Evaluation of windowing techniques for intramuscular EMG-based diagnostic, rehabilitative and assistive devices

Abstract

Objective. Intramuscular electromyography (iEMG) signals, invasively recorded, directly from the muscles are used to diagnose various neuromuscular disorders/diseases and to control rehabilitative and assistive robotic devices. iEMG signals are potentially being used in neurology, kinesiology, rehabilitation and ergonomics, to detect/diagnose various diseases/disorders. Electromyography-based classification and analysis systems are being designed and tested for the classification of various neuromuscular disorders and to control rehabilitative and assistive robotic devices. Many studies have explored parameters such as the pre-processing, feature extraction and selection of classifiers that can affect the performance and efficacy of iEMG-based classification systems. The pre-processing stage includes the removal of any unwanted noise from the original signal and windowing of the signal. Approach. This study investigated and presented the optimum windowing configurations for robust control and better performance results of an iEMG-based analysis system based on the stationarity rate (SR) and classification accuracy. Both disjoint and overlap, windowing techniques with varying window and overlap sizes have been investigated using a machine learning-based classification algorithm called linear discriminant analysis. Main results. The optimum window size ranges are from 200–300 ms for the disjoint and 225–300 ms for the overlap windowing technique, respectively. The inferred results show that for the overlap windowing technique the optimum range of overlap size is from 10%–30% of the length of window size. The mean classification accuracy (MCA) and mean stationarity rate (MSR) were found to be lower in the disjoint windowing technique compared to overlap windowing at all investigated overlap sizes. Statistical analysis (two-way analysis of variance test) showed that the MSR and MCA of the overlap windowing technique was significantly different at overlap sizes of 10%–30% (p-values < 0.05). Significance. The presented results can be used to achieve the best possible classification results and SR for any iEMG-based real-time diagnosis, detection and control system, which can enhance the performance of the system significantly.

Multi-user motion recognition using sEMG via discriminative canonical correlation analysis and adaptive dimensionality reduction

“…Feature extraction is an important step in signal processing, as it reduces the amount of data while also extracting useful features into low-dimensional data. As EMG signals are known to suffer from lack of smoothness, windowing was performed on the sEMG signals after pre-processing (Ashraf et al, 2021 ). The window length was 300 samples with a time period of 150 ms, and the window shift was 50 samples with a time period of 25 ms (Luo et al, 2020b ).…”

Generative Adversarial Neural Network and Genetic Algorithm To Predict Oil and Gas Pipeline Defect Length

“…To illustrate the significance of utilizing a larger dataset and automated optimization for ANN, the study results have been compared with previously published studies on the same dataset [7,12]. Both studies used a feed-forward neural network with a single hidden layer and 10 hidden neurons, and a levenberg-Marquardt training algorithm.…”

“…ML can be defined as a system based on a semiautomated process capable of observing the data for developing an algorithm. Such ML-based approaches have been applied for risk analysis purposes in various engineering applications [9][10][11][12][13]. Algorithms like K-nearest neighbor, support vector machine, and random forest have emerged as widely acceptable algorithms in the last decade [14].…”

Optimized Gaussian Process Regression for Prediction of Oil and Gas Pipelines Defect Length

“…In a broad variety of areas, machine learning approaches are used, such as automatic diagnosis processes, credit card fraud identification, stock market monitoring, biomedical signal processing, and text recognition, autonomous systems, etc. [19][20][21][22]. Traditional probabilistic physical analysis techniques, for the estimation of oil and gas pipeline failure estimation, are computationally expensive.…”

“…Several algorithms have transpired in the last decade, such as k-nearest neighbors, support vector machine, and random forest, etc. [19]. For predicting the probable failure of pipes with active defects due to corrosion Anghel et al ( 2009) developed an innovative kind of vector machine that is maximally supportive [23].…”