#]")
    for p in dtw.paths_[sensor]:
        axs[2].plot(p.T[1], p.T[0])
    for s in dtw.matches_start_end_original_[sensor]:
        axs[1].axvspan(*s, alpha=0.3, color="g")
    for _, s in dtw.stride_list_[sensor][["start", "end"]].iterrows():
        axs[0].axvspan(*s, alpha=0.3, color="g")

    axs[0].set_xlim(300, 2000)
    axs[0].set_xlabel("time [#]")
    fig.tight_layout()
    fig.show()




.. image-sg:: /auto_examples/stride_segmentation/images/sphx_glr_barth_dtw_stride_segmentation_002.png
   :alt: barth dtw stride segmentation
   :srcset: /auto_examples/stride_segmentation/images/sphx_glr_barth_dtw_stride_segmentation_002.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  2.428 seconds)

**Estimated memory usage:**  39 MB


.. _sphx_glr_download_auto_examples_stride_segmentation_barth_dtw_stride_segmentation.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


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

      :download:`Download Python source code: barth_dtw_stride_segmentation.py <barth_dtw_stride_segmentation.py>`

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

      :download:`Download Jupyter notebook: barth_dtw_stride_segmentation.ipynb <barth_dtw_stride_segmentation.ipynb>`


.. only:: html

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

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