Version 1
: Received: 10 July 2024 / Approved: 11 July 2024 / Online: 11 July 2024 (04:52:24 CEST)
How to cite:
Chakraborty, P.; Neogi, B.; Das, A. Non Linear Asymptotic Analysis of Muscle Dynamics Using Diagnostic Electromyographic (D-EMG) Signal. Preprints2024, 2024070911. https://doi.org/10.20944/preprints202407.0911.v1
Chakraborty, P.; Neogi, B.; Das, A. Non Linear Asymptotic Analysis of Muscle Dynamics Using Diagnostic Electromyographic (D-EMG) Signal. Preprints 2024, 2024070911. https://doi.org/10.20944/preprints202407.0911.v1
Chakraborty, P.; Neogi, B.; Das, A. Non Linear Asymptotic Analysis of Muscle Dynamics Using Diagnostic Electromyographic (D-EMG) Signal. Preprints2024, 2024070911. https://doi.org/10.20944/preprints202407.0911.v1
APA Style
Chakraborty, P., Neogi, B., & Das, A. (2024). Non Linear Asymptotic Analysis of Muscle Dynamics Using Diagnostic Electromyographic (D-EMG) Signal. Preprints. https://doi.org/10.20944/preprints202407.0911.v1
Chicago/Turabian Style
Chakraborty, P., Biswarup Neogi and Achintya Das. 2024 "Non Linear Asymptotic Analysis of Muscle Dynamics Using Diagnostic Electromyographic (D-EMG) Signal" Preprints. https://doi.org/10.20944/preprints202407.0911.v1
Abstract
Article explores participant specific study of muscle inherehit dynamics hidden inside the diognostic electromyography (D-EMG) signals during load pull exercises, aiming to uncover underlying patterns and nonlinear behaviors. Employing a multi-step analytical framework, the study first evaluates signal stationarity through Quantile-Quantile (Q-Q) plot and surrogate data analyses, revealing insights into signal stability. Subsequent use of phase space reconstruction techniques exposes intrinsic dynamics within the EMG signals, visualized through Poincar´e maps, elucidating attractor geometry and system dynamics. Histogram analysis further dissects the Poincar´e maps, offering insights into signal behavior relative to physiological processes. Additionally, the study quantifies the largest Lyapunov exponent from the Poincar´e maps, providing a measure of dynamical complexity within the muscle EMG signals during load pull exercises. By integrating these methods, this research sheds light on the nuanced interplay between EMG signals and muscle activity during dynamic exercises, offering valuable insights for rehabilitation strategies.
Keywords
D-EMG Signal; Histogram Analysis; Largest Lyapunov Exponent (LLE); Non Stationarity Test; Phase Space Reconstruction; Recurrance Quantification Analysis (RQA); Surrogate Data Analysis; 3D Poincare Map
Subject
Computer Science and Mathematics, Mathematical and Computational Biology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
