Abstract
For our body to move, the muscle must activate by relaxing and contracting. Muscle activation produces bio-electric signals that can be detected using Electromyography or EMG. The signal produced by the muscle is affected by the type of contraction done by the muscle. The eccentric contraction generating different EMG signals from concentric contraction. EMG signal contains multiple features. These features can be extracted using MATLAB software. This paper focuses on the bicep brachii and brachioradialis in the upper arm and forearm, respectively. The EMG signals are extracted using surface EMG whereby electrical pads are placed onto the surface of the muscle. Features can then be extracted from the EMG signal. This paper will focus on the MAV, VAR, and RMS features of the EMG signal. The features are then classified into eccentric, concentric or isometric contraction. The performance of the K-Nearest Neighbour (KNN) classifier is inconsistent due to the EMG data variabilities. The accuracy varies from one data set to another. However, it is concluded that non-fatigue signal classification accuracy is higher than fatigue signal classification accuracy.
Highlights
The movement of the body is a complicated process that requires a specific group of muscles to contract and relax in a specific order
The accuracy of the K-Nearest Neighbour (KNN) classifier in classifying signals into eccentric, concentric, or isometric contraction during dynamic and isometric movement is subjective to the data used
The KNN classifier was not able to classify correctly due to this variation
Summary
The movement of the body is a complicated process that requires a specific group of muscles to contract and relax in a specific order. Slow-twitch fibres are muscle fibres that twitch for a shorter time They are more unaffected to fatigue and have higher endurance, but they cannot generate rapid force. High-twitch fibres are muscle fibres which can twitch for a longer time [5] They have higher endurance but low resistant to fatigue, but they can generate rapid force. The voltage generated from this noise has an amplitude that is comparable to the amplitude of the EMG signal This type of motion artefact can be removed by using recessed electrodes. Specific features such as Mean Absolute Value (MAV), Root Mean Square (RMS), and Variance (VAR) can be extracted from the EMG signal These specific features are useful in detecting muscle fatigue as it is a time-domain feature, and muscle endurance is measured with time [12].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Advanced Computer Science and Applications
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
