Features Extraction of Electromyography Signals in Time Domain on Biceps Brachii Muscle

Bukhari, W. M.; Daud, Wan Mohd Bukhari Wan; Horng, Chong Shin; Sudirman, Rubita

doi:10.7763/ijmo.2013.v3.332

Cited by 49 publications

(28 citation statements)

References 7 publications

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“…We obtained these features directly in the time domain without requiring signal transformation, well-known as time domain feature. During the past decade, the time domain feature has been widely used in engineering practices and researchers due to its easy, computational simplicity and quick implementation [50,51].…”

Section: Discussionmentioning

confidence: 99%

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

et al. 2018

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Abstract:Termites are the most destructive pests and their attacks significantly impact the quality of wooden buildings. Due to their cryptic behavior, it is rarely apparent from visual observation that a termite infestation is active and that wood damage is occurring. Based on the phenomenon of acoustic signals generated by termites when attacking wood, we proposed a practical framework to detect termites nondestructively, i.e., by using the acoustic signals extraction. This method has the pros to maintain the quality of wood products and prevent higher termite attacks. In this work, we inserted 220 subterranean termites into a pine wood for feeding activity and monitored its acoustic signal. The two acoustic features (i.e., energy and entropy) derived from the time domain were used for this study's analysis. Furthermore, the support vector machine (SVM) algorithm with different kernel functions (i.e., linear, radial basis function, sigmoid and polynomial) were employed to recognize the termites' acoustic signal. In addition, the area under a receiver operating characteristic curve (AUC) was also adopted to analyze and improve the performance results. Based on the numerical analysis, the SVM with polynomial kernel function achieves the best classification accuracy of 0.9188.

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Section: Discussionmentioning

confidence: 99%

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

et al. 2018

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“…Features in time domain have been widely used in medical and engineering practices and researches. Time domain features are used in signal classification due to its easy [29]. In this study, five time domain features methods widely used in the literature have been utilized to obtain the features of the EMG signal.…”

Section: Featured Extractions Of Emg Signalmentioning

confidence: 99%

“…The maximum value of the signal represents the largest of the sampled signal values in each packet divided by windows [29].…”

Section: Maximum Value Of Signalmentioning

confidence: 99%

“…This is done either by halfwave or full-wave rectification of the signal. A full-wave rectification method was applied to preserve the energy of the signal [25,[29][30][31][32][33][34], and the expression for the method is given in Eq. (1).…”

Section: Rectification Processmentioning

confidence: 99%

“…Briefly summarized, the study creates a bioelectrical database of the activities of the hand muscles and the interaction network between the human and prosthetic hand using this database and interface to design a simulator and develop a control algorithm. Analog-to-digital conversion is the process by which the amplitude of the analog signal voltage is represented by a number sequence at specific time points [29][30][31]. The EMG voltage signals used in this study are converted into a number sequence by sampling with a period of 0.001 s.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

EMG-Controlled Prosthetic Hand with Fuzzy Logic Classification Algorithm

Taşar¹,

Gülten²

2017

Modern Fuzzy Control Systems and Its Applications

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In recent years, researchers have conducted many studies on the design and control of prosthesis devices that take the place of a missing limb. Functional ability of prosthesis hands that mimic biological hand functions increases depending on the number of independent finger movements possible. From this perspective, in this study, six different finger movements were given to a prosthesis hand via bioelectrical signals, and the functionality of the prosthesis hand was increased. Bioelectrical signals were recorded by surface electromyography for four muscles with the help of surface electrodes. The recorded bioelectrical signals were subjected to a series of preprocessing and feature extraction processes. In order to create meaningful patterns of motion and an effective cognitive interaction network between the human and the prosthetic hand, fuzzy logic classification algorithms were developed. A five-fingered and 15-jointed prosthetic hand was designed via SolidWorks, and a prosthetic prototype was produced by a 3D printer. In addition, prosthetic hand simulator was designed in Matlab/SimMechanics. Pattern control of both the simulator and the prototype hand in real time was achieved. Position control of motors connected to each joint of the prosthetic hand was provided by a PID controller. Thus, an effective cognitive communication network established between the user, and the real-time pattern control of the prosthesis was provided by bioelectrical signals.

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Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms

Prathap

Kumara

Ragel

2018

Advances in Intelligent Systems and Computing

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Recognizing sEMG (Surface Electromyography) signals belonging to a particular action (e.g., lateral arm raise) automatically is a challenging task as EMG signals themselves have a lot of variation even for the same action due to several factors. To overcome this issue, there should be a proper separation which indicates similar patterns repetitively for a particular action in raw signals. A repetitive pattern is not always matched because the same action can be carried out with different time duration. Thus, a depth sensor (Kinect) was used for pattern identification where three joint angles were recording continuously which is clearly separable for a particular action while recording sEMG signals. To Segment out a repetitive pattern in angle data, MDTW (Moving Dynamic Time Warping) approach is introduced. This technique is allowed to retrieve suspected motion of interest from raw signals. MDTW based on DTW algorithm, but it will be moving through the whole dataset in a pre-defined manner which is capable of picking up almost all the suspected segments inside a given dataset an optimal way. Elevated bicep curl and lateral arm raise movements are taken as motions of interest to show how the proposed technique can be employed to achieve auto identification and labelling. The full implementation is available at https://github.com/GPrathap/OpenBCIPython

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Features Extraction of Electromyography Signals in Time Domain on Biceps Brachii Muscle

Abstract: Abstract-Electromyography

Cited by 49 publications

References 7 publications

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

A Comparison Study of Kernel Functions in the Support Vector Machine and Its Application for Termite Detection

EMG-Controlled Prosthetic Hand with Fuzzy Logic Classification Algorithm

Near Real-Time Data Labeling Using a Depth Sensor for EMG Based Prosthetic Arms

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