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Impact of EMG Signal Filters on Machine Learning Model Training: A Comparison with Clustering on Raw Signal
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Advisor(s)
Abstract(s)
Our current society faces challenges in integrating individuals
with disabilities, making this process difficult and painful. People
with disabilities (PwD) are often mistakenly considered incapable due to
the difficulties they face in daily tasks due to the lack of adapted means
and tools. In this context, assistive technologies play a crucial role in
improving the quality of life for these individuals. However, assistive
technologies still have various limitations, making research in this area
essential to enhance existing solutions and develop new approaches that
meet individual needs, aiming to promote inclusion and equal opportunities.
This paper presents a research project that focuses on the study
of electromyography (EMG) signal processing generated by individuals
who have undergone amputations. These signals are essential in assistive
technologies, such as myoelectric prostheses. The study focuses on
the impact of different filters and machine learning training methods on
this processing. The results of this study have the potential to provide
relevant findings for the development of more efficient assistive technologies.
By understanding the processing of EMG signals and applying
machine learning techniques, it is possible to improve the accuracy and
response speed of prosthetics, increasing the functionality and naturalness
of movements performed by users, as well as paving the way for the
emergence of new technologies.
Description
Keywords
Assistive technologies Electromyography (EMG) signal processing Machine learning
Pedagogical Context
Citation
Barbosa, Ana; Ferreira, Edilson; Grilo, Vinicius; Mattos, Laercio; Lima, José (2024). Impact of EMG Signal Filters on Machine Learning Model Training: A Comparison with Clustering on Raw Signal. In 3rd International Conference on Optimization, Learning Algorithms and Applications (OL2A 2023). Cham: Springer Nature, Vol. 2, p. 50–62. ISBN 978-3-031-53035-7
Publisher
Springer Nature