Discrete Wavelet Packet based Elbow Movement classification using Fine Gaussian SVM

Virdi, Prateek; Narayan, Yogendra; Kumari, Preeti; Mathew, Lini

doi:10.1109/icpeices.2016.7853657

Cited by 21 publications

(6 citation statements)

References 9 publications

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“…SVM has been widely used for classification due to their high accuracy, robustness, parametrization by means of kernel functions, and for being capable of analyzing large datasets [ 51 , 56 , 57 ]. Therefore, in past decades, this algorithm has been applied in biomedical applications such as stress recognition [ 26 ], breast cancer image classification [ 52 ], classification of elbow EMG signal [ 58 ], and classification of hand arthritis stages [ 59 ], among others. Considering all the aforementioned benefits, its use is investigated, including all mentioned kernels, in this work for differentiating between automobile drivers with stress and without stress automatically.…”

Section: Methodsmentioning

confidence: 99%

An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals

Vargas-Lopez

Perez-Ramirez

Valtierra-Rodríguez

et al. 2021

Sensors

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The economic and personal consequences that a car accident generates for society have been increasing in recent years. One of the causes that can generate a car accident is the stress level the driver has; consequently, the detection of stress events is a highly desirable task. In this article, the efficacy that statistical time features (STFs), such as root mean square, mean, variance, and standard deviation, among others, can reach in detecting stress events using electromyographical signals in drivers is investigated, since they can measure subtle changes that a signal can have. The obtained results show that the variance and standard deviation coupled with a support vector machine classifier with a cubic kernel are effective for detecting stress events where an AUC of 0.97 is reached. In this sense, since SVM has different kernels that can be trained, they are used to find out which one has the best efficacy using the STFs as feature inputs and a training strategy; thus, information about model explain ability can be determined. The explainability of the machine learning algorithm allows generating a deeper comprehension about the model efficacy and what model should be selected depending on the features used to its development.

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Section: Methodsmentioning

confidence: 99%

An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals

Vargas-Lopez

Perez-Ramirez

Valtierra-Rodríguez

et al. 2021

Sensors

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show abstract

“…Such versions include linear SVM (Lin-SVM), quadratic SVM (Quad-SVM), fine Gaussian SVM (Fin-Gaus-SVM), medium Gaussian SVM (Med-Gaus-SVM), coarse Gaussian SVM (Cor-Gaus-SVM), cubic SVM (Cub-SVM), cosine KNN (Cos-KNN), coarse KNN (Cor-KNN), and fine KNN (Fin-KNN). The detailed study of SVM versions can be retrieved from [73][74][75][76][77][78][79][80] while the detailed study of KNN versions can be regained from [81][82][83][84][85]. Observing the performance outcomes, Cub-SVM becomes the bestperformed classifier for the selected action datasets.…”

Section: Classificationmentioning

confidence: 99%

Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System Optimization

et al. 2021

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Intelligent visual surveillance systems are attracting much attention from research and industry. The invention of smart surveillance cameras with greater processing power has now been the leading stakeholder, making it conceivable to design intelligent visual surveillance systems. It is possible to assure the safety of people in both homes and public places. This work aims to distinguish the suspicious activities for surveillance environments. For this, a 63 layers deep CNN model is suggested and named "L4-Branched-ActionNet". The suggested CNN structure is centered on the alteration of AlexNet with added four blanched sub-structures. The developed framework is first transformed into a pre-trained framework by conducting its training on an object detection dataset called CIFAR-100 with the SoftMax function. The dataset for suspicious activity recognition is then forwarded to this pretrained model for feature acquisition. The acquired deep features are subjected to feature subset optimization. These extracted features are first coded by applying entropy and then an ant colony system (ACS) is utilized on the entropy-based coded features for optimization. The configured features are then fed into numerous SVM and KNN based classification models. The cubic SVM has the highest efficiency scores, with a performance of 0.9924 in terms of accuracy. The proposed model is also validated on the Weizmann action dataset and attained an accuracy of 0.9796. The successful findings indicate the suggested work's soundness.

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“…EEG data were acquired from 20 healthy subjects at Bio-Medical Laboratory of NITTTR Chandigarh, India [22], [23]. After the raw EEG signal acquisition, EEG data was passed through a 4 th order band-pass Butterworth filter (8Hz to the 30Hz range) for noise elimination [24]. Further, a notch filter of cut off frequency 50 Hz was employed for power line interference.…”

Section: Eeg Data Acquisitionmentioning

confidence: 99%

Motor-Imagery EEG Signals Classificationusing SVM, MLP and LDA Classifiers

Narayan¹

2021

TURCOMAT

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Electroencephalogram (EEG)signals based brain-computer interfacing (BCI) is the current technology trends in the field of rehabilitation robotic. This study compared the performance of support vector machine (SVM), linear discriminant analysis (LDA) and multi-layer perceptron (MLP) classifier with the combination of eight different features as a feature vector. EEG data were acquired from 20 healthy human subjects with predefined protocols. After the EEG signals acquisition, it was pre-processed followed by feature extraction and classification by using SVM MLP and LDA classifiers. The results exhibited that the SVM method was the best approach with 98.8% classification accuracy followed by MLP classifier. Finally, the SVM classifier and Arduino Mega controller was employed for offline controlling of the gripper of the robotic arm prototype. The finding of this study may be useful for online controlling as well as multi-degree of freedom with multi-class EEG dataset.

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Discrete Wavelet Packet based Elbow Movement classification using Fine Gaussian SVM

Cited by 21 publications

References 9 publications

An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals

An Explainable Machine Learning Approach Based on Statistical Indexes and SVM for Stress Detection in Automobile Drivers Using Electromyographic Signals

Suspicious Activity Recognition Using Proposed Deep L4-Branched-Actionnet With Entropy Coded Ant Colony System Optimization

Motor-Imagery EEG Signals Classificationusing SVM, MLP and LDA Classifiers

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