A Non-Invasive Approach to Predict Risk In Dengue Hemorrhagic Fever (DHF) Using Bioelectrical Impedance Analysis

The purpose of this study was to validate a single BIA for predicting the risk in DHF in dengue patients in the Hospital Universiti Kebangsaan Malaysia (HUKM). The BIA technique based on the passing of low-amplitude electrical current less than 1 mA (500 to 800!A) with frequency 50kHz. During hospitalization, 210 patients who are 119 males and 91 females serologically confirmed DF and DHF patients were tested using single BIA. By using multiple regression analysis, race, reactance, complication, headache and the day of fever were found independent determinants of predicting the risk. Hence, this novel approach of BIA technique can provide rapid, non-invasive, and promising method for classifying and evaluating the status of the DHF patients

Open switch faults analysis in voltage source inverter using spectrogram

The performance and effects is critical factor in industry, especially usage power inverter such as motor, switching and control circuit. Until now, the statistic of effects still increase in the application. In order to overcome this, the spectrogram technique is used to represent the signals in time frequency representation (TFR). This paper introduces time-frequency distribution (TFD) technique for detecting and identifying the open circuit fault in application of inverter. The condition monitoring is based on time-frequency distribution. Since Fast Fourier Transform (FFT) is one of the techniques to analyze the signal, but it has some limitations in non-stationary signal. From TFR the parameters such as root means square voltage (Vrms), total waveform distortion (TWD), total harmonic distortion (THD) and total non-harmonics (TnHD) for voltage source inverter (VSI) are used to identify the characteristic of the signals. The result shows that spectrogram technique capable to identify and evaluate the information of voltage source inverter (VSI). The proposed technique is verified as simulation results

Finger Movement Discrimination Of EMG Signals Towards Improved Prosthetic Control Using TFD

Prosthetic is an artificially made as a substitute or replacement for missing part of a body. The function of the missing body part can be replaced by using the prosthesis and it can help disabled people do their activities easily. A myoelectric control system is a fundamental part of modern prostheses. The electromyogram (EMG) signals are used in this system to control the prosthesis movements by taking it from a person's muscle. The problem for the myoelectric control system is when it did not receive the same attention to control fingers due to more dexterous of individual and combined finger control in a signal. Thus, a method to solve the problem of the myoelectric control system by using time-frequency distribution (TFD) is proposed in this paper. The EMG features of the individual and combine finger movements for ten subjects and ten different movements is extracted using TFD, ie. spectrogram. Three machine learning algorithms which are Support Vector Machine (SVM), k-Nearest Neighbor (KNN) and Ensemble Classifier are then used to classify the individuals and combine finger movement based on the extracted EMG feature from the spectrogram. The performance of the proposed method is then verified using classification accuracy. Based on the results, the overall accuracy for the classification is 90% (SVM), 100% (KNN) and 100% (Ensemble Classifier), respectively. The finding of the study could serve as an insight to improve the conventional prosthetic control strategies

Electromygraphy Signal Analysis Using Spectrogram

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 time-frequency 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

Implementation of spectrogram for an improved EMG-based functional capacity evaluation's core-lifting task

This paper proposes a technique to automatically categorize work levels categories to improve the conventional functional capacity evaluation's core lifting task. Surface EMG signals were collected from biceps brachii and erector spinae muscles. Spectrogram was used as a pre-processing approach for auto-segmentation of the EMG signal and for the feature extraction. This set of features was extracted to accurately differentiate between a medium work level and heavy work level. These features were then reduced using linear discriminant analysis and support vector machine acts as a classifier. The results showed that the proposed system offered excellent performance in classifying the work levels categories with high accuracy, sensitivity, specificity, and zero cross-validation error

EMG pattern recognition using TFD for future control of in-car electronic equipment

Distracted drivers contribute to motor vehicle accidents. The maneuvering of in-car electronic equipment and controls, which typically requires the driver’s hands to be off the wheel and eyes off the road, are important factors that distract drivers. To minimize the interference of such distractions, a new control method is presented for detecting and decoding human muscle signals, which is known as electromyography (EMG). It is associated with various fingertips and pressures, and allows the mapping of various commands to control in-car equipment without requiring hands off the wheel. The most important step to facilitate such a scheme is to extract a highly discriminatory feature that can be used to separate and compute different EMG-based actions. The aim of this study is to accurately analyze EMG signals and classify finger movements that can be used to control in-car electronic equipment using a time– frequency distribution (TFD). The average root mean square voltage of seven participants and fourteen different finger movements are extracted as EMG features using a TFD. Four machine learning classifiers, i.e., support vector machine (SVM), decision tree, linear discriminant, and K-nearest neighbor (KNN), are used to classify pointing finger classes. The overall accuracy of the SVM precedes that of the other classifiers (89.3%), followed by decision tree (57.1%), linear discriminant (34.5%), and KNN (27.4%). The findings of this study are expected to be used in real-time applications that require both time and frequency information. Integrating the EMG signal to control in-car electronic equipment is expected to reduce the number of motor vehicle crashes globall

Impact of different lifting height and load mass on muscle performance using periodogram

Musculoskeletal disorders (MSDs) caused by muscle fatigue have been a major problem for industry which needs to be resolved to save costs related to human resource development (extra training and compensation). Detailed fatigue monitoring researches aimed at finding the best fatigue indices is not new although studies on the causes of fatigue can be explored further. Identification analysis is required to monitor the factors that influence muscle performance characteristic of surface electromyography (sEMG) signal. Periodogram monitoring technique applies a frequency domain signal and represents the distribution of the signal power over the frequency. It is a technique that allows the tracing of small changes in the behaviour of sEMG signal when external parameters are varied. This technique is used in this paper to monitor the sEMG signal changes in muscle performance when the lifting height and load mass are varied. The periodogram amplitude, which represents the power, increases with the rise in lifting height and load mass. From the frequency representation of the periodogram, the root mean square voltage (Vrms) is calculated where the muscle performance characteristic could be further identified. The Vrms also shows a similar trend when the lifting height and load mass are varied proving the periodogram technique is useful to monitor changes in the muscle performance during manual lifting

The relationship between trunk angle and electromyography (EMG) signals in biceps branchii and erector spinae muscles during core-lifting task

The core-lifting task is one of the functional capacity evaluations (FCEs) executed at the Social Security Organization rehabilitation center, Malaysia with the end goal to assess the fitness level of return-to-work patients. However, the current lifting task perform depends solely based on the instructor’s evaluation without knowing precisely the muscle condition of the patients. A research is currently done to design a pattern recognition based on electromyography signal to distinguish the muscle performance. It is also known that proper way of lifting does affect the EMG signal. Hence, this paper studies the relationship between the trunk angle and EMG signal. EMG signals from 7 subjects performing a total of 3 core-lifting task cycles were recorded using skin-surface electrodes located over the belly of right and left biceps branchii, and left and right erector spinae, while the subject’s motion was captured and analyzed using Venus 3D. Trunk angle was then calculated and compared with the electromyography signal. Results illustrate that there exists a relationship but not a distinguishable one between the trunk posture (mainly the trunk angle) and the electromyography signal of the erector spinae when the subjects performed core-lifting task by implementing squat lifting. Thus, the results conclude that the trunk angle can be ignored as long as the same squat lifting is applied. However, the results may vary if compared with stoop lifting, as the trunk angle is significantly different for both

Cuckoo Search Approach for Cutting Stock Problem

Cutting Stock Problem has been used in many industries like paper, glass, wood and etc. Cutting Stock Problem has helped industries to reduce trim loss and at the same time meets the customer’s requirement. The purpose of this paper is to develop a new approach which is Cuckoo Search Algorithm in Cutting Stock Problem. Cutting Stock Problem with Linear Programming based method has been improved down the years to the point that it reaches limitation that it cannot achieve a reasonable time in searching for solution. Therefore, many researchers have to turn to metaheuristic algorithms as a solution to the problem which also makes these algorithms become famous. Cuckoo Search Algorithm is selected because it is a new algorithm and outperforms many algorithms. Hence, this paper intends to experiment the performance of Cuckoo Search in Cutting Stock Problem