Single channel sEMG muscle fatigue prediction: An implementation using least square support vector machine

Sharawardi, Nur Shidah Ahmad; Choo, Yun-Huoy; Shao, Chong; Muda, Azah Kamilah; Goh, Ong Sing

doi:10.1109/wict.2014.7077287

Cited by 12 publications

(8 citation statements)

References 16 publications

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“…In the last years, least square support vector machine has been effectively used to solve the pre-diction and classification issues in medical domain. Due to its high performance to classify the time series data with a high classification accuracy and a minimum time execution [33], it is employed in various fields such as for prediction of muscle fatigue in electromyogram signals [32] and breast cancer prediction [34].…”

Section: Least Square Support Vector Machine (Ls-svm)mentioning

confidence: 99%

Clustering similar time series data for the prediction the patients with heart disease

Lafta

AL‐Musaylh

Shallal

2022

IJEECS

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Developed intelligent technologies are become play a promising role in providing better decision-making and improving the medical services provided to the patients. A risk prediction task for short-term is big challenge task; however, it is a great importance for recommendation systems in health care field to provide patients with accurate and reliable recommendations. In this work, clustering method and least square support vector machine are used for prediction a short-term disease risk prediction. The clustering similar method is based on euclidean Distance which used to identify the similar sliding windows. The proposed model is trained by using the slide windows samples. Finally, the appropriate recommendations are generated for heart diseases patients who need to take a medical test or not for following day using least square support vector machine. A real dataset which collected from heart diseases patient is used for evaluation. The proposed method yields a good results related by the recommendations accuracy generated to chronicle heart patients and reduce the risk of incorrect recommendations.

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Section: Least Square Support Vector Machine (Ls-svm)mentioning

confidence: 99%

Clustering similar time series data for the prediction the patients with heart disease

Lafta

AL‐Musaylh

Shallal

2022

IJEECS

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“…For further classification, the recorded EMG signals were extracted with time and frequency domain which are mean absolute value (MAV), root mean square (RMS), median and waveform length (WL) that are taken from 50 samples per subject [16]. MAV is produced by calculating the absolute value of the signal's average as shown in (1).…”

Section: Data Classificationmentioning

confidence: 99%

“…Other papers [16][17][18][19][20], they aimed to classify hand gestures by using EMG signals where seven features are extracted from it. For the hand gestures, four gestures are chosen.…”

mentioning

confidence: 99%

Classification of EMG signal for multiple hand gestures based on neural network

Abu

Rosleesham

Suboh

et al. 2020

IJEECS

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<span>This paper presents the classification of EMG signal for multiple hand gestures based on neural network. In this study, the Electromyography is used to measure the muscle cell’s electrical activities which is commonly represented in a function time. Every muscle has their own signals, which was produced in every movement. Surface electromyography (sEMG) is used as a non-invasive technique for acquiring the EMG signal. The development of sensors’ detection and measuring the EMG have been improved and have become more precise while maintaining a small size. In this paper, the main objective is to identify the hand gestures based on: (1) Cylindrical Grasp, (2) Supination (Twist Left), (3) Pronation (Twist Right), (4) Resting Hand and (5) Open Hand that are predefined by using Arduino IDE, CoolTerm software and Microsoft Excel before using artificial neural network for classifying purposes in MATLAB. Finally, the extraction of the EMG patterns for each movement went through features extraction of the signals which is used to train the classifier in MATLAB to classify signals in the neural network. The features extracted are using mean absolute value (MAV), median, waveform length (WL) and root mean square (RMS). The Artificial Neural Network (ANN) produced accuracy of 80% for training and testing for 10 hidden neurons layer.</span>

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“…A number of research papers report the application of machine learning techniques in medical diagnosis [3,[16][17][18][19][20][21][22]. Such techniques enable classification of normal and abnormal cases helping the diagnoses process of patients.…”

Section: Introductionmentioning

confidence: 99%

“…For example, machine learning can be used to identify heart disease, cerebral infarction, urological dysfunction, diagnosis of students with learning disabilities, muscle fatigue prediction, etc. [20][21]. In addition, research is going on the aspect of spinal abnormalities and low back pain.…”

Section: Introductionmentioning

confidence: 99%

Data-driven diagnosis of spinal abnormalities using feature selection and machine learning algorithms

Raihan-Al-Masud¹,

Mondal²

2020

PLoS ONE

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This paper focuses on the application of machine learning algorithms for predicting spinal abnormalities. As a data preprocessing step, univariate feature selection as a filter based feature selection, and principal component analysis (PCA) as a feature extraction algorithm are considered. A number of machine learning approaches namely support vector machine (SVM), logistic regression (LR), bagging ensemble methods are considered for the diagnosis of spinal abnormality. The SVM, LR, bagging SVM and bagging LR models are applied on a dataset of 310 samples publicly available in Kaggle repository. The performance of classification of abnormal and normal spinal patients is evaluated in terms of a number of factors including training and testing accuracy, recall, and miss rate. The classifier models are also evaluated by optimizing the kernel parameters, and by using the results of receiver operating characteristic (ROC) and precision-recall curves. Results indicate that when 78% data are used for training, the observed training accuracies for SVM, LR, bagging SVM and bagging LR are 86.30%, 85.47%, 86.72% and 85.06%, respectively. On the other hand, the accuracies for the test dataset for SVM, LR, bagging SVM and bagging LR are the same being 86.96%. However, bagging SVM is the most attractive as it has a higher recall value and a lower miss rate compared to others. Hence, bagging SVM is suitable for the classification of spinal patients when applied on the most five important features of spinal samples.

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Single channel sEMG muscle fatigue prediction: An implementation using least square support vector machine

Cited by 12 publications

References 16 publications

Clustering similar time series data for the prediction the patients with heart disease

Clustering similar time series data for the prediction the patients with heart disease

Classification of EMG signal for multiple hand gestures based on neural network

Data-driven diagnosis of spinal abnormalities using feature selection and machine learning algorithms

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