A Comparison of Different Signal Processing Techniques for Upper Limb Muscle Activity Onset Detection using Surface Electromyography

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dc.contributor.authorKoppolu, P.K.
dc.contributor.authorChemmangat, K.
dc.date.accessioned2026-02-06T06:34:56Z
dc.date.issued2023
dc.description.abstractThis work presents the use of real-time experimental Surface Electromyography (sEMG) signals to determine muscle activity of upper limb by detecting the exact onset and offset timings. Various muscle activity detection methods were evaluated, such as Sample Entropy (SEn), Permutation Entropy (PEn), Amplitude Aware Permutation Entropy (AAPEn), and Integrated Profile (IP). The performance of these methods was compared, and it was found that IP detects muscle activity quickly and requires less computation for real-time implementation as compared to other methods. © 2023 IEEE.
dc.identifier.citation2023 3rd International Conference on Artificial Intelligence and Signal Processing, AISP 2023, 2023, Vol., , p. -
dc.identifier.urihttps://doi.org/10.1109/AISP57993.2023.10134857
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/29534
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.subjectElectromyography
dc.subjectEntropy
dc.subjectIntegration Profile
dc.subjectMuscle activity detection
dc.titleA Comparison of Different Signal Processing Techniques for Upper Limb Muscle Activity Onset Detection using Surface Electromyography

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