The best window selection of electromyography signal during riding motorcycle using spectrogram

Mohd Saad, Norhashimah and Tengku Zawawi, Tengku Nor Shuhada and Abdullah, Abdul Rahim and Sudirman, Rubita and Rashid, Helmi (2023) The best window selection of electromyography signal during riding motorcycle using spectrogram. International Journal of Intelligent Systems and Applications in Engineering, 11 (3). 530 - 538. ISSN 2147-6799

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Abstract

Electromyography (EMG) signals are widely used as an important tool whichhelpsto understand human activities. However, EMG signal has the complexity of random signals, highly nonlinear, non-stationary,and multi-frequency properties. Previous researchers have appliedthe time domain and frequency domain, but it lacks either time or frequency information, thus time-frequency distribution (TFD) such as Spectrogram is suitable and widely used in extracting EMG signals. However, this method using Hanning Window is a fixed window that compromises between time and frequency resolution. Some researchers used timewindow selection intheir research, however,there are no standard guidelines for determining window selection for all EMG signals. Thus, this paper has presented the guidelines for determining the best window size for EMG signal whileriding a motorcycle using Spectrogram.There are eight muscles for left and right from four types of muscles group which are Extensor Carpi Radialis, Trapezius, Erector Spinae,and Latissimus Dorsi.Six window sizes of 128, 256, 512, 1024, 2048 and 4096 ms are selected to determine the best size window to be used for the future analysis of the EMG signal. Machine Learning of SVM is used for confirmation performance evaluation for the best window size as the highest accuracy results. The results have proved window size 1024 is the best window size for EMG signal for riding a motorcycle. From this finding, the future analysis of this signal will use this size window when involving Spectrogram method.in the future.

Item Type:	Article
Uncontrolled Keywords:	Electromyography (EMG), Time-frequency Distribution (TFD), Spectrogram, Window Selection, Support Vector Machine (SVM)
Divisions:	Faculty of Electrical and Electronic Engineering Technology
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	06 Nov 2025 04:16
Last Modified:	06 Nov 2025 04:16
URI:	http://eprints.utem.edu.my/id/eprint/29128
Statistic Details:	View Download Statistic

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