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project-emg.html
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<!DOCTYPE html>
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<title>Hand Movement Classification</title>
<meta name="description" content="Multi-subject Identification of Hand Movements Using Machine Learning" />
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<section class="project-cs-hero">
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<h1 class="heading-primary">Identification of Hand Movements Using Machine Learning</h1>
<div class="project-cs-hero__info">
<p class="text-primary">
Project aimed to develop an automatic hand gesture recognition system based on supervised
Machine Learning techniques.
</p>
</div>
<div class="project-cs-hero__cta">
<a href="https://link.springer.com/chapter/10.1007%2F978-3-030-78901-5_11" class="btn btn--bg" target="_blank">Paper Link</a>
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<h3 class="project-details__content-title">Project Overview</h3>
<p class="project-details__desc-para">
Electromyography (EMG) signals are generated by the electrical activity of skeletal muscle fibers, and are used by
health professionals for several purposes, including disease diagnosis and rehabilitation. These kind of signals are
random, highly non-stationary, and display subtle variations associated with anatomical and motor coordination differences
between subjects. Recently, with the increase number of automatic learning techniques, it is now possible to develop and
train systems to recognize movements, action potentials and diagnose neuromuscular diseases, overcoming the limitations
presented by the nature of these signals.
</p>
<p class="project-details__desc-para">
In this project eight computational models were trained to recognize six hand
gestures and generalize between different subjects using raw data recordings
of EMG signals from 36 subjects. In addition to the application in the health sector,
digital inclusion should be aimed at individuals with physical disabilities, with
which, this models could contribute to the development of identification
and interaction devices that can emulate the movement of hands.
</p>
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<h3 class="project-details__content-title">Results Highlight</h3>
<p class="project-details__desc-para">
The accuracy obtained in the cross-validation procedure for Random Forest (<b>96.25%</b>), ANN (<b>96.09%</b>) and CNN (<b>95.84%</b>) is comparable to the literature reports
and outperforms the results presented as state-of-the-art for this classification task. Furthermore, the dataset used in this research, which contains data
from 36 patients is bigger than those previously used in most multi-subject EMG signal classification tasks.
</p>
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<div class="project-details__desc">
<h3 class="project-details__content-title">Products</h3>
<p class="project-details__desc-para">
[1] A. Mora-Rubio et al., “Multi-subject Identification of Hand Movements Using Machine Learning,”
<i>Sustainable Smart Cities and Territories</i>, Springer International Publishing, p. 117-128, 2022, doi:
<a style="color: #0062b9; text-decoration-line: underline;" href="https://doi.org/10.1007/978-3-030-78901-5_11"
target="_blank" rel="noreferrer">10.1007/978-3-030-78901-5_11</a>.
</p>
</div>
<div class="project-details__tools-used">
<h3 class="project-details__content-title">Tools Used</h3>
<div class="skills">
<div class="skills__skill">Python</div>
<div class="skills__skill">TensorFlow</div>
<div class="skills__skill">Scikit-Learn</div>
</div>
</div>
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<h3 class="project-details__content-title">See Resources</h3>
<a
href="https://link.springer.com/chapter/10.1007%2F978-3-030-78901-5_11"
class="btn btn--med btn--theme project-details__links-btn"
target="_blank"
>Paper Link</a
>
<a
href="https://github.com/BioAITeam/emg-signal-classification"
class="btn btn--med btn--theme-inv project-details__links-btn"
target="_blank"
>Code Link</a
>
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