Comparison of KNN and Random Forest Algorithms in Classifying EMG Signals

Depending on the growing average age and busy work life, muscle disorders are also increasing. Disturbing use life hurts the upper limb due to casing. Electromyography (EMG) muscle sensors are used to detect muscle diseases. To obtain more accurate results, the perception of the data received with the EMG sensors is required. This evaluation was compared with electromyography (EMG) muscle sensors used as a muscle measurement tool and those taken from the upper limb and KNN explanations and Random Forest examinations, which are the predictions of machine learning in this context and give more accurate results than other effects. Three EMG muscle sensors are attached to the upper limb of the user and taken from 0o, 45o and 90o angles with the microcontroller development board. It has been read and tested with the resulting machine-learning readings. The percentages of the accuracy of the highest accuracy KNN and Random Forest locations were chosen for their assumptions and use in use.