.. DO NOT EDIT.
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.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "acoustic/acoustic_hgr_tutorial.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

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

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

.. _sphx_glr_acoustic_acoustic_hgr_tutorial.py:


Acoustic Hand Gesture Recognition Tutorial
==============================================================

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!pip install pysensing

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In this tutorial, we will be implementing codes for acoustic Hand Gesture Recognition


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.. code-block:: Python

    import torch
    import torchaudio
    import matplotlib.pyplot as plt
    from torch.utils.data import DataLoader
    import os
    import numpy as np
    import torch.nn as nn
    import tqdm
    import sys
    from pysensing.acoustic.datasets.hgr import AMG
    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')







.. GENERATED FROM PYTHON SOURCE LINES 24-33

Hand Gesture Recognition with Acoustic Myography and Wavelet Scattering Transform
-----------------------------------
Reimplementation of "Hand Gesture Recognition with Acoustic Myography and Wavelet Scattering Transform"

This dataset contains acoustic myography of different hand gestures. The aucoustic data is in 8 channel. In this library, subjects
AA01, CU14, DH18, NL20, NM08 and SR11 are selected as testing data, while the remaining used for training.

The classes contains in the dataset are [Pronation, Supination, Wrist Flexion, Wrist Extension, Radial Deviation, Ulnar Deviation,
Hand close, Hand open, Hook grip, Fine pinch, Tripod grip, Index finger flexion, Thumb finger flexion, and No movement (Rest)]

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Load the data
------------------------
Method 1: Use get_dataloader

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.. code-block:: Python

    from pysensing.acoustic.datasets.get_dataloader import *
    train_loader,test_loader = load_hgr_dataset(
        root='./data',
        download=True)

    # Method 2: Manually setup the dataloader
    root = './data' # The path contains the samosa dataset
    amg_traindataset = AMG(root,'train')
    amg_testdataset = AMG(root,'test')
    # Define the Dataloader
    amg_trainloader = DataLoader(amg_traindataset,batch_size=32,shuffle=False,drop_last=True)
    amg_testloader = DataLoader(amg_testdataset,batch_size=32,shuffle=False,drop_last=True)
    #List the activity classes in the dataset
    dataclass = amg_traindataset.class_dict
    # Example of the samples in the dataset
    index = 128
    # Randomly 3elect an index
    spectrogram,activity= amg_traindataset.__getitem__(index)
    plt.figure(figsize=(10,6))
    plt.imshow(spectrogram.numpy()[0])
    plt.title("Spectrogram for activity: {}".format(activity))
    plt.show()



.. image-sg:: /acoustic/images/sphx_glr_acoustic_hgr_tutorial_001.png
   :alt: Spectrogram for activity: 2
   :srcset: /acoustic/images/sphx_glr_acoustic_hgr_tutorial_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none

    using dataset: AMG




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And that's it. We're done with our acoustic hand gesture recognition tutorials. Thanks for reading.


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

   **Total running time of the script:** (0 minutes 1.226 seconds)


.. _sphx_glr_download_acoustic_acoustic_hgr_tutorial.py:

.. only:: html

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

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

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

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

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


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