Study of K-Nearest Neighbour Classification Performance on Fatigue and Non-Fatigue EMG Signal Features

Abstract

For our body to move, the muscle must activate by relaxing and contracting. Muscle activation produces bio-electric signals that can be detected using Electromyography or EMG. The signal produced by the muscle is affected by the type of contraction done by the muscle. The eccentric contraction generating different EMG signals from concentric contraction. EMG signal contains multiple features. These features can be extracted using MATLAB software. This paper focuses on the bicep brachii and brachioradialis in the upper arm and forearm, respectively. The EMG signals are extracted using surface EMG whereby electrical pads are placed onto the surface of the muscle. Features can then be extracted from the EMG signal. This paper will focus on the MAV, VAR, and RMS features of the EMG signal. The features are then classified into eccentric, concentric or isometric contraction. The performance of the K-Nearest Neighbour (KNN) classifier is inconsistent due to the EMG data variabilities. The accuracy varies from one data set to another. However, it is concluded that non-fatigue signal classification accuracy is higher than fatigue signal classification accuracy.

Sport Fatigue Monitoring and Analyzing Through Multi-Source Sensors

“…Furthermore, we compare GP with some other canonical classifiers, including k-nearest neighbor (kNN) (Bukhari et al 2020), linear discriminant analysis (LDA) (Venugopal et al 2014;Zhu et al 2022) and neural network (NN) (Subasi & Kiymik 2010), logistic regression (LR) (Marri & Swaminathan 2015) when using MFCC features. The parameter k is tuned in the range from 1 to10 stepped by 2.…”

Application of Surface Electromyography in Exercise Fatigue: A Review

“…Feature extraction from the sEMG signal plays an important role in the accuracy of fatigue detection. Time domain, frequency domain, time-frequency domain, and nonlinear parameters are four major types in sEMG-based signal processing ( Too et al, 2018b ; Yousif et al, 2019 ; Bukhari et al, 2020 ).…”

“…Classification in this article normally refers to supervised learning where individuals are classified based on their features. Some classification algorithms based on sEMG are listed in Table 1 , mainly including fuzzy logic (FL) ( Li, 2017 ), hidden Markov model (HMM) ( Shahmoradi et al, 2017 ), k-nearest neighbor (KNN) ( Bukhari et al, 2020 ), support vector machine (SVM) ( Chen et al, 2021b ), linear discriminant analysis (LDA) ( Ahmed et al, 2020 ), and artificial neural network (ANN) ( Subasi and Kiymik, 2010 ).…”

The Muscle Fatigue’s Effects on the sEMG-Based Gait Phase Classification: An Experimental Study and a Novel Training Strategy

“…Bukhari et al [22] have studied the performance of the K-Nearest Neighbors (KNN) classifier on the non-fatigue data and the fatigue data. In their study, the subjects were asked to perform the "bicep curl", and muscle fatigue was induced by the repetitive motion.…”

“…Tkach et al [21] studied the influence of three factors, including muscle fatigue, on the classification of four contraction patterns, which focused on time-domain features. Bukhari et al [22] performed the experiments to investigate the performance of the K-nearest neighbor (KNN) classifier, which was used to classify contraction patterns of biceps brachii under muscle fatigue. Although presenting impressive inspirations, how the non-fatigue data-trained classifiers would deteriorate on fatigue data might still be a research gap.…”