Detection of muscle fatigue, from statistical methods to software applications
Journal Title: UNKNOWN - Year 2017, Vol 1, Issue 1
Abstract
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 “arduino” 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 tram’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 microvolt’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 fatigue’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.
Authors and Affiliations
Mohamed REZKI, Abdelkader BELAIDI
Keywords
Related Articles
- EP ID EP261214
- DOI 10.26415/2572-004X-vol1iss1p8-8
- Views 54
- Downloads 0
Brain Structures Segmentation by using Statistical Models
Background: Automatic segmentation of brain structures is a fundamental step for quantitative analysis of images in many brain’s pathologies such as Alzheimer’s, brain’s tumors or multiple sclerosis. The goal of our work...
Effect of natural extracts of a medicinal plant: Pistacia lentiscus on lipid peroxidation
Medicinal plants are important for pharmacological research and the development of medicines thanks to their active principle which are grouped under metabolites known as secondary metabolites. These researches mad...
Research of the yeasts Candida non - albicans on various medical devices in the service of resuscitation of the Teaching hospital of Tlemcen, Algeria
Background and aim: The fungic infections, more particularly the Candidiasis are frequent in the intensive care units. This had a relation with the heaviness of the pathologies, presented by the patients and the quasi-sy...
Real-time sleep apnea detection using wavelet packet transform and support vector machines
Sleep apnea events as obstructive, central, mixed, or hypopnea are characterized by frequent breathing cessations or reduction in upper airflow during sleep. An advanced method for analyzing the patterning of biomedical...
A Compact Sift-Based Strategy for Visual Information Retrieval in Large Image Databases
This paper applies the Standard Scale Invariant Feature Transform (S-SIFT) algorithm to accomplish the image descriptors of an eye region for a set of human eyes images from the UBIRIS database despite photometric transf...