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SEMG onset detection caused by Temperature Variation

Abstract:

This article is part of a research work that aims to characterize the effect of the temperature at the myoelectric level in the human body, specifically in upper extremities in order to be applied to improve the prosthesis in the future. Surface electromyographic signals (sEMG) are currently being used for many medical applications, hence the importance of characterizing them in the best possible way. These signals are acquired and processed by electronic interfaces connected to the computer by means of a data acquisition card and processed in MatLab-Simulink. The study consists in comparing the times in which the significant changes in the surface electromyographic signal (sEMG) occur, considering that the only parameter that is being manipulated is the temperature and with the assumption that the rest of the parameters involved are kept constant. By using a statistical method like the AGLR (approximate generalized likelihood ratio) algorithm, it is being corroborated that there are changes at a myoelectric level when this variable affects the upper limb, to develop a mathematical model that will serve as a guide for temperature changes recognition patterns. The results obtained are favorable and open the possibility for this tool to perform real-time detection for future applications in this research.