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Shariatzadeh, MohammadJavad

University of British Columbia

Dynamic muscle contractions are integral to most daily activities and sports. Assessing muscle fatigue during these contractions is crucial for optimizing athletic performance, preventing injuries, improving patient monitoring and diagnostics, and enhancing human-machine interactions. The mean and median frequency of the surface electromyography (sEMG) signals are the most commonly used spectral variables that are considered the gold standard for muscle fatigue assessment, especially in static contractile conditions, where the sEMG signal can be assumed as stationary. However, detecting and predicting muscle fatigue during dynamic exercise poses a complex challenge because of the many signal-distorting factors, such as electrode movements, variations in muscle tissue conductivity, and rapid changes in the recruitment/derecruitment of motor units. Performance-based methods that are commonly used to measure muscle fatigue during static contractions can not be used during dynamic movement without interrupting the main task and the chance of adding extra muscle fatigue. This dissertation presents an approach to predict muscle fatigue during dynamic contractions by employing wavelet analysis of surface electromyography (sEMG) signals. The advantage of this method for muscle fatigue detection is its ability to predict muscle fatigue induced by submaximal contractions without the need for directly measuring maximal performance. The study introduces a wearable sEMG monitoring device equipped with sensor fusion technology designed specifically to capture the complex dynamics of muscle activity during motion. Our signal processing method involves four main steps: (1) segmenting sEMG signals based on the gyroscope data recorded by IMU sensors, (2) selecting the most suitable mother wavelet, (3) denoising the sEMG signals, and (4) calculating normalized energy to predict maximal voluntary isometric contraction force as an indicator of muscle fatigue. A linear relationship was established between Maximum Voluntary Isometric Contraction (MVIC) and Percentage of Normalized Energy (PNE), allowing for the quantification and prediction of muscle fatigue for each person during exercise time. The linear relationship provides predictive insight into the state of muscle fatigue (i.e., the point where the activated muscles can no longer complete another repetition within the appropriate range of motion), enabling the anticipation of the muscle failure point by monitoring the sEMG signal.

Text

2024-09-23

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/89275

Mechanical Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/