Selective ensemble learning for cross-muscle ALS disease identification with EMG signal

Wang, Xujian; Teng, Shenghua; Hao, Chenxu; Liu, Yan; He, Ji; Zhang, Shuo

doi:10.1109/bibm55620.2022.9995037

Cited by 2 publications

(1 citation statement)

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“…The EMG signal is complex and not static. Traditional signal analysis, various feature extraction and classification methods have been proposed for EMG classification in the literature (Wang, 2022, Bawa, 2022. It consists of stages such as labelling the classes for examining the EMG signals, determining the appropriate feature vectors and choosing the optimum, determining the classification models, and calculating the classification success (Akgün, 2022).…”

Section: Introductionmentioning

confidence: 99%

Comparison of KNN and Random Forest Algorithms in Classifying EMG Signals

ERSİN,

YAZ

2023

EJOSAT

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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.

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Section: Introductionmentioning

confidence: 99%