Gaits Classification of Normal vs. Patients by Wireless Gait Sensor and Support Vector Machine (SVM) Classifier

2017 ◽  
Vol 5 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Taro Nakano ◽  
B.T. Nukala ◽  
J. Tsay ◽  
Steven Zupancic ◽  
Amanda Rodriguez ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Human Subjects ◽  
Wearable Sensors ◽  
Normal Subjects ◽  
Support Vector ◽  
Svm Classifier ◽  
Time Dynamic ◽  
Linear Svm ◽  
Dynamic Gait

Due to the serious concerns of fall risks for patients with balance disorders, it is desirable to be able to objectively identify these patients in real-time dynamic gait testing using inexpensive wearable sensors. In this work, the authors took a total of 49 gait tests from 7 human subjects (3 normal subjects and 4 patients), where each person performed 7 Dynamic Gait Index (DGI) tests by wearing a wireless gait sensor on the T4 thoracic vertebra. The raw gait data is wirelessly transmitted to a near-by PC for real-time gait data collection. To objectively identify the patients from the gait data, the authors used 4 different types of Support Vector Machine (SVM) classifiers based on the 6 features extracted from the raw gait data: Linear SVM, Quadratic SVM, Cubic SVM, and Gaussian SVM. The Linear SVM, Quadratic SVM and Cubic SVM all achieved impressive 98% classification accuracy, with 95.2% sensitivity and 100% specificity in this work. However, the Gaussian SVM classifier only achieved 87.8% accuracy, 71.7% sensitivity, and 100% specificity. The results obtained with this small number of human subjects indicates that in the near future, the authors should be able to objectively identify balance-disorder patients from normal subjects during real-time dynamic gaits testing using intelligent SVM classifiers.

scholarly journals A real-time drowsiness and fatigue recognition using support vector machine

2020 ◽  
Vol 9 (4) ◽  
pp. 584
Author(s):  
Nur Nabilah Abu Mangshor ◽  
Iylia Ashiqin Abdul Majid ◽  
Shafaf Ibrahim ◽  
Nurbaity Sabri
Keyword(s):  
Support Vector Machine ◽  
Real Time ◽  
Recognition Task ◽  
Support Vector ◽  
Svm Classifier ◽  
Face Expression ◽  
Exact Position ◽  
Sobel Edge Detection ◽  
Processing Techniques ◽  
Current Accuracy

<p>A drowsiness and fatigue problems among the drivers are the main factor that contributes to road accidents. These problems are vital to be resolved as they could contribute to damage of road facilities, vehicles and most importantly the loss of lives. In avoiding these matters, a proper mechanism is needed to alert the driver to stay awake throughout the driving journey. Thus, this study proposed a real-time prototype for recognizing the drowsiness and fatigue face expression of the driver. The methodology of this study involves facial features detection using Viola-Jones algorithm to detect the exact position of both left and right eyes and mouth. Next, based on the detected eyes and mouth beforehand, the segmentation processes performed on both eyes and mouth using Sobel edge detection to obtain facial regions. The feature extraction phase is conducted using shape-based feature to obtain the extraction values. Support vector machine (SVM) classifier is deployed for the recognition task. A total of 100 images are used during the testing stages. This study achieved a competetive result of 90.00% of accuracy. Yet, hybridization or integration of more image processing techniques will be performed in the future to improve the current accuracy obtained.</p>

Real-Time EEG Device for Epilepsy Detection Using Wavelet Transform and Support Vector Machine

2021 ◽  
pp. 104-135
Author(s):  
Sharad Sarjerao Jagtap ◽  
Rajesh Kumar M.
Keyword(s):  
Support Vector Machine ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Real Time ◽  
Low Cost ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrete Wavelet ◽  
Feature Extraction Technique

This chapter gives an effective and efficient technique that can detect epilepsy in real time. It is low cost, low power, and real-time devices that can easily detect epilepsy. Along with EEG device, one can upgrade with GSM module to alert the doctors and parents of patients about its occurrence to prevent a sudden fall, which may cause injury and death. The accuracy of this EEG device depends on the quality of feature extraction technique and classification algorithm. In this chapter, support vector machine (SVM) is used as a classifier. Wavelet transform gives feature extraction, which helps to train data and to detect normal or seizure patients. Discrete wavelet transform (DWT) decomposes the signals into three decomposition levels. In this detection, mean, median, and non-linear parameter entropy were calculated for every sub-band as key parameters. The extracted features are then applied to SVM classifier for the classification. Better accuracy of classification is obtained using wavelet and SVM classifier.

scholarly journals A new model for iris data set classification based on linear support vector machine parameter's optimization

2020 ◽  
Vol 10 (1) ◽  
pp. 1079
Author(s):  
Zahraa Faiz Hussain ◽  
Hind Raad Ibraheem ◽  
Mohammad Alsajri ◽  
Ahmed Hussein Ali ◽  
Mohd Arfian Ismail ◽  
...  
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Data Set ◽  
Principle Components Analysis ◽  
Web Text Mining ◽  
Linear Svm ◽  
Iris Data

Data mining is known as the process of detection concerning patterns from essential amounts of data. As a process of knowledge discovery. Classification is a data analysis that extracts a model which describes an important data classes. One of the outstanding classifications methods in data mining is support vector machine classification (SVM). It is capable of envisaging results and mostly effective than other classification methods. The SVM is a one technique of machine learning techniques that is well known technique, learning with supervised and have been applied perfectly to a vary problems of: regression, classification, and clustering in diverse domains such as gene expression, web text mining. In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.

scholarly journals Automated Coronary Artery Disease Detection using RQA Features and Quadratic Support Vector Machine

2020 ◽  
Vol 8 (5) ◽  
pp. 2799-2804
Keyword(s):  
Coronary Artery Disease ◽  
Support Vector Machine ◽  
Coronary Artery ◽  
Cardiac Activity ◽  
Normal Subjects ◽  
Support Vector ◽  
Svm Classifier ◽  
Normal Heart ◽  
Artery Disease ◽  
Heart Condition

One of the major causes of death globally due to heart disease is the coronary artery disease (CAD). Due to CAD the blood flow to the cardiac muscle is reduced. The progression of this process eventually causes Myocardial Infarction (MI) that result in sudden death. Hence the detection of CAD at early phase is essential. The electrocardiogram (ECG) is mainly used to capture the abnormal cardiac activity for CAD. But the difficulties in manual interpretation of ECG signal leads to error in CAD detection. To overcome the difficulty in CAD diagnostic task, we have proposed a computer aided methodology using the heart rate variability signal (HRV) for auto diagnosis of CAD and Normal heart condition. The hidden characteristics of HRV signal are identified through Recurrence Plot (RP) and the hidden information is quantified by Recurrence quantification analysis (RQA). The extracted RQA based nonlinear features are analyzed for their clinical significance. The set the effective features are used for classification and subjected to three types of Support vector machine (SVM) classifier to discriminate CAD and the normal heart condition. The ECG database of CAD and normal subjects are taken from Physio.net database to obtain the experimental results. The highest diagnostic ability of the classifier is obtained by quadratic SVM with the accuracy of 98.83% where as the linear and cubic SVM classifier provide 97.22 % and 98.37 % classification accuracy respectively.

scholarly journals Analog Circuit Fault Diagnosis Based on Support Vector Machine Classifier and Fuzzy Feature Selection

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1496
Author(s):  
Hao Liang ◽  
Yiman Zhu ◽  
Dongyang Zhang ◽  
Le Chang ◽  
Yuming Lu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Mutual Information ◽  
Analog Circuit ◽  
Fault Classification ◽  
Support Vector ◽  
Svm Classifier ◽  
Fault Parameters ◽  
Diagnosis Method ◽  
Circuit Fault Diagnosis

In analog circuit, the component parameters have tolerances and the fault component parameters present a wide distribution, which brings obstacle to classification diagnosis. To tackle this problem, this article proposes a soft fault diagnosis method combining the improved barnacles mating optimizer(BMO) algorithm with the support vector machine (SVM) classifier, which can achieve the minimum redundancy and maximum relevance for feature dimension reduction with fuzzy mutual information. To be concrete, first, the improved barnacles mating optimizer algorithm is used to optimize the parameters for learning and classification. We adopt six test functions that are on three data sets from the University of California, Irvine (UCI) machine learning repository to test the performance of SVM classifier with five different optimization algorithms. The results show that the SVM classifier combined with the improved barnacles mating optimizer algorithm is characterized with high accuracy in classification. Second, fuzzy mutual information, enhanced minimum redundancy, and maximum relevance principle are applied to reduce the dimension of the feature vector. Finally, a circuit experiment is carried out to verify that the proposed method can achieve fault classification effectively when the fault parameters are both fixed and distributed. The accuracy of the proposed fault diagnosis method is 92.9% when the fault parameters are distributed, which is 1.8% higher than other classifiers on average. When the fault parameters are fixed, the accuracy rate is 99.07%, which is 0.7% higher than other classifiers on average.

scholarly journals A Roller Bearing Fault Diagnosis Method Based on LCD Energy Entropy and ACROA-SVM

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
HungLinh Ao ◽  
Junsheng Cheng ◽  
Kenli Li ◽  
Tung Khac Truong
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Roller Bearing ◽  
Local Characteristic ◽  
Support Vector ◽  
Svm Classifier ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Energy Entropy

This study investigates a novel method for roller bearing fault diagnosis based on local characteristic-scale decomposition (LCD) energy entropy, together with a support vector machine designed using an Artificial Chemical Reaction Optimisation Algorithm, referred to as an ACROA-SVM. First, the original acceleration vibration signals are decomposed into intrinsic scale components (ISCs). Second, the concept of LCD energy entropy is introduced. Third, the energy features extracted from a number of ISCs that contain the most dominant fault information serve as input vectors for the support vector machine classifier. Finally, the ACROA-SVM classifier is proposed to recognize the faulty roller bearing pattern. The analysis of roller bearing signals with inner-race and outer-race faults shows that the diagnostic approach based on the ACROA-SVM and using LCD to extract the energy levels of the various frequency bands as features can identify roller bearing fault patterns accurately and effectively. The proposed method is superior to approaches based on Empirical Mode Decomposition method and requires less time.

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