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Al Harrach, M., S. Boudaoud, M. Hassan, F. S. Ayachi, D. Gamet, J. F. Grosset, and F. Marin. "Denoising of HD-sEMG signals using canonical correlation analysis." Medical & Biological Engineering & Computing 55, no. 3 (May 25, 2016): 375–88. http://dx.doi.org/10.1007/s11517-016-1521-x.
Повний текст джерела
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Duan, Haiqiang, Chenyun Dai, and Wei Chen. "The Evaluation of Classifier Performance during Fitting Wrist and Finger Movement Task Based on Forearm HD-sEMG." Mathematical Problems in Engineering 2022 (March 28, 2022): 1–12. http://dx.doi.org/10.1155/2022/9594521.
Повний текст джерелаАнотація:
The transmission of human body movement signals to other devices through wearable smart bracelets has attracted increasing attention in the field of human-machine interfaces. However, owing to the limited data collection range of wearable bracelets, it is necessary to study the relationship between the superposition of the wrist and fingers and their cooperative motions to simplify the data collection system of such devices. Multichannel high-density surface electromyogram (HD-sEMG) signals exhibit high spatial resolutions, and they can help improve the accuracy of the multichannel fitting. In this study, we quantified the HD-sEMG forearm spatial activation features of 256 channels of hand movement and performed a linear fitting of the data obtained for finger and wrist movements in order to verify the linear superposition relationship between the cooperative and independent movements of the wrist and fingers. This study aims to classify and predict the results of the fitting and measured fingers and wrist cooperative actions using four commonly adopted classifiers and evaluate the performance of the classifiers in gesture fitting. The results indicated that linear discriminant analysis affords the highest classification performance, whereas the random forest method achieved the worst performance. This study can serve as a guide for gesture signal simplification in the future.
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Veer, Karan. "Spectral and mathematical evaluation of electromyography signals for clinical use." International Journal of Biomathematics 09, no. 06 (August 2, 2016): 1650094. http://dx.doi.org/10.1142/s1793524516500947.
Повний текст джерелаАнотація:
The surface electromyography (SEMG) is a complicated biomedical signal, generated during voluntary or involuntary muscle activities and these muscle activities are always controlled by the nervous system. In this paper, the processing and analysis of SEMG signals at multiple muscle points for different operations were carried out. Myoelectric signals were detected using designed acquisition setup which consists of an instrumentation amplifier, filter circuit, an amplifier with gain adjustment. Further, Labview[Formula: see text]-based data programming code was used to record SEMG signals for independent activities. The whole system consists of bipolar noninvasive electrodes, signal acquisition protocols and signal conditioning at different levels. This work uses recorded SEMG signals generated by biceps and triceps muscles for four different arm activities. Feature extraction was done on the recorded signal for investigating the voluntary muscular contraction relationship for exercising statistic measured index method to evaluate distance between two independent groups by directly addressing the quality of signal in separability class for different arm movements. Thereafter repeated factorial analysis of variance technique was implemented to evaluate the effectiveness of processed signal. From these results, it demonstrates that the proposed method can be used as SEMG feature evaluation index.
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Zhang, Yanyan, Gang Wang, Chaolin Teng, Zhongjiang Sun, and Jue Wang. "The Analysis of Hand Movement Distinction Based on Relative Frequency Band Energy Method." BioMed Research International 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/781769.
Повний текст джерелаАнотація:
For the purpose of successfully developing a prosthetic control system, many attempts have been made to improve the classification accuracy of surface electromyographic (SEMG) signals. Nevertheless, the effective feature extraction is still a paramount challenge for the classification of SEMG signals. The relative frequency band energy (RFBE) method based on wavelet packet decomposition was proposed for the prosthetic pattern recognition of multichannel SEMG signals. Firstly, the wavelet packet energy of SEMG signals in each subspace was calculated by using wavelet packet decomposition and the RFBE of each frequency band was obtained by the wavelet packet energy. Then, the principal component analysis (PCA) and the Davies-Bouldin (DB) index were used to perform the feature selection. Lastly, the support vector machine (SVM) was applied for the classification of SEMG signals. Our results demonstrated that the RFBE approach was suitable for identifying different types of forearm movements. By comparing with other classification methods, the proposed method achieved higher classification accuracy in terms of the classification of SEMG signals.
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Wang, Gang, Yanyan Zhang, and Jue Wang. "The Analysis of Surface EMG Signals with the Wavelet-Based Correlation Dimension Method." Computational and Mathematical Methods in Medicine 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/284308.
Повний текст джерелаАнотація:
Many attempts have been made to effectively improve a prosthetic system controlled by the classification of surface electromyographic (SEMG) signals. Recently, the development of methodologies to extract the effective features still remains a primary challenge. Previous studies have demonstrated that the SEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time series analysis and the time-frequency domain methods, we proposed the wavelet-based correlation dimension method to extract the effective features of SEMG signals. The SEMG signals were firstly analyzed by the wavelet transform and the correlation dimension was calculated to obtain the features of the SEMG signals. Then, these features were used as the input vectors of a Gustafson-Kessel clustering classifier to discriminate four types of forearm movements. Our results showed that there are four separate clusters corresponding to different forearm movements at the third resolution level and the resulting classification accuracy was 100%, when two channels of SEMG signals were used. This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of SEMG signals and is suitable for classifying different types of forearm movements. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy.
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Herrera, Efrén V., Edgar M. Vela, Victor A. Arce, Katherine G. Molina, Nathaly S. Sánchez, Paúl J. Daza, Luis E. Herrera, and Douglas A. Plaza. "Temperature Influences at the Myoelectric Level in the Upper Extremities of the Human Body." Open Biomedical Engineering Journal 14, no. 1 (October 23, 2020): 28–42. http://dx.doi.org/10.2174/1874120702014010028.
Повний текст джерелаАнотація:
Objective: Nowadays, surface electromyography (sEMG) signals are used for a variety of medical interaction applications along with hardware and software interfaces. These signals require advanced techniques with different approaches that enable processing the sEMG signals acquired in the upper limb muscles of a person. Methods: The purpose of this article is to analyze the sEMG signals of the upper limb of a person exposed to temperature changes to envisage its behavior and its nature. The anticipated diagnostic is a key factor in the health field. Therefore, it is very important to develop more precise methods and techniques. For the present study, a heat chamber that allows controlling the temperature of the area where the patient rests his or her hand was designed and implemented. With the appropriate hardware interfaces, the sEMG signals of the hand were registered with MatLab/Simulink software for further analysis. The article explains the analysis and develops knowledge, through a probabilistic approach regarding the change in the sEMG signals. Results: The results show that there is an activity in the sEMG signal response due to changes in temperature and it is feasible to detect them using the proposed method. Conclusion: This finding contributes to research that seeks to characterize temperature’s effect in the biomedical field.
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Shahbakhti, Mohammad, Elnaz Heydari, and Gia Thien Luu. "Segmentation of ECG from Surface EMG Using DWT and EMD: A Comparison Study." Fluctuation and Noise Letters 13, no. 04 (October 20, 2014): 1450030. http://dx.doi.org/10.1142/s0219477514500308.
Повний текст джерелаАнотація:
The electrocardiographic (ECG) signal is a major artifact during recording the surface electromyography (SEMG). Removal of this artifact is one of the important tasks before SEMG analysis for biomedical goals. In this paper, the application of discrete wavelet transform (DWT) and empirical mode decomposition (EMD) for elimination of ECG artifact from SEMG is investigated. The focus of this research is to reach the optimized number of decomposed levels using mean power frequency (MPF) by both techniques. In order to implement the proposed methods, ten simulated and three real ECG contaminated SEMG signals have been tested. Signal-to-noise ratio (SNR) and mean square error (MSE) between the filtered and the pure signals are applied as the performance indexes of this research. The obtained results suggest both techniques could remove ECG artifact from SEMG signals fair enough, however, DWT performs much better and faster in real data.
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Hari, Lakshmi M., Gopinath Venugopal, and Swaminathan Ramakrishnan. "Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 236, no. 2 (October 11, 2021): 208–17. http://dx.doi.org/10.1177/09544119211048011.
Повний текст джерелаАнотація:
In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.
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Naik, Ganesh R., Dinesh K. Kumar, Sridhar P. Arjunan, and Marimuthu Palaniswami. "INDEPENDENT COMPONENT APPROACH TO THE ANALYSIS OF HAND GESTURE sEMG AND FACIAL sEMG." Biomedical Engineering: Applications, Basis and Communications 20, no. 02 (April 2008): 83–93. http://dx.doi.org/10.4015/s1016237208000647.
Повний текст джерелаАнотація:
Independent component analysis algorithm, a recently developed multivariate statistical data analysis technique, has been successfully used for signal extraction in the field of biomedical and statistical signal processing. This paper reviews the concept of ICA and demonstrates its usefulness and limitations in the context of surface electromyogram related to hand movements and facial muscles. In the first experiment, ICA has been used to separate the electrical activity from different hand gestures. The second part of our study considers separating electrical activity from facial muscles. In both instances, surface electromyogram has been used as an indicator of muscle activity. The theoretical analysis and experimental results demonstrate that ICA is suitable for the identification of different hand gestures using sEMG signals. The results identify the unsuitability of ICA when the similar techniques are used for the facial muscles in order to perform different vowel classification. This technique could be used as a prerequisite tool to measure the reliability of sEMG based systems in rehabilitations and human computer interaction applications.
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Lersviriyanantakul, Chaiwat, Apidet Booranawong, Kiattisak Sengchuai, Pornchai Phukpattaranont, Booncharoen Wongkittisuksa, and Nattha Jindapetch. "Implementation of a real-time automatic onset time detection for surface electromyography measurement systems using NI myRIO." Thermal Science 20, suppl. 2 (2016): 591–602. http://dx.doi.org/10.2298/tsci150929041l.
Повний текст джерелаАнотація:
For using surface electromyography (sEMG) in various applications, the process consists of three parts: an onset time detection for detecting the first point of movement signals, a feature extraction for extracting the signal attribution, and a feature classification for classifying the sEMG signals. The first and the most significant part that influences the accuracy of other parts is the onset time detection, particularly for automatic systems. In this paper, an automatic and simple algorithm for the real-time onset time detection is presented. There are two main processes in the proposed algorithm; a smoothing process for reducing the noise of the measured sEMG signals and an automatic threshold calculation process for determining the onset time. The results from the algorithm analysis demonstrate the performance of the proposed algorithm to detect the sEMG onset time in various smoothing-threshold equations. Our findings reveal that using a simple square integral (SSI) as the smoothing-threshold equation with the given sEMG signals gives the best performance for the onset time detection. Additionally, our proposed algorithm is also implemented on a real hardware platform, namely NI myRIO. Using the real-time simulated sEMG data, the experimental results guarantee that the proposed algorithm can properly detect the onset time in the real-time manner.
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Mendes Junior, José Jair Alves, Melissa La Banca Freitas, Daniel Prado Campos, Felipe Adalberto Farinelli, Sergio Luiz Stevan, and Sérgio Francisco Pichorim. "Analysis of Influence of Segmentation, Features, and Classification in sEMG Processing: A Case Study of Recognition of Brazilian Sign Language Alphabet." Sensors 20, no. 16 (August 5, 2020): 4359. http://dx.doi.org/10.3390/s20164359.
Повний текст джерелаАнотація:
Sign Language recognition systems aid communication among deaf people, hearing impaired people, and speakers. One of the types of signals that has seen increased studies and that can be used as input for these systems is surface electromyography (sEMG). This work presents the recognition of a set of alphabet gestures from Brazilian Sign Language (Libras) using sEMG acquired from an armband. Only sEMG signals were used as input. Signals from 12 subjects were acquired using a MyoTM armband for the 26 signs of the Libras alphabet. Additionally, as the sEMG has several signal processing parameters, the influence of segmentation, feature extraction, and classification was considered at each step of the pattern recognition. In segmentation, window length and the presence of four levels of overlap rates were analyzed, as well as the contribution of each feature, the literature feature sets, and new feature sets proposed for different classifiers. We found that the overlap rate had a high influence on this task. Accuracies in the order of 99% were achieved for the following factors: segments of 1.75 s with a 12.5% overlap rate; the proposed set of four features; and random forest (RF) classifiers.
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Hossen, A., G. Deuschl, S. Groppa, U. Heute, and M. Muthuraman. "Discrimination of physiological tremor from pathological tremor using accelerometer and surface EMG signals." Technology and Health Care 28, no. 5 (September 18, 2020): 461–76. http://dx.doi.org/10.3233/thc-191947.
Повний текст джерелаАнотація:
BACKGROUND AND OBJECTIVE: Although careful clinical examination and medical history are the most important steps towards a diagnostic separation between different tremors, the electro-physiological analysis of the tremor using accelerometry and electromyography (EMG) of the affected limbs are promising tools. METHODS: A soft-decision wavelet-based decomposition technique is applied with 8 decomposition stages to estimate the power spectral density of accelerometer and surface EMG signals (sEMG) sampled at 800 Hz. A discrimination factor between physiological tremor (PH) and pathological tremor, namely, essential tremor (ET) and the tremor caused by Parkinson’s disease (PD), is obtained by summing the power entropy in band 6 (B6: 7.8125–9.375 Hz) and band 11 (B11: 15.625–17.1875 Hz). RESULTS: A discrimination accuracy of 93.87% is obtained between the PH group and the ET & PD group using a voting between three results obtained from the accelerometer signal and two sEMG signals. CONCLUSION: Biomedical signal processing techniques based on high resolution wavelet spectral analysis of accelerometer and sEMG signals are implemented to efficiently perform classification between physiological tremor and pathological tremor.
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Wang, Junhong, Lipeng Wang, Xugang Xi, Seyed M. Miran, and Anke Xue. "Estimation and Correlation Analysis of Lower Limb Joint Angles Based on Surface Electromyography." Electronics 9, no. 4 (March 26, 2020): 556. http://dx.doi.org/10.3390/electronics9040556.
Повний текст джерелаАнотація:
Many people lose their motor function because of spinal cord injury or stroke. This work studies the patient’s continuous movement intention of joint angles based on surface electromyography (sEMG), which will be used for rehabilitation. In this study, we introduced a new sEMG feature extraction method based on wavelet packet decomposition, built a prediction model based on the extreme learning machine (ELM) and analyzed the correlation between sEMG signals and joint angles based on the detrended cross-correlation analysis. Twelve individuals participated in rehabilitation tasks, to test the performance of the proposed method. Five channels of sEMG signals were recorded, and denoised by the empirical mode decomposition. The prediction accuracy of the wavelet packet feature-based ELM prediction model was found to be 96.23% ± 2.36%. The experimental results clearly indicate that the wavelet packet feature and ELM is a better combination to build a prediction model.
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Campanini, Isabella, Andrea Merlo, Catherine Disselhorst-Klug, Luca Mesin, Silvia Muceli, and Roberto Merletti. "Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors." Sensors 22, no. 11 (May 30, 2022): 4150. http://dx.doi.org/10.3390/s22114150.
Повний текст джерелаАнотація:
Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG.
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Veer, Karan, and Tanu Sharma. "Electromyographic classification of effort in muscle strength assessment." Biomedical Engineering / Biomedizinische Technik 63, no. 2 (March 28, 2018): 131–37. http://dx.doi.org/10.1515/bmt-2016-0038.
Повний текст джерелаАнотація:
AbstractDual-channel evaluation of surface electromyogram (SEMG) signals acquired from amputee subjects using computational techniques for classification of arm motions is presented in this study. SEMG signals were classified by the neural network (NN) and interpretation was done using statistical techniques to extract the effectiveness of the recorded signals. From the results, it was observed that there exists a calculative difference in amplitude gain across different motions and that SEMG signals have great potential to classify arm motions. The outcomes indicated that the NN algorithm performs significantly better than other algorithms, with a classification rate (CR) of 96.40%. Analysis of variance (ANOVA) presents the results to validate the effectiveness of the recorded data to discriminate SEMG signals. The results are of significant thrust in identifying the operations that can be implemented for classifying upper-limb movements suitable for prostheses’ design.
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KARTHICK, P. A., G. VENUGOPAL, and S. RAMAKRISHNAN. "ANALYSIS OF SURFACE EMG SIGNALS UNDER FATIGUE AND NON-FATIGUE CONDITIONS USING B-DISTRIBUTION BASED QUADRATIC TIME FREQUENCY DISTRIBUTION." Journal of Mechanics in Medicine and Biology 15, no. 02 (April 2015): 1540028. http://dx.doi.org/10.1142/s021951941540028x.
Повний текст джерелаАнотація:
In this paper, an attempt has been made to analyze surface electromyography (sEMG) signals under non-fatigue and fatigue conditions using time-frequency based features. The sEMG signals are recorded from biceps brachii muscle of 50 healthy volunteers under well-defined protocol. The pre-processed signals are divided into six equal epochs. The first and last segments are considered as non-fatigue and fatigue zones respectively. Further, these signals are subjected to B-distribution based quadratic time-frequency distribution (TFD). Time frequency based features such as instantaneous median frequency (IMDF) and instantaneous mean frequency (IMNF) are extracted. The expression of spectral entropy is modified to obtain instantaneous spectral entropy (ISPEn) from the time-frequency spectrum. The results show that all the extracted features are distinct in both conditions. It is also observed that the values of all features are higher in non-fatigue zone compared to fatigue condition. It appears that this method is useful in analysing various neuromuscular conditions using sEMG signals.
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Lim, Feraldo, Eka Budiarto, and Rusman Rusyadi. "Comparison of Hand Gesture Classification from Surface Electromyography Signal between Artificial Neural Network and Principal Component Analysis." ICONIET PROCEEDING 2, no. 3 (February 13, 2019): 177–83. http://dx.doi.org/10.33555/iconiet.v2i3.30.
Повний текст джерелаАнотація:
The goal of this research is to detect Surface Electromyography (SEMG) signal froma person’s arm using Myo Armband and classify his / her performed finger ges-tures based onthe corresponding signal. Artificial Neural Network (based on the machine learning approach)and Principal Component Analysis (based on the feature extraction approach) with and withoutFast Fourier Transform (FFT) were selected as the methods utilized in this research. Analysisresults show that ANN has achieved 62.14% gesture classifying accuracy, while PCA withoutFFT has achieved 30.43% and PCA without FFT has achieved 48.15% accuracy. The threeclassifiers are tested using SEMG data from a set of six recorded custom gestures. Thecomparison results show that the ANN classifier shows higher classifying accuracy and morerobust rather than the PCA classifier’s classi-fying accuracy. Therefore, ANN classifier is moresuited to be implemented in classifying SEMG signals as hand gestures.
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Toledo-Peral, Cinthya Lourdes, Josefina Gutiérrez-Martínez, Jorge Airy Mercado-Gutiérrez, Ana Isabel Martín-Vignon-Whaley, Arturo Vera-Hernández, and Lorenzo Leija-Salas. "sEMG Signal Acquisition Strategy towards Hand FES Control." Journal of Healthcare Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/2350834.
Повний текст джерелаАнотація:
Due to damage of the nervous system, patients experience impediments in their daily life: severe fatigue, tremor or impaired hand dexterity, hemiparesis, or hemiplegia. Surface electromyography (sEMG) signal analysis is used to identify motion; however, standardization of electrode placement and classification of sEMG patterns are major challenges. This paper describes a technique used to acquire sEMG signals for five hand motion patterns from six able-bodied subjects using an array of recording and stimulation electrodes placed on the forearm and its effects over functional electrical stimulation (FES) and volitional sEMG combinations, in order to eventually control a sEMG-driven FES neuroprosthesis for upper limb rehabilitation. A two-part protocol was performed. First, personalized templates to place eight sEMG bipolar channels were designed; with these data, a universal template, called forearm electrode set (FELT), was built. Second, volitional and evoked movements were recorded during FES application. 95% classification accuracy was achieved using two sessions per movement. With the FELT, it was possible to perform FES and sEMG recordings simultaneously. Also, it was possible to extract the volitional and evoked sEMG from the raw signal, which is highly important for closed-loop FES control.
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Veer, Karan, and Renu Vig. "Identification and classification of upper limb motions using PCA." Biomedical Engineering / Biomedizinische Technik 63, no. 2 (March 28, 2018): 191–96. http://dx.doi.org/10.1515/bmt-2016-0224.
Повний текст джерелаАнотація:
Abstract:This paper describes the utility of principal component analysis (PCA) in classifying upper limb signals. PCA is a powerful tool for analyzing data of high dimension. Here, two different input strategies were explored. The first method uses upper arm dual-position-based myoelectric signal acquisition and the other solely uses PCA for classifying surface electromyogram (SEMG) signals. SEMG data from the biceps and the triceps brachii muscles and four independent muscle activities of the upper arm were measured in seven subjects (total dataset=56). The datasets used for the analysis are rotated by class-specific principal component matrices to decorrelate the measured data prior to feature extraction.
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Feng, Yongfei, Mingwei Zhong, Xusheng Wang, Hao Lu, Hongbo Wang, Pengcheng Liu, and Luige Vladareanu. "Active triggering control of pneumatic rehabilitation gloves based on surface electromyography sensors." PeerJ Computer Science 7 (April 19, 2021): e448. http://dx.doi.org/10.7717/peerj-cs.448.
Повний текст джерелаАнотація:
The portable and inexpensive hand rehabilitation robot has become a practical rehabilitation device for patients with hand dysfunction. A pneumatic rehabilitation glove with an active trigger control system is proposed, which is based on surface electromyography (sEMG) signals. It can trigger the hand movement based on the patient’s hand movement trend, which may improve the enthusiasm and efficiency of patient training. Firstly, analysis of sEMG sensor installation position on human’s arm and signal acquisition process were carried out. Then, according to the statistical law, three optimal eigenvalues of sEMG signals were selected as the follow-up neural network classification input. Using the back propagation (BP) neural network, the classifier of hand movement is established. Moreover, the mapping relationship between hand sEMG signals and hand actions is built by training and testing. Different patients choose the same optimal eigenvalues, and the calculation formula of eigenvalues’ amplitude is unique. Due to the differences among individuals, the weights and thresholds of each node in the BP neural network model corresponding to different patients are not the same. Therefore, the BP neural network model library is established, and the corresponding network is called for operation when different patients are trained. Finally, based on sEMG signal trigger, the pneumatic glove training control algorithm was proposed. The combination of the trigger signal waveform and the motion signal waveform indicates that the pneumatic rehabilitation glove is triggered to drive the patient’s hand movement. Preliminary tests have confirmed that the accuracy rate of trend recognition for hand movement is about 90%. In the future, clinical trials of patients will be conducted to prove the effectiveness of this system.
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DJUWARI, DJUWARI, DINESH K. KUMAR, SRIDHAR P. ARJUNAN, and GANESH R. NAIK. "LIMITATIONS AND APPLICATIONS OF ICA FOR SURFACE ELECTROMYOGRAM FOR IDENTIFYING HAND GESTURES." International Journal of Computational Intelligence and Applications 07, no. 03 (September 2008): 281–300. http://dx.doi.org/10.1142/s1469026808002272.
Повний текст джерелаАнотація:
Surface electromyogram (SEMG) has numerous applications, but the presence of artifacts and cross talk especially at low level of muscle activity makes the recordings unreliable. Spectral and temporal overlap can make the removal of artifacts and noise, or separation of relevant signals from other bioelectric signals extremely difficult. Identification of hand gestures using low level of SEMG is one application that has a number of applications but the presence of high level of cross talk makes such an application highly unreliable. Individual muscles may be considered as independent at the local level and this makes an argument for separating the signals using independent component analysis (ICA). In the recent past, due to the easy availability of ICA tools, a number of researchers have attempted to use ICA for this application. This paper reports research conducted to evaluate the use of ICA for the separation of muscle activity and removal of the artifacts from SEMG. It discusses some of the conditions that could affect the reliability of the separation and evaluates issues related to the properties of the signals and a number of sources. This paper also identifies the lack of suitable measure of quality of separation for bioelectric signals and it recommends and tests a more robust measure of separation. This paper also proposes semi-blind ICA approach with the combination of prior knowledge of SEMG sources with ICA to identify hand gestures using low level of SEMG recordings. The theoretical analysis and experimental results demonstrate that ICA is suitable for SEMG signals. The results demonstrate the limitations of such applications due to the inability of the system to identify the correct order and magnitude of the signals. This paper determines the suitability of the use of error between estimated and actual mixing matrix as a mean for identifying the quality of separation of the output. This work also demonstrates that semi-blind ICA can accurately identify complex hand gestures from the low-level SEMG recordings.
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Deng, Yanxia, Farong Gao, and Huihui Chen. "Angle Estimation for Knee Joint Movement Based on PCA-RELM Algorithm." Symmetry 12, no. 1 (January 8, 2020): 130. http://dx.doi.org/10.3390/sym12010130.
Повний текст джерелаАнотація:
Surface electromyogram (sEMG) signals are easy to record and offer valuable motion information, such as symmetric and periodic motion in human gait. Due to these characteristics, sEMG is widely used in human-computer interaction, clinical diagnosis and rehabilitation medicine, sports medicine and other fields. This paper aims to improve the estimation accuracy and real-time performance, in the case of the knee joint angle in the lower limb, using a sEMG signal, in a proposed estimation algorithm of the continuous motion, based on the principal component analysis (PCA) and the regularized extreme learning machine (RELM). First, the sEMG signals, collected during the lower limb motion, are preprocessed, while feature samples are extracted from the acquired and preconditioned sEMG signals. Next, the feature samples dimensions are reduced by the PCA, as well as the knee joint angle system is measured by the three-dimensional motion capture system, are followed by the normalization of the feature variable value. The normalized sEMG feature is used as the input layer, in the RELM model, while the joint angle is used as the output layer. After training, the RELM model estimates the knee joint angle of the lower limbs, while it uses the root mean square error (RMSE), Pearson correlation coefficient and model training time as key performance indicators (KPIs), to be further discussed. The RELM, the traditional BP neural network and the support vector machine (SVM) estimation results are compared. The conclusions prove that the RELM method, not only has ensured the validity of results, but also has greatly reduced the learning train time. The presented work is a valuable point of reference for further study of the motion estimation in lower limb.
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Sangaboina, Swathi. "IOT Enabled Wearable Gloves with SEMG Subsystem with Posture Analysis." International Journal for Research in Applied Science and Engineering Technology 9, no. 9 (September 30, 2021): 1690–95. http://dx.doi.org/10.22214/ijraset.2021.38236.
Повний текст джерелаАнотація:
Abstract: Electromyogram (EMG) is a technique to track the record , analyze and estimate the electrical activity produced by muscles. This technique is used to detect the muscle issues that harm the nerves activity , muscle tissues and identify the location where they are joined together . This paper discusses the implementation of a project which can be considered as a tool for the acquisition of muscle activity, presentation and real-time attainment of EMG signal using a specific EMG sensor. The live EMG reading is recorded using the Wi-Fi- enabled Raspberrypi and then sent to a remote server in our case ThingSpeak server with the help of IoT concepts which helps in the telemetry of the obtained biomedical signals using the cloud. Results are displayed in ThingSpeak. The live recordings are also obtained on the PC using the serial plotter. This project can also help us in monitor and observe the progress of the patient treatment even if the physiotherapist could not come and data can be directly sent to them. Thus, the project aims to develop an EMG monitoring device based on IoT, for analyzing and acquiring EMG signals. Keywords: EMG sensor, Raspberry pi, LCD, ADS1115
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Arozi, Moh, Wahyu Caesarendra, Mochammad Ariyanto, M. Munadi, Joga D. Setiawan, and Adam Glowacz. "Pattern Recognition of Single-Channel sEMG Signal Using PCA and ANN Method to Classify Nine Hand Movements." Symmetry 12, no. 4 (April 3, 2020): 541. http://dx.doi.org/10.3390/sym12040541.
Повний текст джерелаАнотація:
A number of researchers prefer using multi-channel surface electromyography (sEMG) pattern recognition in hand gesture recognition to increase classification accuracy. Using this method can lead to computational complexity. Hand gesture classification by employing single channel sEMG signal acquisition is quite challenging, especially for low-rate sampling frequency. In this paper, a study on the pattern recognition method for sEMG signals of nine finger movements is presented. Common surface single channel electromyography (sEMG) was used to measure five different subjects with no neurological or muscular disorder by having nine hand movements. This research had several sequential processes (i.e., feature extraction, feature reduction, and feature classification). Sixteen time-domain features were employed for feature extraction. The features were then reduced using principal component analysis (PCA) into two and three-dimensional feature space. The artificial neural network (ANN) classifier was tested on two different feature sets: (1) using all principal components obtained from PCA (PC1–PC3) and (2) using selected principal components (PC2 and PC3). The third best principal components were then used for classification using ANN. The average accuracy using all subject signals was 86.7% to discriminate the nine finger movements.
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Lv, Ying, Qingli Zheng, Xiubin Chen, Yi Jia, Chunsheng Hou, and Meiwen An. "Analysis on Muscle Forces of Extrinsic Finger Flexors and Extensors in Flexor Movements with sEMG and Ultrasound." Mathematical Problems in Engineering 2022 (May 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/7894935.
Повний текст джерелаАнотація:
The coupling relationship between surface electromyography (sEMG) signals and muscle forces or joint moments is the basis for sEMG applications in medicine, rehabilitation, and sports. The solution of muscle forces is the key issue. sEMG and Muscle-Tendon Junction (MTJ) displacements of the flexor digitorum superficialis (FDS), flexor digitorum profundus (FDP), and extensor digitorum (ED) were measured during five sets of finger flexion movements. Meanwhile, the muscle forces of FDS, FDP, and ED were calculated by the Finite Element Digital Human Hand Model (FE-DHHM) driven by MTJ displacements. The results showed that, in the initial position of the flexion without resistance, the high-intensity contraction of the ED kept the palm straight and the FDS was involved. The sEMG-force relationship of FDS was linear during the flexion with resistance, while FDP showed a larger sEMG amplitude than FDS, with no obvious linearity with its muscle forces. sEMG-MTJ displacement relationships for FDS and FDP were consistent with the trend of their own sEMG-force relationships. sEMG of ED decreased and then increased during the flexion with resistance, with no obvious linear relationship with muscle forces. The analysis of the proportion of muscle force and integrated EMG (iEMG) reflected the different activation patterns of FDS and ED.
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Kahl, Lorenz, and Ulrich Hofmann. "Removal of ECG Artifacts Affects Respiratory Muscle Fatigue Detection—A Simulation Study." Sensors 21, no. 16 (August 23, 2021): 5663. http://dx.doi.org/10.3390/s21165663.
Повний текст джерелаАнотація:
This work investigates elimination methods for cardiogenic artifacts in respiratory surface electromyographic (sEMG) signals and compares their performance with respect to subsequent fatigue detection with different fatigue algorithms. The analysis is based on artificially constructed test signals featuring a clearly defined expected fatigue level. Test signals are additively constructed with different proportions from sEMG and electrocardiographic (ECG) signals. Cardiogenic artifacts are eliminated by high-pass filtering (HP), template subtraction (TS), a newly introduced two-step approach (TSWD) consisting of template subtraction and a wavelet-based damping step and a pure wavelet-based damping (DSO). Each method is additionally combined with the exclusion of QRS segments (gating). Fatigue is subsequently quantified with mean frequency (MNF), spectral moments ratio of order five (SMR5) and fuzzy approximate entropy (fApEn). Different combinations of artifact elimination methods and fatigue detection algorithms are tested with respect to their ability to deliver invariant results despite increasing ECG contamination. Both DSO and TSWD artifact elimination methods displayed promising results regarding the intermediate, “cleaned” EMG signal. However, only the TSWD method enabled superior results in the subsequent fatigue detection across different levels of artifact contamination and evaluation criteria. SMR5 could be determined as the best fatigue detection algorithm. This study proposes a signal processing chain to determine neuromuscular fatigue despite the presence of cardiogenic artifacts. The results furthermore underline the importance of selecting a combination of algorithms that play well together to remove cardiogenic artifacts and to detect fatigue. This investigation provides guidance for clinical studies to select optimal signal processing to detect fatigue from respiratory sEMG signals.
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Martinot, J., N. Le-Dong, V. Cuthbert, S. Denison, D. Gozal, and J. Pepin. "0792 Mandibular Movement Monitoring with Artificial Intelligence Analysis for the Diagnosis of Sleep Bruxism." Sleep 43, Supplement_1 (April 2020): A301—A302. http://dx.doi.org/10.1093/sleep/zsaa056.788.
Повний текст джерелаАнотація:
Abstract Introduction Sleep bruxism (BXM) is the result of rhythmic muscular masticatory activity (RMMA) and can be captured by masseters surface electromyography (sEMG). Despite the multiple adverse negative consequences of BXM, a simple reliable home diagnostic device is currently unavailable, with in laboratory audio-video polysomnography (type I PSG) remaining the gold standard diagnostic tool. Mandibular movements (MM) recordings during sleep can readily identify RMMA, are simple to set up and can be easily repeated from night to night. Here, we aimed to identify stereotypical MM in patients with BXM, and to develop RMMA automatic detection and BXM diagnosis using an artificial intelligence-based approach. Methods MM were recorded by a dedicated sensor (Sunrise, Namur, Belgium) in 12 patients with BXM during type I PSG. The Sunrise system consists of a coin-sized hardware that is comfortably placed on the subject’s chin. Its embedded inertial measurement unit communicates via Bluetooth with a smartphone and automatically transfers MM signals to a cloud-based infrastructure at the end of the night. Data processing and analysis are then performed in Python programming language. A time series cluster analysis was applied to sequences of masseters sEMG and MM signals during BXM episodes (n=300) and during spontaneous micro-arousals (n=300). Then, a convolutional neuronal network (CNN) was developed to identify BXM and distinguish it from spontaneous micro-arousals while exclusively relying on MM signal. Results Based on the cluster analysis, BXM periods were characterized by a specific pattern of MM signals (higher frequency and amplitude), which was closely associated with the sEMG signals but clearly differed from the MM signal patterns during micro-arousals. CNN-based classifier distinguished the BXM events from other RMMAs during micro-arousals and respiratory efforts with an overall accuracy of 91%. Conclusion Sleep bruxism can be automatically identified, quantified, and characterized with mandibular movements analysis supported by artificial intelligence technology. Support This work was supported by the French National Research Agency (ANR-12-TECS-0010), in the framework of the “Investissements d’avenir” program (ANR-15-IDEX-02). https://life.univ-grenoble-alpes.fr.
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Ostojic, Mirko, Milan Milosavljevic, Aleksandra Kovacevic, Miodrag Stokic, Djordje Stefanovic, Gordana Mandic-Gajic, and Ljiljana Jelicic. "Changes in power of surface electromyogram during breath-holding." Srpski arhiv za celokupno lekarstvo 148, no. 7-8 (2020): 440–46. http://dx.doi.org/10.2298/sarh191118037o.
Повний текст джерелаАнотація:
Introduction/Objective. Numerous studies on surface electromyographic (sEMG) signals in response to different respiratory parameters, particularly on sternocleidomastoid (SCM) muscles and diaphragm (DIA), indicated the promising advantages of their simultaneous monitoring with possible applications in the analysis of their correlation. This motivated ? detailed statistical analysis of the average power (PAV) on sEMG signals during prolonged breath-holding, simultaneously measured in the SCM and DIA areas. Methods. The physiological breath-holding method was applied to 30 healthy volunteers, with sEMG of SCM and DIA regions measured before, during, and after the breath-holding exercise. All the subjects were sitting in an upward position, with nostrils closed by the right index finger and thumb during breath-hold. To synchronize the records, the user would press a special switch using the other hand at the beginning and at the end of breath-holding experiment. The average power of sEMG (PAV) was measured for each 500 ms signal window. Results. The PAV remains constant before and 3 seconds after the exercise. During the ending of breathholding, at least one region had the PAV afflux of a minimum of 91%. Student?s t-test between SCM signals shows a significant difference of p < 0.001, while the DIA lacks it. Although the results showed that SCM is the dominant region in 76.67% of cases, the exclusive PAV afflux in the DIA region was detected in precisely five cases (16.67% of the total namber of participants). Conclusions. Our research concludes that there is the necessity of simultaneous measurement of SCM and DIA to observe dominant changes in sEMG during breath-holding. The physiological response of the respiratory center can be observed by approximately doubling PAV in one of SCM or DIA regions.
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Hernandez, Luis, and Clayton Camic. "Fatigue-Mediated Loss of Complexity is Contraction-Type Dependent in Vastus Lateralis Electromyographic Signals." Sports 7, no. 4 (April 2, 2019): 78. http://dx.doi.org/10.3390/sports7040078.
Повний текст джерелаАнотація:
The purpose of this study was to investigate the effect of fatigue status and contraction type on complexity of the surface electromyographic (sEMG) signal. Twelve females (mean age ± SD = 21.1 ± 1.4 years) performed three fatigue-inducing protocols that involved maximal concentric, eccentric, or isometric knee-extensor contractions over three non-consecutive sessions. Pre- and post-fatigue assessments were also completed each session and consisted of three maximal efforts for each type of contraction. Complexity of sEMG signals from the vastus lateralis was assessed using Sample Entropy (SampEn) and Detrended Fluctuation Analysis (DFA) as expressed using the scaling exponent α. The results showed that fatigue decreased (p < 0.05) sEMG complexity as indicated by decreased SampEn (non-fatigued: 1.57 ± 0.22 > fatigued: 1.46 ± 0.25) and increased DFA α (non-fatigued: 1.27 ± 0.26 < fatigued: 1.32 ± 0.23). In addition, sEMG complexity was different among contraction types as indicated by SampEn (concentric: 1.58 ± 0.22 > eccentric: 1.47 ± 0.27 and isometric: 1.50 ± 0.21) and DFA α (concentric: 1.27 ± 0.18 < isometric: 1.32 ± 0.18). Thus, these findings suggested sEMG complexity is affected by fatigue status and contraction type, with the degree of fatigue-mediated loss of complexity dependent on the type of contraction used to elicit fatigue.
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Hajian, Gelareh, Ali Etemad, and Evelyn Morin. "Automated Channel Selection in High-Density sEMG for Improved Force Estimation." Sensors 20, no. 17 (August 27, 2020): 4858. http://dx.doi.org/10.3390/s20174858.
Повний текст джерелаАнотація:
Accurate and real-time estimation of force from surface electromyogram (EMG) signals enables a variety of applications. We developed and validated new approaches for selecting subsets of high-density (HD) EMG channels for improved and lower-dimensionality force estimation. First, a large dataset was recorded from a number of participants performing isometric contractions in different postures, while simultaneously recording HD-EMG channels and ground-truth force. The EMG signals were acquired from three linear surface electrode arrays, each with eight monopolar channels, and were placed on the long head and short head of the biceps brachii and brachioradialis. After data collection and pre-processing, fast orthogonal search (FOS) was employed for force estimation. To select a subset of channels, principal component analysis (PCA) in the frequency domain and a novel index called the power-correlation ratio (PCR), which maximizes the spectral power while minimizing similarity to other channels, were used. These approaches were compared to channel selection using time-domain PCA. We selected one, two, and three channels per muscle from the original seven differential channels to reduce the redundancy and correlation in the dataset. In the best case, we achieved an approximate improvement of 30% for force estimation while reducing the dimensionality by 57% for a subset of three channels.
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Ruvalcaba, J. Antonio, M. I. Gutiérrez, A. Vera, and L. Leija. "Wearable Active Electrode for sEMG Monitoring Using Two-Channel Brass Dry Electrodes with Reduced Electronics." Journal of Healthcare Engineering 2020 (August 1, 2020): 1–11. http://dx.doi.org/10.1155/2020/5950218.
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Gel-based electrodes are employed to record sEMG signals for prolonged periods. These signals are used for the control of myoelectric prostheses, clinical analysis, or sports medicine. However, when the gel dries, the electrode-skin impedance increases considerably. Using dry active electrodes (AEs) to compensate variations of impedance is an alternative for long-term recording. This work describes the optimization of the electronic design of a conventional AE by removing the impedance coupling stage and two filters. The proposed work consisted of 5 stages: electrodes, amplification (X250), 2.2 Vdc offset, low-pass filter, and ADC with USART communication. The device did not need the use of electrolytic gel. The measurements of CMRR (96 dB), amplitude of the output sEMG signal (∼1.6 Vp-p), and system bandwidth (15–450 Hz) were performed in order to confirm the reliability of the device as an sEMG signal acquisition system. The SNR values from seven movements performed by eleven volunteers were compared in order to measure the repeatability of the measurements (average 30.32 dB for a wrist flexion). The SNR for wrist flexion measured with the proposed and the commercial system was compared; the values were 49 dB and 60 dB, respectively.
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Khoshdel, Vahab, and Alireza Akbarzadeh. "An optimized artificial neural network for human-force estimation: consequences for rehabilitation robotics." Industrial Robot: An International Journal 45, no. 3 (May 21, 2018): 416–23. http://dx.doi.org/10.1108/ir-10-2017-0190.
Повний текст джерелаАнотація:
Purpose This paper aims to present an application of design of experiments techniques to determine the optimized parameters of artificial neural networks (ANNs), which are used to estimate human force from Electromyogram (sEMG) signals for rehabilitation robotics. Physiotherapists believe, to make a precise therapeutic exercise, we need to design and perform therapeutic exercise base on patient muscle activity. Therefore, sEMG signals are the best tool for using in therapeutic robots because they are related to the muscle activity. Using sEMG signals as input for therapeutic robots need precise human force estimation from sEMG. Furthermore, the ANN estimator performance is highly dependent on the accuracy of the target date and setting parameters. Design/methodology/approach In the previous studies, the force data, which are collected from the force sensors or dynameters, has widely been used as target data in the training phase of learning ANN. However, force sensors or dynameters could measure only contact force. Therefore, the authors consider the contact force, limb’s dynamic and time in target data to increase the accuracy of target data. Findings There are plenty of algorithms that are used to obtain optimal ANN settings. However, to the best of our knowledge, they do not use regression analysis to model the effect of each parameter, as well as present the contribution percentage and significance level of the ANN parameters for force estimation. Originality/value In this paper, a new model to estimate the force from sEMG signals is presented. In this method, the sum of the limb’s dynamics and the contact force is used as target data in the training phase. To determine the limb’s dynamics, the patient’s body and the rehabilitation robot are modeled in OpenSim. Furthermore, in this paper, sEMG experimental data are collected and the ANN parameters based on an orthogonal array design table are regulated to train the ANN. Taguchi is used to find the optimal parameters settings. Next, analysis of variance technique is used to obtain significance level, as well as contribution percentage of each parameter, to optimize ANN’s modeling in human force estimation. The results indicate that the presented model can precisely estimate human force from sEMG signals.
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Savithri, Chanda Nagarajan, Ebenezer Priya, and Kevin Rajasekar. "A machine learning approach to identify hand actions from single-channel sEMG signals." Biomedical Engineering / Biomedizinische Technik 67, no. 2 (February 22, 2022): 89–103. http://dx.doi.org/10.1515/bmt-2021-0072.
Повний текст джерелаАнотація:
Abstract Surface Electromyographic (sEMG) signal is a prime source of information to activate prosthetic hand such that it is able to restore a few basic hand actions of amputee, making it suitable for rehabilitation. In this work, a non-invasive single channel sEMG amplifier is developed that captures sEMG signal for three typical hand actions from the lower elbow muscles of able bodied subjects and amputees. The recorded sEMG signal detrends and has frequencies other than active frequencies. The Empirical Mode Decomposition Detrending Fluctuation Analysis (EMD–DFA) is attempted to de-noise the sEMG signal. A feature vector is formed by extracting eight features in time domain, seven features each in spectral and wavelet domain. Prominent features are selected by Fuzzy Entropy Measure (FEM) to ease the computational complexity and reduce the recognition time of classification. Classification of different hand actions is attempted based on multi-class approach namely Partial Least Squares Discriminant Analysis (PLS–DA) to control the prosthetic hand. It is inferred that an accuracy of 89.72% & 84% is observed for the pointing action whereas the accuracy for closed fist is 81.2% & 79.54% while for spherical grasp it is 80.6% & 76% respectively for normal subjects and amputees. The performance of the classifier is compared with Linear Discriminant Analysis (LDA) and an improvement of 5% in mean accuracy is observed for both normal subjects and amputees. The mean accuracy for all the three different hand actions is significantly high (83.84% & 80.18%) when compared with LDA. The proposed work frame provides a fair mean accuracy in classifying the hand actions of amputees. This methodology thus appears to be useful in actuating the prosthetic hand.
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Chen, Lin, Jianting Fu, Yuheng Wu, Haochen Li, and Bin Zheng. "Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals." Sensors 20, no. 3 (January 26, 2020): 672. http://dx.doi.org/10.3390/s20030672.
Повний текст джерелаАнотація:
By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.
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Emayavaramban, G., A. Amudha, T. Rajendran, M. Sivaramkumar, K. Balachandar, and T. Ramesh. "Identifying User Suitability in sEMG Based Hand Prosthesis Using Neural Networks." Current Signal Transduction Therapy 14, no. 2 (October 10, 2019): 158–64. http://dx.doi.org/10.2174/1574362413666180604100542.
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Background: Identifying user suitability plays a vital role in various modalities like neuromuscular system research, rehabilitation engineering and movement biomechanics. This paper analysis the user suitability based on neural networks (NN), subjects, age groups and gender for surface electromyogram (sEMG) pattern recognition system to control the myoelectric hand. Six parametric feature extraction algorithms are used to extract the features from sEMG signals such as AR (Autoregressive) Burg, AR Yule Walker, AR Covariance, AR Modified Covariance, Levinson Durbin Recursion and Linear Prediction Coefficient. The sEMG signals are modeled using Cascade Forward Back propagation Neural Network (CFBNN) and Pattern Recognition Neural Network. Methods: sEMG signals generated from forearm muscles of the participants are collected through an sEMG acquisition system. Based on the sEMG signals, the type of movement attempted by the user is identified in the sEMG recognition module using signal processing, feature extraction and machine learning techniques. The information about the identified movement is passed to microcontroller wherein a control is developed to command the prosthetic hand to emulate the identified movement. Results: From the six feature extraction algorithms and two neural network models used in the study, the maximum classification accuracy of 95.13% was obtained using AR Burg with Pattern Recognition Neural Network. This justifies that the Pattern Recognition Neural Network is best suited for this study as the neural network model is specially designed for pattern matching problem. Moreover, it has simple architecture and low computational complexity. AR Burg is found to be the best feature extraction technique in this study due to its high resolution for short data records and its ability to always produce a stable model. In all the neural network models, the maximum classification accuracy is obtained for subject 10 as a result of his better muscle fitness and his maximum involvement in training sessions. Subjects in the age group of 26-30 years are best suited for the study due to their better muscle contractions. Better muscle fatigue resistance has contributed for better performance of female subjects as compared to male subjects. From the single trial analysis, it can be observed that the hand close movement has achieved best recognition rate for all neural network models. Conclusion: In this paper a study was conducted to identify user suitability for designing hand prosthesis. Data were collected from ten subjects for twelve tasks related to finger movements. The suitability of the user was identified using two neural networks with six parametric features. From the result, it was concluded thatfit women doing regular physical exercises aged between 26-30 years are best suitable for developing HMI for designing a prosthetic hand. Pattern Recognition Neural Network with AR Burg extraction features using extension movements will be a better way to design the HMI. However, Signal acquisition based on wireless method is worth considering for the future.
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Veer, Karan. "Experimental Study and Characterization of SEMG Signals for Upper Limbs." Fluctuation and Noise Letters 14, no. 03 (June 29, 2015): 1550028. http://dx.doi.org/10.1142/s0219477515500285.
Повний текст джерелаАнотація:
Surface electromyogram (SEMG) is used to measure the activity of superficial muscles and is an essential tool to carry out biomechanical assessments required for prosthetic design. Many previous attempts suggest that, electromyogram (EMG) signals have random nature. Here, dual channel evaluation of EMG signals acquired from the amputed subjects using computational techniques for classification of arm motion are presented. After recording data from four predefined upper arm motions, interpretation of signal was done for six statistical features. The signals are classified by the neural network (NN) and then interpretation was done using statistical technique to extract the effectiveness of recorded signals. The network performances are analyzed by considering the number of input features, hidden layer, learning algorithm and mean square error. From the results, it is observed that there exists calculative difference in amplitude gain across different motions and have great potential to classify arm motions. The outcome indicates that NN algorithm performs significantly better than other algorithms with classification accuracy (CA) of 96.40%. Analysis of variance technique presents the results to validate the effectiveness of recorded data to discriminate SEMG signals. Results are of significant thrust in identifying the operations that can be implemented for classifying upper limb movements suitable for prostheses design.
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Wang, Lin, Hong Wang, Rong Rong Fu, and Ning Ning Zhang. "Characteristic Parameters of Surface Electromyography Signals of Cervical Muscles." Applied Mechanics and Materials 249-250 (December 2012): 1308–12. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.1308.
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Surface electromyography (SEMG) signals of cervical muscles are investigated by time-frequency analysis and biomechanics analysis. Medium frequency (MF) and integrated electromyography (IEMG) are extracted and analyzed from SEMG signals of subjects’ upper trapezius. The Experimental results show that the value of MF decreases and the value of IEMG increases with the increase of fatigue of the vertical muscles. Also, the values of IEMG at different testing points of same cervical muscle are compared. The value of IEMG with higher resistant moment is higher than that with lower resistant moment. That means the muscle with high resistance moment is easier to be fatigue. This investigation is important for people, especially those who work/read with bowing head or before computer for a long time, to prevent cervical spondylosis.
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Qing, Zengyu, Zongxing Lu, Yingjie Cai, and Jing Wang. "Elements Influencing sEMG-Based Gesture Decoding: Muscle Fatigue, Forearm Angle and Acquisition Time." Sensors 21, no. 22 (November 19, 2021): 7713. http://dx.doi.org/10.3390/s21227713.
Повний текст джерелаАнотація:
The surface Electromyography (sEMG) signal contains information about movement intention generated by the human brain, and it is the most intuitive and common solution to control robots, orthotics, prosthetics and rehabilitation equipment. In recent years, gesture decoding based on sEMG signals has received a lot of research attention. In this paper, the effects of muscle fatigue, forearm angle and acquisition time on the accuracy of gesture decoding were researched. Taking 11 static gestures as samples, four specific muscles (i.e., superficial flexor digitorum (SFD), flexor carpi ulnaris (FCU), extensor carpi radialis longus (ECRL) and finger extensor (FE)) were selected to sample sEMG signals. Root Mean Square (RMS), Waveform Length (WL), Zero Crossing (ZC) and Slope Sign Change (SSC) were chosen as signal eigenvalues; Linear Discriminant Analysis (LDA) and Probabilistic Neural Network (PNN) were used to construct classification models, and finally, the decoding accuracies of the classification models were obtained under different influencing elements. The experimental results showed that the decoding accuracy of the classification model decreased by an average of 7%, 10%, and 13% considering muscle fatigue, forearm angle and acquisition time, respectively. Furthermore, the acquisition time had the biggest impact on decoding accuracy, with a maximum reduction of nearly 20%.
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Cheng, Juan, Fulin Wei, Chang Li, Yu Liu, Aiping Liu, and Xun Chen. "Position-independent gesture recognition using sEMG signals via canonical correlation analysis." Computers in Biology and Medicine 103 (December 2018): 44–54. http://dx.doi.org/10.1016/j.compbiomed.2018.08.020.
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Karthick, P. A., M. Navaneethakrishna, N. Punitha, A. R. Jac Fredo, and S. Ramakrishnan. "Analysis of muscle fatigue conditions using time-frequency images and GLCM features." Current Directions in Biomedical Engineering 2, no. 1 (September 1, 2016): 483–87. http://dx.doi.org/10.1515/cdbme-2016-0107.
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AbstractIn this work, an attempt has been made to differentiate muscle non-fatigue and fatigue conditions using sEMG signals and texture representation of the time-frequency images. The sEMG signals are recorded from the biceps brachii muscle of 25 healthy adult volunteers during dynamic fatiguing contraction. The first and last curls of these signals are considered as the non-fatigue and fatigue zones, respectively. These signals are preprocessed and the time-frequency spectrum is computed using short time fourier transform (STFT). Gray-Level Co-occurrence Matrix (GLCM) is extracted from low (15–45 Hz), medium (46–95 Hz) and high (96–150 Hz) frequency bands of the time-frequency images. Further, the features such as contrast, correlation, energy and homogeneity are calculated from the resultant matrices. The results show that the high frequency band based features are able to differentiate non-fatigue and fatigue conditions. The features such as correlation, contrast and homogeneity extracted at angles 0°, 45°, 90°, and 135° are found to be distinct with high statistical significance (p < 0.0001). Hence, this framework can be used for analysis of neuromuscular disorders.
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Pietraszewski, Przemysław, Artur Gołaś, Michał Krzysztofik, Marta Śrutwa, and Adam Zając. "Evaluation of Lower Limb Muscle Electromyographic Activity during 400 m Indoor Sprinting among Elite Female Athletes: A Cross-Sectional Study." International Journal of Environmental Research and Public Health 18, no. 24 (December 14, 2021): 13177. http://dx.doi.org/10.3390/ijerph182413177.
Повний текст джерелаАнотація:
The purpose of this cross-sectional study was to analyze changes in normalized surface electromyography (sEMG) signals for the gastrocnemius medialis, biceps femoris, gluteus maximus, tibialis anterior, and vastus lateralis muscles occurring during a 400 m indoor sprint between subsequent curved sections of the track. Ten well-trained female sprinters (age: 21 ± 4 years; body mass: 47 ± 5 kg; body height: 161 ± 7 cm; 400 m personal best: 52.4 ± 1.1 s) performed an all-out 400 m indoor sprint. Normalized sEMG signals were recorded bilaterally from the selected lower limb muscles. The two-way ANOVA (curve × side) revealed no statistically significant interaction. However, the main effect analysis showed that normalized sEMG signals significantly increased in subsequent curves run for all the studied muscles: gastrocnemius medialis (p = 0.003), biceps femoris (p < 0.0001), gluteus maximus (p = 0.044), tibialis anterior (p = 0.001), and vastus lateralis (p = 0.023), but differences between limbs were significant only for the gastrocnemius medialis (p = 0.012). The results suggest that the normalized sEMG signals for the lower limb muscles increased in successive curves during the 400 m indoor sprint. Moreover, the gastrocnemius medialis of the inner leg is highly activated while running curves; therefore, it should be properly prepared for high demands, and attention should be paid to the possibility of the occurrence of a negative adaptation, such as asymmetries.
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Liu, Lizheng, Jianjun Cui, Jian Niu, Na Duan, Xianjia Yu, Qingqing Li, Shih-Ching Yeh, and Li-Rong Zheng. "Design of Mirror Therapy System Base on Multi-Channel Surface-Electromyography Signal Pattern Recognition and Mobile Augmented Reality." Electronics 9, no. 12 (December 14, 2020): 2142. http://dx.doi.org/10.3390/electronics9122142.
Повний текст джерелаАнотація:
Numerous studies have proven that the mirror therapy can make rehabilitation more effective on hemiparesis following a stroke. Using surface electromyography (SEMG) to predict gesture presents one of the important subjects in related research areas, including rehabilitation medicine, sports medicine, prosthetic control, and so on. However, current signal analysis methods still fail to achieve accurate recognition of multimode motion in a very reliable way due to the weak physiological signal and low noise-ratio. In this paper, a mirror therapy system based on multi-channel SEMG signal pattern recognition and mobile augmented reality is studied. Besides, wavelet transform method is designed to mitigate the noise. The spectrogram obtained by analyzing electromyography signals is proposed to be used as an image. Two approaches, including Convolutional Neural Network (CNN) and grid-optimized Support Vector Machine (SVM), are designed to classify the SEMG of different gestures. The mobile augmented reality provides a virtual hand movement in the real environment to perform mirror therapy process. The experimental results show that the overall accuracy of SVM is 93.07%, and that of CNN is up to 97.8%.
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Zhang, Xianfu, Yuping Hu, Ruimin Luo, Chao Li, and Zhichuan Tang. "The Impact of Load Style Variation on Gait Recognition Based on sEMG Images Using a Convolutional Neural Network." Sensors 21, no. 24 (December 15, 2021): 8365. http://dx.doi.org/10.3390/s21248365.
Повний текст джерелаАнотація:
Surface electromyogram (sEMG) signals are widely employed as a neural control source for lower-limb exoskeletons, in which gait recognition based on sEMG is particularly important. Many scholars have taken measures to improve the accuracy of gait recognition, but several real-time limitations affect its applicability, of which variation in the load styles is obvious. The purposes of this study are to (1) investigate the impact of different load styles on gait recognition; (2) study whether good gait recognition performance can be obtained when a convolutional neural network (CNN) is used to deal with the sEMG image from sparse multichannel sEMG (SMC-sEMG); and (3) explore whether the control system of the lower-limb exoskeleton trained by sEMG from part of the load styles still works efficiently in a real-time environment where multiload styles are required. In addition, we discuss an effective method to improve gait recognition at the levels of the load styles. In our experiment, fifteen able-bodied male graduate students with load (20% of body weight) and using three load styles (SBP = backpack, SCS = cross shoulder, SSS = straight shoulder) were asked to walk uniformly on a treadmill. Each subject performed 50 continuous gait cycles under three speeds (V3 = 3 km/h, V5 = 5 km/h, and V7 = 7 km/h). A CNN was employed to deal with sEMG images from sEMG signals for gait recognition, and back propagation neural networks (BPNNs) and support vector machines (SVMs) were used for comparison by dealing with the same sEMG signal. The results indicated that (1) different load styles had remarkable impact on the gait recognition at three speeds under three load styles (p < 0.001); (2) the performance of gait recognition from the CNN was better than that from the SVM and BPNN at each speed (84.83%, 81.63%, and 83.76% at V3; 93.40%, 88.48%, and 92.36% at V5; and 90.1%, 86.32%, and 85.42% at V7, respectively); and (3) when all the data from three load styles were pooled as testing sets at each speed, more load styles were included in the training set, better performance was obtained, and the statistical analysis suggested that the kinds of load styles included in training set had a significant effect on gait recognition (p = 0.002), from which it can be concluded that the control system of a lower-limb exoskeleton trained by sEMG using only some load styles is not sufficient in a real-time environment.
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Lin, B., S. F. Wong, and A. Baca. "Comparison of Different Time-Frequency Analyses Techniques Based on sEMG-Signals in Table Tennis: A Case Study." International Journal of Computer Science in Sport 17, no. 1 (July 1, 2018): 77–93. http://dx.doi.org/10.2478/ijcss-2018-0004.
Повний текст джерелаАнотація:
Abstract The surface EMG signal in the action of dynamic contraction has more movement interference compared to sustained static contractions. In addition, the recruitment and de-recruitment of motor units causes a faster change in the surface EMG signal’s proprieties. Therefore, more complex techniques are required to extract information from the surface EMG signal. The standardized protocol for surface myoelectric signal measurement in table tennis was a case study in this research area. The Autoregressive method based on the Akaike Information Criterion, the Wavelet method based on intensity analysis, and the Hilbert-Huang transform method were used to estimate the muscle fatigue and non-fatigue condition. The result was that the Hilbert-Huang transform method was shown to be more reliable and accurate for studying the biceps brachii muscle in both conditions. However, the Wavelet method based on intensity analysis is more reliable and accurate for the pectoralis major muscle, deltoideus anterior muscle and deltoideus medialis muscle. The results suggest that different time-frequency analysis techniques influence different muscle analyses based on surface EMG signals in fatigue and non-fatigue conditions
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Said, Sherif, Abdullah S. Karar, Taha Beyrouthy, Samer Alkork, and Amine Nait-ali. "Biometrics Verification Modality Using Multi-Channel sEMG Wearable Bracelet." Applied Sciences 10, no. 19 (October 5, 2020): 6960. http://dx.doi.org/10.3390/app10196960.
Повний текст джерелаАнотація:
Electrical biosignals have the potential for use as biometric authenticators, owing to their ability to facilitate liveness detection and concealed nature. In this work, the viability of using surface electromyogram (sEMG) as a biometric modality for users verification is investigated. A database of multi-channel sEMG signals is created using a wearable armband from able-bodied users. Each user used his/her muscles to form a password that consists of a unique combination of specific hand gestures. A total of 18 features are extracted from the signals in order to distinguish between the users. Several features are extracted in the frequency domain after estimating the power spectral density while using the Welch’s method. Specifically, average frequency, signal power, median frequency, Kurtosis, Deciles, coefficient of dissymmetry, and the peak frequency of the sEMG signal are considered. To further increase the accuracy of the classifier, time domain features are also extracted through segmentation of the signal into 10 segments, and then calculating both the root mean square and length of the signal. Several classifiers that are based on K-nearest Neighbors (KNN), Linear Discernment Analysis (LDA), and Ensemble of Classifiers are constructed, trained, and statistically compared, resulting in an average accuracy in 97.4%, 98.3%, and 98.5%, respectively. False acceptance rate (FAR) and False Rejection Rate (FRR) are estimated for each classifier in order to determine the effectiveness of the biometrics verification system. Although the ensemble classifier accuracy was found to be the highest, the results show that the KNN classifier exhibits a FAR of 0.2% and FRR of 2.9%. Thus, the KNN classifier was found to he the optimum classifier after the extraction of all 18 features. This work demonstrates the usefulness of sEMG as a biometric authenticator in user verification.
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Wang, You, Hengyang Wang, Huiyan Li, Asif Ullah, Ming Zhang, Han Gao, Ruifen Hu, and Guang Li. "Qualitative Recognition of Primary Taste Sensation Based on Surface Electromyography." Sensors 21, no. 15 (July 23, 2021): 4994. http://dx.doi.org/10.3390/s21154994.
Повний текст джерелаАнотація:
Based on surface electromyography (sEMG), a novel recognition method to distinguish six types of human primary taste sensations was developed, and the recognition accuracy was 74.46%. The sEMG signals were acquired under the stimuli of no taste substance, distilled vinegar, white granulated sugar, instant coffee powder, refined salt, and Ajinomoto. Then, signals were preprocessed with the following steps: sample augments, removal of trend items, high-pass filter, and adaptive power frequency notch. Signals were classified with random forest and the classifier gave a five-fold cross-validation accuracy of 74.46%, which manifested the feasibility of the recognition task. To further improve the model performance, we explored the impact of feature dimension, electrode distribution, and subject diversity. Accordingly, we provided an optimized feature combination that reduced the number of feature types from 21 to 4, a preferable selection of electrode positions that reduced the number of channels from 6 to 4, and an analysis of the relation between subject diversity and model performance. This study provides guidance for further research on taste sensation recognition with sEMG.
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Xi, Xugang, Wenjun Jiang, Seyed M. Miran, Xian Hua, Yun-Bo Zhao, Chen Yang, and Zhizeng Luo. "Feature Extraction of Surface Electromyography Based on Improved Small-World Leaky Echo State Network." Neural Computation 32, no. 4 (April 2020): 741–58. http://dx.doi.org/10.1162/neco_a_01270.
Повний текст джерелаАнотація:
Surface electromyography (sEMG) is an electrophysiological reflection of skeletal muscle contractile activity that can directly reflect neuromuscular activity. It has been a matter of research to investigate feature extraction methods of sEMG signals. In this letter, we propose a feature extraction method of sEMG signals based on the improved small-world leaky echo state network (ISWLESN). The reservoir of leaky echo state network (LESN) is connected by a random network. First, we improved the reservoir of the echo state network (ESN) by these networks and used edge-added probability to improve these networks. That idea enhances the adaptability of the reservoir, the generalization ability, and the stability of ESN. Then we obtained the output weight of the network through training and used it as features. We recorded the sEMG signals during different activities: falling, walking, sitting, squatting, going upstairs, and going downstairs. Afterward, we extracted corresponding features by ISWLESN and used principal component analysis for dimension reduction. At the end, scatter plot, the class separability index, and the Davies-Bouldin index were used to assess the performance of features. The results showed that the ISWLESN clustering performance was better than those of LESN and ESN. By support vector machine, it was also revealed that the performance of ISWLESN for classifying the activities was better than those of ESN and LESN.
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Li, Shunchong, Xinjun Sheng, Honghai Liu, and Xiangyang Zhu. "Design of a myoelectric prosthetic hand implementing postural synergy mechanically." Industrial Robot: An International Journal 41, no. 5 (August 12, 2014): 447–55. http://dx.doi.org/10.1108/ir-03-2014-0312.
Повний текст джерелаАнотація:
Purpose – This paper aims to describe the design of a multi-degree of freedom (DOF) prosthetic hand prototype implementing postural synergy mechanically, which is actuated by two motors via a transmission unit, and is controlled using surface electromyography (sEMG) signal. Design/methodology/approach – First, an anthropomorphic robotic hand is designed to imitate the human hand. The robotic hand has 18 DOF, 12 of which are actively driven by Bowden cables. Next, a set of different grasp modes are performed on a “full actuation” robotic hand, and principal component analysis (PCA) method is used to extract the first two postural synergies. Then, they are used to design a differential pulley-based transmission unit using two independent inputs to drive 12 output tendons. Finally, two control signals extracted from six channels of sEMG signals are used to proportionally control the two motors for achieving hand posture synthesis. Findings – Using a differential pulley-based mechanical transmission unit to implement the synthesis of the first two postural synergies can make the prosthetic hand achieve different grasps by two motors, such as power, precision and lateral grasps. It is also feasible to control this “two actuation” prosthetic hand by relating the two-dimensional sEMG inputs with the first two postural synergies. Originality/value – Mechanical implantation of postural synergies reduces the number of independent actuators without sacrificing the prosthetic hand’s versatility and simplifies its controller. Two-dimensional control extracted from sEMG is mapped into the combination coefficients of postural synergy synthesis. It shows potential application in the practical prosthetic hand.
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Chen, Jingcheng, Yining Sun, and Shaoming Sun. "Muscle Synergy of Lower Limb Motion in Subjects with and without Knee Pathology." Diagnostics 11, no. 8 (July 22, 2021): 1318. http://dx.doi.org/10.3390/diagnostics11081318.
Повний текст джерелаАнотація:
Surface electromyography (sEMG) has great potential in investigating the neuromuscular mechanism for knee pathology. However, due to the complex nature of neural control in lower limb motions and the divergences in subjects’ health and habits, it is difficult to directly use the raw sEMG signals to establish a robust sEMG analysis system. To solve this, muscle synergy analysis based on non-negative matrix factorization (NMF) of sEMG is carried out in this manuscript. The similarities of muscle synergy of subjects with and without knee pathology performing three different lower limb motions are calculated. Based on that, we have designed a classification method for motion recognition and knee pathology diagnosis. First, raw sEMG segments are preprocessed and then decomposed to muscle synergy matrices by NMF. Then, a two-stage feature selection method is executed to reduce the dimension of feature sets extracted from aforementioned matrices. Finally, the random forest classifier is adopted to identify motions or diagnose knee pathology. The study was conducted on an open dataset of 11 healthy subjects and 11 patients. Results show that the NMF-based sEMG classifier can achieve good performance in lower limb motion recognition, and is also an attractive solution for clinical application of knee pathology diagnosis.
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She, Zhu, Tian, Wang, Yokoi, and Huang. "SEMG Feature Extraction Based on Stockwell Transform Improves Hand Movement Recognition Accuracy." Sensors 19, no. 20 (October 14, 2019): 4457. http://dx.doi.org/10.3390/s19204457.
Повний текст джерелаАнотація:
Feature extraction, as an important method for extracting useful information from surfaceelectromyography (SEMG), can significantly improve pattern recognition accuracy. Time andfrequency analysis methods have been widely used for feature extraction, but these methods analyzeSEMG signals only from the time or frequency domain. Recent studies have shown that featureextraction based on time-frequency analysis methods can extract more useful information fromSEMG signals. This paper proposes a novel time-frequency analysis method based on the Stockwelltransform (S-transform) to improve hand movement recognition accuracy from forearm SEMGsignals. First, the time-frequency analysis method, S-transform, is used for extracting a feature vectorfrom forearm SEMG signals. Second, to reduce the amount of calculations and improve the runningspeed of the classifier, principal component analysis (PCA) is used for dimensionality reduction of thefeature vector. Finally, an artificial neural network (ANN)-based multilayer perceptron (MLP) is usedfor recognizing hand movements. Experimental results show that the proposed feature extractionbased on the S-transform analysis method can improve the class separability and hand movementrecognition accuracy compared with wavelet transform and power spectral density methods.