.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/turning/_01_td_elgohary.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_turning__01_td_elgohary.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_turning__01_td_elgohary.py:


ElGohary Turning Algo
=====================

.. warning:: The ElGohary turning algorithm could not be properly validated as part of the Mobilise-D project, as no
    clear consensus exists what a "turn" is.
    This makes it difficult to design a suitable reference system, which nature of calculating turns does not bias the
    results fundamentally.

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.

.. GENERATED FROM PYTHON SOURCE LINES 15-29

Loading data
------------
.. note :: More infos about data loading can be found in the :ref:`data loading example <data_loading_example>`.

We load example data from the lab dataset together with the Stereophoto reference system.
We will use the Stereophoto output for turns ("td") as ground truth, as it is the most accurate reference system
available in the dataset.
Still, the turn detection might not be fully reliable.
Note, that the INDIP system, also uses just a single lower back IMU to calculate the turns.
Hence, it can not really be considered a reference system, in this context.

The turn algorithm requires the data to be in the body frame (see the User Guide on this topic).
We use the ``to_body_frame`` function to transform the data, as we know that the sensor was well aligned, with the
expected sensor frame conventions.

.. GENERATED FROM PYTHON SOURCE LINES 29-44

.. code-block:: default

    from mobgap.data import LabExampleDataset
    from mobgap.utils.conversions import to_body_frame

    example_data = LabExampleDataset(
        reference_system="Stereophoto", reference_para_level="wb"
    )

    single_test = example_data.get_subset(
        cohort="HA", participant_id="001", test="Test11", trial="Trial1"
    )
    imu_data = to_body_frame(single_test.data_ss)
    reference_wbs = single_test.reference_parameters_.wb_list

    sampling_rate_hz = single_test.sampling_rate_hz








.. GENERATED FROM PYTHON SOURCE LINES 45-47

Note, that the reference turns don't use the 45 deg lower cutoff for turns by default.
Hence, we apply this here for consistency.

.. GENERATED FROM PYTHON SOURCE LINES 47-51

.. code-block:: default

    ref_turns = single_test.reference_parameters_.turn_parameters.query(
        "angle_deg.abs() >= 45"
    )








.. GENERATED FROM PYTHON SOURCE LINES 52-61

Applying the algorithm
----------------------
In a typical pipeline, we first identify the gait sequences and then apply the turning detection algorithm to each
gait sequence individually.
However, the turning algorithm can also be applied to the whole recording at once.
Though, it might produce false positives, in "non-walking" segments.

Below we show both approaches, starting with the whole recording.
This allows us to visualize how the algorithm works.

.. GENERATED FROM PYTHON SOURCE LINES 61-69

.. code-block:: default

    from mobgap.turning import TdElGohary

    turning_detector = TdElGohary()

    turning_detector.detect(imu_data, sampling_rate_hz=sampling_rate_hz)
    turn_list = turning_detector.turn_list_
    turn_list






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
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          <td>8136</td>
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          <th>8</th>
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        </tr>
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          <th>9</th>
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          <td>12686</td>
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          <td>80.281528</td>
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          <th>10</th>
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          <td>13242</td>
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          <td>-174.197791</td>
          <td>right</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 70-73

We can also extract additional debug information from the algorithm.
The yaw-angle gives us the estimated orientation of the lower back in the axis of the turning.
The ``raw_turn_list_`` gives us the raw detected turns, before filtering them based on duration and angle.

.. GENERATED FROM PYTHON SOURCE LINES 73-78

.. code-block:: default

    yaw_angle = turning_detector.yaw_angle_
    raw_turns = turning_detector.raw_turn_list_
    raw_turns







.. 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;
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          <th>direction</th>
          <th>center</th>
        </tr>
        <tr>
          <th>turn_id</th>
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          <th>27</th>
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          <th>30</th>
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        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 79-89

To better understand, how things work, we can plot all the results together.

We can see that after filtering the signal, the algorithm identifies peaks in the signal that are higher in absolute
values than the ``min_peak_angle_velocity_dps`` (dotted lines).
Around these "turn-centers" the turn is defined as the region until the signal drops below the
``lower_threshold_velocity_dps`` (dashed lines).

We then look at the duration and the angle of the detected turns and filter them based on the provided thresholds.
We can see that at the end, only a small number of turns remain.
Most of the raw turns are filtered out by the ``allowed_turn_angle_deg`` threshold, which is set to 45 degrees.

.. GENERATED FROM PYTHON SOURCE LINES 89-175

.. code-block:: default

    import matplotlib.pyplot as plt


    def plot_turns(algo_with_results: TdElGohary):
        fig, axs = plt.subplots(3, 1, figsize=(10, 6), sharex=True)
        axs[0].set_ylabel("gyr_is [dps]")
        if algo_with_results.global_frame_data_ is None:
            data = algo_with_results.data
            axs[0].set_title("Raw gyr_is data")
            axis = "gyr_is"
        else:
            data = algo_with_results.global_frame_data_
            axs[0].set_title("Raw gyr_is data (global frame)")
            axis = "gyr_gis"
        data.reset_index(drop=True).plot(y=axis, ax=axs[0])

        axs[1].set_title("Filtered IMU signal with raw turns and thresholds.")
        axs[1].set_ylabel("filtered gyr_is [dps]")
        filtered_data = (
            algo_with_results.smoothing_filter.clone()
            .filter(data[axis], sampling_rate_hz=sampling_rate_hz)
            .filtered_data_
        )
        filtered_data.reset_index(drop=True).plot(ax=axs[1])

        raw_turn_list = algo_with_results.raw_turn_list_
        # Plot turn centeres
        axs[1].plot(
            raw_turn_list["center"],
            filtered_data.iloc[raw_turn_list["center"]],
            "o",
        )
        # Plot start and end of turns as regions
        for i, row in raw_turn_list.iterrows():
            axs[1].axvspan(
                row["start"],
                row["end"],
                alpha=0.5,
                color="gray" if row["direction"] == "left" else "blue",
            )

        # Draw dashed line at +/- velocity_dps
        axs[1].axhline(
            algo_with_results.lower_threshold_velocity_dps,
            color="green",
            linestyle="--",
            label="velocity_dps",
        )
        axs[1].axhline(
            -algo_with_results.lower_threshold_velocity_dps,
            color="gray",
            linestyle="--",
        )

        # Draw dottet line at +/- height
        axs[1].axhline(
            algo_with_results.min_peak_angle_velocity_dps,
            color="green",
            linestyle=":",
            label="height",
        )
        axs[1].axhline(
            -algo_with_results.min_peak_angle_velocity_dps,
            color="gray",
            linestyle=":",
        )

        axs[2].set_title("Yaw angle with final turns")
        axs[2].set_ylabel("Yaw angle [deg]")
        axs[2].set_xlabel("samples [#]")
        algo_with_results.yaw_angle_.reset_index(drop=True).plot(ax=axs[2])

        # Plot start and end of turns as regions
        for i, row in algo_with_results.turn_list_.iterrows():
            axs[2].axvspan(
                row["start"],
                row["end"],
                alpha=0.5,
                color="gray" if row["direction"] == "left" else "blue",
            )

        fig.show()


    plot_turns(turning_detector)




.. image-sg:: /auto_examples/turning/images/sphx_glr__01_td_elgohary_001.png
   :alt: Raw gyr_is data, Filtered IMU signal with raw turns and thresholds., Yaw angle with final turns
   :srcset: /auto_examples/turning/images/sphx_glr__01_td_elgohary_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 176-179

Now that we understand how the algorithm works, we apply it in the context of a typical pipeline using the
``GsIterator`` in combination with the reference WBs.
This allows us to compare the results to the reference system.

.. GENERATED FROM PYTHON SOURCE LINES 179-192

.. code-block:: default

    from mobgap.pipeline import GsIterator

    iterator = GsIterator()

    for (gs, data), result in iterator.iterate(imu_data, reference_wbs):
        td = turning_detector.clone().detect(
            data, sampling_rate_hz=sampling_rate_hz
        )
        result.turn_list = td.turn_list_

    turns = iterator.results_.turn_list
    turns






.. 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;
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          <th>duration_s</th>
          <th>angle_deg</th>
          <th>direction</th>
        </tr>
        <tr>
          <th>wb_id</th>
          <th>turn_id</th>
          <th></th>
          <th></th>
          <th></th>
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          <th></th>
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          <th>1</th>
          <td>3937</td>
          <td>4160</td>
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          <td>-127.870142</td>
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          <th>2</th>
          <td>4167</td>
          <td>4544</td>
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          <th>2</th>
          <th>0</th>
          <td>7777</td>
          <td>8136</td>
          <td>3.59</td>
          <td>-149.706194</td>
          <td>right</td>
        </tr>
        <tr>
          <th>3</th>
          <th>0</th>
          <td>9381</td>
          <td>9628</td>
          <td>2.47</td>
          <td>-139.641274</td>
          <td>right</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 193-194

We can compare this to the reference turns.

.. GENERATED FROM PYTHON SOURCE LINES 194-196

.. code-block:: default

    ref_turns






.. 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;
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          <td>right</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 197-199

The results look reasonable, but we can see that in gs 3 and 5 the algorithm was not able to detect the smaller turns
with lower turning angles.

.. GENERATED FROM PYTHON SOURCE LINES 201-217

Working in the global coordinate system
---------------------------------------
The original ElGohary paper uses on-board sensor fusion to track the orientation of the sensor.
This information is used to transform all sensor data into the global coordinate system.
This should make the identification of the inferior-superior (IS) axis more robust.

We did not use this approach within Mobilise-D, to avoid introducing an additional source of error through a sensor
fusion algorithm.
However, the result of the algorithms can be significantly influenced by that decision, in particular if participants
have a lot of body sway of walk bend forward.
Below we show two approached on how you could use the algorithm with a global frame estimation.

1. Using the internal global frame estimation
---------------------------------------------
For this we pass an instance of the MadgwickAHRS algorithm to estimate the global orientation of the sensor to the
algorithm.

.. GENERATED FROM PYTHON SOURCE LINES 217-221

.. code-block:: default

    from mobgap.orientation_estimation import MadgwickAHRS

    orientation_estimator = MadgwickAHRS()








.. GENERATED FROM PYTHON SOURCE LINES 222-224

Now we can apply the algorithm again.
We are going to apply it to the entire recording to show the step-by-step process.

.. GENERATED FROM PYTHON SOURCE LINES 224-231

.. code-block:: default

    turning_detector_global = TdElGohary(
        orientation_estimation=orientation_estimator
    )

    turning_detector_global.detect(imu_data, sampling_rate_hz=sampling_rate_hz)
    plot_turns(turning_detector_global)




.. image-sg:: /auto_examples/turning/images/sphx_glr__01_td_elgohary_002.png
   :alt: Raw gyr_is data (global frame), Filtered IMU signal with raw turns and thresholds., Yaw angle with final turns
   :srcset: /auto_examples/turning/images/sphx_glr__01_td_elgohary_002.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 232-237

Based on the plotted results, we can see that the algorithm identifies basically the same turns as before.

The same can be observed, when applying the algorithm per GS.
The turns are almost identical as before, indicating, that the global frame transformation was not really required
for this specific dataset.

.. GENERATED FROM PYTHON SOURCE LINES 237-248

.. code-block:: default

    iterator = GsIterator()

    for (gs, data), result in iterator.iterate(imu_data, reference_wbs):
        td = turning_detector.clone().detect(
            data, sampling_rate_hz=sampling_rate_hz
        )
        result.turn_list = td.turn_list_

    turns_global_per_gs = iterator.results_.turn_list
    turns_global_per_gs






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
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          <th>angle_deg</th>
          <th>direction</th>
        </tr>
        <tr>
          <th>wb_id</th>
          <th>turn_id</th>
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          <th>3</th>
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          <td>right</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 249-260

Alternatively, we could also use the global frame estimation to transform the data into the global frame before
applying the algorithm.

2. Transforming the data into the global frame
----------------------------------------------
For this, we first estimate the global frame for the entire recording and then put the rotated data into the Gait
Sequence Iterator.
Theoretically, this should yield slightly better results, as the Madgwick algorithm always needs a certain amount of
time to converge to the correct orientation.
By running it once over the entire data, these convergence period should only happen once at the beginning of the
entire recording and not affect the start of each gait sequence.

.. GENERATED FROM PYTHON SOURCE LINES 260-276

.. code-block:: default

    ori_estimator = orientation_estimator.clone().estimate(
        imu_data, sampling_rate_hz=sampling_rate_hz
    )
    imu_data_global = ori_estimator.rotated_data_

    iterator = GsIterator()

    for (gs, data), result in iterator.iterate(imu_data_global, reference_wbs):
        td = turning_detector.clone().detect(
            data, sampling_rate_hz=sampling_rate_hz
        )
        result.turn_list = td.turn_list_

    turns_global = iterator.results_.turn_list
    turns_global






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
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    <br />

.. GENERATED FROM PYTHON SOURCE LINES 277-279

.. code-block:: default

    ref_turns






.. raw:: html

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    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 280-282

Which of the two approaches is better, depends on multiple factors.
If the global frame should be used generally, further investigation is needed.


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

   **Total running time of the script:** (0 minutes 3.557 seconds)

**Estimated memory usage:**  13 MB


.. _sphx_glr_download_auto_examples_turning__01_td_elgohary.py:

.. only:: html

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




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

      :download:`Download Python source code: _01_td_elgohary.py <_01_td_elgohary.py>`

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

      :download:`Download Jupyter notebook: _01_td_elgohary.ipynb <_01_td_elgohary.ipynb>`


.. only:: html

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

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