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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_trajectory_reconstruction_with_magnetometer.py:


Region Level Trajectory with Magnetometer
=========================================

While most algorithms in gaitmap are designed to only work with accelerometer and gyroscope data, we have implemented
the magnetometer version of the Madgwick algorithm (`use_magnetometer=True` in `MadgwickAHRS`).
This can be used as standalone orientation method or as part of the `MadgwickRtsKalman` trajectory method.

Below, we show an example recording (provided by the Fraunhofer IIS) of an X-Sense sensor attached at the foot.
The person walked L-shaped trajectory repeatedly.

In this example we will compare the results of the Kalman Filter with and without the use of the magnetometer data.

To simplify the example, we have already pre-aligned the data to the gaitmap coordinate system and ensured that the
data is in the correct format.

.. GENERATED FROM PYTHON SOURCE LINES 21-23

Getting input data
------------------

.. GENERATED FROM PYTHON SOURCE LINES 23-31

.. code-block:: default

    import matplotlib.pyplot as plt

    from gaitmap.example_data import get_magnetometer_l_path_data
    from gaitmap.trajectory_reconstruction import MadgwickRtsKalman, RtsKalman

    imu_data = get_magnetometer_l_path_data().iloc[:100000]
    sampling_frequency_hz = 400








.. GENERATED FROM PYTHON SOURCE LINES 32-34

Selecting and Configuring Algorithms
------------------------------------

.. GENERATED FROM PYTHON SOURCE LINES 34-38

.. code-block:: default

    mad_with_mag = MadgwickRtsKalman(use_magnetometer=True, madgwick_beta=0.105, velocity_error_variance=0.01)
    mad_without_mag = MadgwickRtsKalman(use_magnetometer=False)
    simple = RtsKalman()








.. GENERATED FROM PYTHON SOURCE LINES 39-41

Calculate and inspect results
-----------------------------

.. GENERATED FROM PYTHON SOURCE LINES 41-49

.. code-block:: default

    mad_with_mag.estimate(data=imu_data, sampling_rate_hz=sampling_frequency_hz)
    mad_without_mag.estimate(data=imu_data, sampling_rate_hz=sampling_frequency_hz)
    simple.estimate(data=imu_data, sampling_rate_hz=sampling_frequency_hz)

    with_mag_traj = mad_with_mag.position_
    without_mag_traj = mad_without_mag.position_
    simple_traj = simple.position_








.. GENERATED FROM PYTHON SOURCE LINES 50-55

Plotting
--------
We are plotting the x-y plane to see if the magnetometer has a relevant influence on the trajectory.
In particular, we assume that the heading of the foot is more accurate when using the magnetometer and does
not drift as much as without it.

.. GENERATED FROM PYTHON SOURCE LINES 55-67

.. code-block:: default

    plt.figure(figsize=(12, 6))
    plt.plot(simple_traj["pos_x"], simple_traj["pos_y"], label="Simple Kalman")
    plt.plot(without_mag_traj["pos_x"], without_mag_traj["pos_y"], label="Madgwick Kalman without Magnetometer")
    plt.plot(with_mag_traj["pos_x"], with_mag_traj["pos_y"], label="Madgwick Kalman with Magnetometer")

    plt.xlabel("x [m]")
    plt.ylabel("y [m]")
    plt.axis("equal")

    plt.legend()
    plt.show()




.. image-sg:: /auto_examples/trajectory_reconstruction/images/sphx_glr_with_magnetometer_001.png
   :alt: with magnetometer
   :srcset: /auto_examples/trajectory_reconstruction/images/sphx_glr_with_magnetometer_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 68-80

Conclusion
----------
We can see that with the magnetometer the heading is somewhat stable.
While there is still a positional drift between the repetitions, all the walking path align with the same axis.
For the other two methods, we see less positional drift, but the heading drifts significantly.

The positional drift around x=0 is likely caused by large metallic objects in the room, which distort the magnetic
field.
Both other methods don't show comparable artifacts in that region.

In all three cases, tuning the parameters of the Kalman filter could help to further reduce drift.
However, without the magnetometer, there is little chance to get a stable heading.


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

   **Total running time of the script:** ( 1 minutes  8.600 seconds)

**Estimated memory usage:**  674 MB


.. _sphx_glr_download_auto_examples_trajectory_reconstruction_with_magnetometer.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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