Pattern recognition of electromyography (EMG) signal for wrist movement using learning vector quantization (LVQ)

Putra, Darma Setiawan; Weru, Y U W; Fitriady,

doi:10.1088/1757-899x/506/1/012020

Cited by 9 publications

(4 citation statements)

References 23 publications

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“…They are simple to extract, do not require any type of transformation and high computational burden [15]. We computed six timedomain features that have been successful in the literature in discriminating the EMG signals, namely, variance (var), standard deviation (std), root mean square (rms), average energy (ae), minimum (min) and maximum (max) [16][17][18]. Table 1 presents the equations of timedomain features.…”

Section: Feature Extractionmentioning

confidence: 99%

Electromyography based hand movement classification and feature extraction using machine learning algorithms

EKİNCİ,

GARİP,

SERBEST

2023

Politeknik Dergisi

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The categorization of hand gestures holds significant importance in controlling orthotic and prosthetic devices, enabling human-machine interaction, and facilitating telerehabilitation applications. For many years, methods of motion analysis based on image processing techniques have been employed to detect hand motions. However, recent research has focused on utilizing muscle contraction for detecting hand movements. Specifically, there has been an increase in studies that classify hand movements using surface electromyography (sEMG) data from the muscles of the hand and arm. In our study, we estimated the open (extension of the fingers) and closed (flexion of the fingers) positions of the hand by analyzing EMG data obtained from 4 volunteer participants' Extensor digitorum and Flexor carpi radialis muscles. In order to accurately discriminate EMG signals, various statistical measures such as variance, standard deviation, root mean square, average energy, minimum and maximum features were utilized. The dataset containing these additional features was then subjected to classification algorithms including Support Vector Machines (SVM), K Nearest Neighbour (KNN), Decision Tree (DT), and Gaussian Naive Bayes (GNB) for the purpose of classifying hand positions into open or closed states. Among the tested algorithms, SVM achieved the highest success rate with a maximum accuracy of 73.1%, while KNN yielded the lowest success rate at a minimum accuracy of 55.9%. To further enhance prediction accuracy in future studies, it is suggested that data from a larger set of muscles be collected.

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Section: Feature Extractionmentioning

confidence: 99%

Electromyography based hand movement classification and feature extraction using machine learning algorithms

EKİNCİ,

GARİP,

SERBEST

2023

Politeknik Dergisi

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“…Traditional feature fusion with constant weights attempts to merge multiple feature vectors into a vector, which performs poorly in muscle fatigue recognition since feature weights cannot change with the testing object 13 . In this study, the multi-class support , , , , exp…”

Section: Improved Particle Swarm Optimization-support Vector Machine (Ipso-svm) Classifiermentioning

confidence: 99%

Research On Exercise Fatigue Estimation Method of Pilates Rehabilitation Based On ECG and sEMG Feature Fusion

Chen

2021

Preprint

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Purpose. Fatigue estimation is of great significance to improve the accuracy of intention recognition and avoid secondary injury in Pilates rehabilitation. Surface electromyography (sEMG) is used to estimate fatigue with low and unstable recognition rates. To improve the rate, this paper fused electrocardiogram (ECG) signal and sEMG signal under three different states, and the classification model of the improved proved particle swarm optimization support vector machine (IPSO-SVM) algorithm was established. Methods. Twenty subjects performed 150 minutes of Pilates rehabilitation exercise. ECG and sEMG signals were collected at the same time. After necessary preprocessing, the IPSO-SVM classification model based on feature fusion was established to identify three different fatigue states (relaxed, transition, and tired). The model effects of different classification algorithms and different fused data types were compared. Results. Compared with common physiological signal classification methods such as BP neural network algorithm(BPNN), K-nearest neighbor(KNN), and Linear discriminant analysis(LDA), IPSO-SVM had obvious advantages in the classification effect of sEMG and ECG signals, the average recognition rate was 87.83%. The recognition rates of sEMG and ECG fusion feature classification models were 94.25%, 92.25%, 94.25%. The recognition accuracy and model performance was significantly improved. Conclusion. The sEMG and ECG signal after feature fusion form a complementary mechanism. At the same time, IPOS-SVM can accurately detect the fatigue state in the process of Pilates rehabilitation. This study establishes technical support for establishing relevant man-machine devices and improving the safety of Pilates rehabilitation.

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“…In medical images, we need to deal with millions or billion pixels per picture to recognize or diagnose particular diseases. Thus, a huge number of computational processes are needed at the same time and the run time should be taken into consideration also [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Speed of the Learning Vector Quantization (LVQ) Algorithm by Adding Partial Distance Computation

AbuAlghanam

Adwan

Shariah

et al. 2022

Cybernetics and Information Technologies

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Learning Vector Quantization (LVQ) is one of the most widely used classification approaches. LVQ faces a problem as when the size of data grows large it becomes slower. In this paper, a modified version of LVQ, which is called PDLVQ is proposed to accelerate the traditional version. The proposed scheme aims to avoid unnecessary computations by applying an efficient Partial Distance (PD) computation strategy. Three different benchmark datasets are used in the experiments. The comparisons have been done between LVQ and PDLVQ in terms of runtime and in result, it turns out that PDLVQ shows better efficiency than LVQ. PDLVQ has achieved up to 37% efficiency in runtime compared to LVQ when the dimensions have increased. Also, the enhanced algorithm (PDLVQ) shows clear enhancement to decrease runtime when the size of dimensions, the number of clusters, or the size of data becomes increased compared with the traditional one which is LVQ.

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Pattern recognition of electromyography (EMG) signal for wrist movement using learning vector quantization (LVQ)

Cited by 9 publications

References 23 publications

Electromyography based hand movement classification and feature extraction using machine learning algorithms

Electromyography based hand movement classification and feature extraction using machine learning algorithms

Research On Exercise Fatigue Estimation Method of Pilates Rehabilitation Based On ECG and sEMG Feature Fusion

Enhancing the Speed of the Learning Vector Quantization (LVQ) Algorithm by Adding Partial Distance Computation

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