Detection of muscle fatigue, from statistical methods to software applications
Background: Muscle fatigue has become increasingly present in our daily lives. This is related to lifestyle difficulties. Several methods were proposed in order to detect the muscle fatigue. This report proposes a short review of statistical methods and processing tools extracted from MATLAB software dedicated to detection of fatigue.
Methods: The first part in this study is an application of a useful electronic card named â€œarduinoâ€ for acquiring the electromyography signal (EMG). This latter is the perfect signal to describe fatigue in muscles. The acquisition of data is done in two steps; the first is a simple acquisition representing rest (the subject is relaxed). Then, in the next step, the subject does a series of physical exercises representing moving continuously a handlebar (in order to simulate the work of the tramâ€™s conductor). After obtaining raw data from the acquired signals, we apply statistical methods and some processing tools in order to detect fatigue.
Results: For the statistical method, we apply the spectral density, which is a mathematical tool that represents the various spectral components of a signal and to perform the harmonic analysis. We deduct that 80 microvoltâ€™s is the intensity of getting fatigue (exercise of moving the handlebar). Using processing tools (FFT and STFT techniques), we obtain essential information on the fatigueâ€™s beginning time.
Conclusion: A brief survey of statistical and processing tools to extract fatigue information from an EMG signal was done. A typical example of the importance of detecting fatigue was also illustrated (tram conductor). We aimed for a lot of results from this study, especially because we want to compare techniques. After studying, the STFT technique seem the best.
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