Papers by Tengku Nor Shuhada Tengku Zawawi
Electromyography Features Information Based on Functional Range of Motion for Health Screening Program
2023 International Conference on Advanced Technologies for Communications (ATC)
Musculoskeletal disorder (MSDs) is one of the most popular issues of occupational injuries and di... more Musculoskeletal disorder (MSDs) is one of the most popular issues of occupational injuries and disabilities. It has a big impact and creates a big problem for industries to be resolved. In MSDs, electromyography (EMG) is one of the methods to be studied in order to detect MSDs problem. This research focuses on the EMG signal analysis by using time domain and frequency domain (Welch Power Spectral Density) method. It gives more information from the signal and it is the most suitable method for classifying the moments in order to identify the behavioural of the signals. Axial rotational reach and upper level reach task from Health Screening Program (HST) is performed using functional range of motion (FROM) by considering left and right biceps brachii muscles to be analysed. There are two parameters chosen for each time and for each frequency domain to be tested, which are mean an absolute value (MAV) and root mean square (RMS) for time domain. Median frequency (MDF) and mean frequency...
Journal of Telecommunication, Electronic and Computer Engineering, 2016
Electromyography (EMG) signal is non-stationary signal and highly complex time and frequency char... more Electromyography (EMG) signal is non-stationary signal and highly complex time and frequency characteristics. Fast-Fourier transform common technique in signal processing involving EMG signal. However, this technique has a limitation to provide the time-frequency information for EMG signals. This paper presents the analysis of EMG signal of the variable lifting height and mass of load between the four subjects selected in manual lifting by using spectrogram. Spectrogram is one of the time-frequency representation (TFR) that represents the threedimensional of the signal with respect to time and frequency in magnitude presentations. The manual lifting tasks is based on manual lifting of 5 kg and 10 kg load that performed by the right biceps brachii at lifting height of 75 cm and 140 cm. Four from ten healthy volunteers in fresh condition is selected into this comparison of subject performance tasks with their raw data collections. The raw data of EMG signals were then analyzed using M...
Journal of Telecommunication, Electronic and Computer Engineering, 2016
Time-frequency representation of a signal has been widely used in various research areas to analy... more Time-frequency representation of a signal has been widely used in various research areas to analyze non-stationary signals (ie. electromyography (EMG) signals). However, due to the high computational complexity of certain time-frequency distribution techniques, the application of these techniques in the analysis of long duration EMG signals is not suitable. To overcome this problem, muscle contraction segmentation is essential to process the existed EMG signals, since not all of the EMG signal contains valid information to be analyzed. Thus, this paper presents an algorithm to automatically detect and segment the muscle contractions existed in EMG signal during long duration recordings. Surface EMG signals were collected from biceps branchii muscle of ten subjects during manual lifting. Subjects were required to lift a 5 kg load mass with lifting height of 75 cm until experiencing fatigue. The utilization of instantaneous energy of EMG is used to estimate the presence of first muscl...
A Review of Electromyography Signal Analysis of Fatigue Muscle for Manual Lifting
2020 6th International Conference on Computing Engineering and Design (ICCED), 2020
Electromyography (EMG) signal is decidedly complex time and frequency characteristics. The common... more Electromyography (EMG) signal is decidedly complex time and frequency characteristics. The common technique of Fast-Fourier transform is applied in signal processing involving EMG signal. However, to provide time-frequency information for EMG signals, it has a limitation. This paper presents the systematic review of the concept of EMG signal and how the EMG signal can be analysed which focuses on signal processing using time-frequency distribution. Spectrogram is suggested to used compared to short-time Fourier transform (STFT) and wavelet because it is lower process complexity, high resolution and higher accuracy of EMG signal's interpretation. Besides that, from the spectrogram, some of the signal characteristics are identified able to provide clearer information of the analysed signal. Thus, this paper will help the researcher in order to get an overview of the concept of EMG signal. A further researcher can expand the information to get more advanced in this field based on this concept.
Deep Convolutional Neural Network for Featureless Electromyogram Pattern Recognition Using Time-Frequency Distribution
Sensor Letters, 2018
Indonesian Journal of Electrical Engineering and Computer Science, 2018
Social Security Organisation (SOCSO) Malaysia has reported that the incidence of work related to ... more Social Security Organisation (SOCSO) Malaysia has reported that the incidence of work related to musculoskeletal disorders (MSDs) has been growing planetary in the manufacturing industry. MSDs are the result of repetitive, forceful or awkward movements on our body and or body parts of bones, joints, ligaments and other soft tissues. Workplace pains and strains can be serious and disabling for workers, causing pain and suffering ranging from discomfort to severe disability. To overcome this problem, Electromyography is proper to use in Health Screening Program (HSP) it to monitor darn diagnose the muscle’s performance for their patient and know the exact localization of muscle pain. The previous researchers has been explore of several in EMG analysis techniques and features proposed in time, frequency and time-frequency domain analysis. This review of common EMG signal processing techniques is proposed by assembling from simple to complex analysis techniques to give the overview info...
Indonesian Journal of Electrical Engineering and Computer Science, 2017
The purpose of this paper is to improve the features of Health Screening Test System (HSTS) on So... more The purpose of this paper is to improve the features of Health Screening Test System (HSTS) on Social Security Organization (SOCSO) program as physical evaluation for musculoskeletal disable workers (MSDs). SOCSO existing functional testing system are not suitable because of the evaluation was recorded manually peg board too high for Asian people. The occupational therapist whose involve in all the procedures is just doing the judgment in times to determine the capability of the patients. The functional capacity evaluation (FCE) technique is based on the functional range of motion evaluation that consist of positional tolerance respecting to time-motion testing on HSTS peg board and it is by referring to the original work movement. The main features of HSTS are able to measure speed, acceleration and evaluation of SOCSO’s patients for returning to work based on SOCSO’s requirement. In order to validate the accuracy of the proposed model, HSTS is used to evaluate the patient’s positi...
sEMG signals analysis using time-frequency distribution for symmetric and asymmetric lifting
2015 International Symposium on Technology Management and Emerging Technologies (ISTMET), 2015
Following the industrial revolution, a third of the world's economic output is derived from m... more Following the industrial revolution, a third of the world's economic output is derived from manufacturing industries. In the manufacturing industry sectors, manual lifting is commonly practiced even though mechanized material handling equipment are provided. Due to this repetitive lifting behavior, it will contributes to muscle fatigue and low back pain that can lead to work efficiency and low productivity. This will cause great losses to the company and to the country. The purpose of this study is to investigate changes in the surface electromyography (sEMG) signals during repetitive manual lifting for different twist angle values. The EMG signals are taken from right Biceps Branchii of five healthy subjects (age range 21 - 25 years) while performing symmetric and asymmetric lifting of twist angle 0° and 90° at lifting height of 140 cm with load mass of 10 kg. The analysis is done by applying time-frequency distribution (TFD) which is spectrogram technique to represent the EMG signal in time-frequency representation (TFR) before displaying the instantaneous voltage, Vrms (t) value. This study found that symmetric lifting requires less force to lift the load and that asymmetric lifting has faster tendency to experienced fatigue. This study concludes that the twist angle has a significant influence to the muscle performance of right Biceps Branchii.
Electromyography (EMG) is widely used in controlling the signal in manipulating the robot assiste... more Electromyography (EMG) is widely used in controlling the signal in manipulating the robot assisted rehabilitation. In order to manipulate a more accurate robot assisted, the feature extraction and selection were equally important. This study evaluated the performance of time domain (TD) and frequency domain (FD) features in discriminating EMG signal. To investigate the features performance, the linear discriminate analysis (LDA) was introduced. The present study showed that the FD features achieved the highest accuracy of 91.34% in LDA. The results were verified by LDA classifier and FD features showed best classification performance in EMG signal classification application.
This paper presents the segmentation process using spectrogram for electromyography (EMG) signal ... more This paper presents the segmentation process using spectrogram for electromyography (EMG) signal analysis. Manual lifting activities are repeated to five times with the different load mass and lifting height are performed until achieve muscle fatigue to collect the data. The results show the technique used is able to differentiate between the contraction and baseline. Thus, contraction counted is able know the performance of the EMG signal. The increasing of load masses and high is inversely proportional to the muscle performance. The overall results conclude that, the application of spectrogram able to use in auto segmentation process for EMG signal.
In manufacturing industries, manual lifting is commonly practiced by workers in their routine to ... more In manufacturing industries, manual lifting is commonly practiced by workers in their routine to move or transport the objects to a desired place. Manual lifting with high repetition and loading on the arm will contribute the effects of soft tissues and muscle fatigue that will affect the performance of the worker to work with efficient. This paper presents the analysis of EMG signal from muscle activity to see the performance of muscle fatigue. Various researchers have proposed fast Fourier transforms (FFT) in analysing the EMG signal. However, this technique only gives spectral information but does not provide temporal information. Thus, the technique is not suitable for EMG analysis that consists of magnitude and frequency variation. To overcome the limitation, spectrogram is proposed to analyse the signal because it can represent the signal in jointly time-frequency representation (TFR). In fatigue muscle activities, ten volunteers in fresh condition and no previous of history injury are used as the subjects. Data is taken from right Biceps Branchii with lifting height of 140 cm and load mass of 5 kg. This research shows that the repeatability of manual lifting will contribute to the muscle fatigue for all the phases stated in this paper. This study concludes that phase 2 contribute highest effort by doing manual lifting task, compared to phase 1, 3 and 4, but all phases experienced the muscle fatigue.
Asian Journal Of Medical Technology, 2021
Musculoskeletal disorders (MSDs) are widespread through the world and are the second most common ... more Musculoskeletal disorders (MSDs) are widespread through the world and are the second most common cause of disability in work setting. There are many method used to analyse MSDs to know the reality situation and affacted to the employess The review is to compare in terms of design, methodology, approach and identify the equipment and method used from the previous researchers that have many advantages dan disadvantage of the method to come out the best suggestion of equipment and method proper to used and improvement that should be do for the future researchers. The relevant literature was obtained from the following strategy. Kerwords were idenfied after a scoping study into the some types of MSDs analysis by focusing on the equipment and method. A number of articles between 2010-2021 were extracted from Google Scholar database using keywords of “musculoskeletal disorders analysis”, “ergonomics analysis” and “ MSD identification”. The study found that most of the method in MSD more f...
International Journal of Integrated Engineering, 2019
This paper presents a study of the classification of myoelectric signal using spectrogram with di... more This paper presents a study of the classification of myoelectric signal using spectrogram with different window sizes. The electromyography (EMG) signals of 40 hand movement types are collected from 10 subjects through NinaPro database. By employing spectrogram, the EMG signals are represented in time-frequency representation. Ten features are extracted from spectrogram for performance evaluation. In this study, two classifiers namely support vector machine (SVM) and linear discriminate analysis (LDA) are used to evaluate the performance of spectrogram features in the classification of EMG signals. To determine the best window size in spectrogram, three different Hanning window sizes are examined. The experimental results indicate that by applying spectrogram with optimize window size and LDA, the highest mean classification accuracy of 91.29% is obtained.
TELKOMNIKA (Telecommunication Computing Electronics and Control), 2019
In recent day, Electromyography (EMG) signal are widely applied in myoelectric control. Unfortuna... more In recent day, Electromyography (EMG) signal are widely applied in myoelectric control. Unfortunately, most of studies focused on the classification of EMG signals based on healthy subjects. Due to the lack of study in amputee subject, this paper aims to investigate the performance of healthy and amputee subjects for the classification of multiple hand movement types. In this work, Gabor transform (GT) is used to transform the EMG signal into time-frequency representation. Five time-frequency features are extracted from GT coefficient. Feature extraction is an effective way to reduce the dimensionality, as well as keeping the valuable information. Two popular classifiers namely k-nearest neighbor (KNN) and support vector machine (SVM) are employed for performance evaluation. The developed system is evaluated using the EMG data acquired from the publicy available NinaPro Database. The results revealed that the extracting GT features can achieve promising performance in the classification of EMG signals.
Indonesian Journal of Electrical Engineering and Computer Science, 2019
In this paper, the performance of featureless EMG pattern recognition in classifying hand and wri... more In this paper, the performance of featureless EMG pattern recognition in classifying hand and wrist movements are presented. The time-frequency distribution (TFD), spectrogram is employed to transform the raw EMG signals into time-frequency representation (TFR). The TFRs or spectrogram images are then directly fed into convolutional neural network (CNN) for classification. Two CNN models are proposed to learn the features automatically from the images without the need of manual feature extraction. The performance of CNN with different number of convolutional layers is examined. The proposed CNN models are evaluated using the EMG data from 10 intact and 11 amputee subjects through the publicly access NinaPro database. Our results show that CNN classifier offered the best mean classification accuracy of 88.04% in recognizing hand and wrist movements.
Journal of Electrical Engineering & Technology, 2019
The application of electromyography (EMG) has shown great success in rehabilitation engineering. ... more The application of electromyography (EMG) has shown great success in rehabilitation engineering. With the existing multiple-channel EMG recording system, the detection and classification of EMG pattern have become viable. The purpose of this study is to investigate the relation between sampling rate and EMG pattern recognition by using spectrogram. The features are extracted from spectrogram coefficients and the principal component analysis is applied for dimensionality reduction. In addition, the optimal Hanning window size is identified and selected before performance evaluation. For noise evaluation, the additive white Gaussian noise (AGWN) is added to the EMG signal at 30, 25, 20 dB SNR. The results illustrated that the 512 Hz sampling rate can maintain a small decrement of 0.76% accuracy compared to 1024 Hz. However, when the AGWN is added, the 256 and 512 Hz sampling rates showed a greater reduction in overall classification performance. For a lower SNR, the gaps in classification accuracy between 1024 Hz, 512 Hz and 256 Hz sampling rates are obviously presented. It signifies that reducing the sampling rate lower than 1024 Hz might not be a good choice since the noise and artifact have to be taken into consideration in a real system.
Electromygraphy Signal Analysis Using Spectrogram
Applied Mechanics and Materials, 2015
Renewable energy is an alternative option that can be substituted for future energy demand. Many ... more Renewable energy is an alternative option that can be substituted for future energy demand. Many type of battery are used in commerce to propel portable power and this makes the task of selecting the right battery type is crucial. This paper presents the analysis of voltage charging and discharging for lead acid battery using time-frequency distribution (TFD) which is spectrogram. Spectogram technique is used to represent the signals in the time-frequency representation (TFR). The parameter of a signal such as instantaneous root mean square (RMS) voltage, direct current voltage (VDC) and alternating current voltage (VAC) are estimated from the TFR to identify the signal characteristics. This analysis, focus on lead-acid battery with nominal battery voltage of 6 and 12V and storage capacity from 5 until 50Ah. The battery is a model using MATLAB/SIMULINK and the results show that spectrogram technique is capable to identify and determine the signal characteristic of Lead Acid battery.
2013 IEEE Student Conference on Research and Developement, 2013
Electromyography (EMG) is known as complex bioelectricity signals that representing the contracti... more Electromyography (EMG) is known as complex bioelectricity signals that representing the contraction of the muscle in humanbody. The EMG signal offers useful information that can help to understand the human movement. Many techniques have been proposed by various researchers such as fast Fourier transforms (FFT). However, the technique only gives temporal information of the signal and does not suitable for EMG that consists of magnitude and frequency variation. In this paper,the analysis of EMG signal is presented using timefrequency distribution (TFD) which is spectrogram with different window size. Since the spectrogram represent the theEMG signal in time-frequency representation (TFR), it is very appropriate to analyze the signal. The EMG signals from Biceps muscle of two subjects are collected for body position of 0° and 90°. From the TFR, parameters of the signal such as instantaneous fundamental root mean square (RMS) voltage (Vrms) are estimated. To identify the suitable windows size, spectrogram with size window of 64, 256, 512 and 1024 is used to analyze the signal and the performance of the TFR are evaluated. The results show that spectrogram with window size of 512 gives optimal TFR of the EMG signals and suitable to characterize the signal.
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Papers by Tengku Nor Shuhada Tengku Zawawi