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SVM Classifier and evaluation of muscle power of EMG signals and Python implementation

In this work, an EMG-based signal implemented through a Python programming methodology classifier is presented. The developed system is based on the flexor digitorum profundus muscle signals where the data are obtained through the measurement stage on the EKG/EMG board which is transmitted using an XBEE protocol with a Raspberry Pi3 platform interfaced. The purpose of this work is to design accurate and low-cost post-pandemic techniques and technologies in order to make contributions to telemedicine. The classification of the EMG signals based on a SVM classifier for more than 30 tests shows a precision over of 93.3% of the cases sensed and classified correctly. The experimental results show the hyperplane of the classifier appropriately divides the low potential data against the high potential ones, which represents that the muscle under test established in two classes.