A Low Cost ECG Biometry System Based on an Ensemble of Support Vector Machine Classifiers

2016 ◽  
pp. 425-433 ◽  
Author(s):  
Luca Mesin ◽  
Alejandro Munera ◽  
Eros Pasero
Keyword(s):  
Support Vector Machine ◽  
Low Cost ◽  
Support Vector

Machine Learning Models for Finger Bend Evaluation using Implemented Low cost Flex Sensor

2021 ◽  
Vol 9 (VI) ◽  
pp. 3605-3611
Author(s):  
Pratyush Kaware
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Low Cost ◽  
Learning Algorithms ◽  
Cost Effective ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Models ◽  
Machine Learning Models

In this paper a cost-effective sensor has been implemented to read finger bend signals, by attaching the sensor to a finger, so as to classify them based on the degree of bent as well as the joint about which the finger was being bent. This was done by testing with various machine learning algorithms to get the most accurate and consistent classifier. Finally, we found that Support Vector Machine was the best algorithm suited to classify our data, using we were able predict live state of a finger, i.e., the degree of bent and the joints involved. The live voltage values from the sensor were transmitted using a NodeMCU micro-controller which were converted to digital and uploaded on a database for analysis.

scholarly journals Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3144 ◽  
Author(s):  
Sherif Said ◽  
Ilyes Boulkaibet ◽  
Murtaza Sheikh ◽  
Abdullah S. Karar ◽  
Samer Alkork ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Experimental Test ◽  
Support Vector Machine Classifier ◽  
Low Cost ◽  
Support Vector ◽  
Wide Range ◽  
3D Printed ◽  
Semg Signals

In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.

scholarly journals IoT-based Estimation System for Microcystis aeruginosa Cyanobacteria in Laguna de Bay using an Arduino-controlled Spectrophotometric Device

2021 ◽  
Vol 325 ◽  
pp. 04007
Author(s):  
Lawrence D. Alejandrino ◽  
Jessica Joy D. Jocson ◽  
Micah Romina R. Mirarza ◽  
Ericson D. Dimaunahan ◽  
Ramon G Garcia ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Linear Regression ◽  
Potable Water ◽  
Low Cost ◽  
High Accuracy ◽  
The Philippines ◽  
Support Vector ◽  
Svm Algorithm ◽  
Laguna De Bay ◽  
Estimation System

Laguna de Bay, the largest freshwater lake in the Philippines, provides livelihood to the fishermen and serves as a source of potable water to the locals. However, freshwater quality has degraded, whereas one of the main contributors are Cyanobacteria that produce cyanotoxins. Existing studies that uses a similar device are either too expensive or too bulky. The purpose of this study is to estimate the cyanobacteria concentration by using a low-cost 16-channel spectrophotometric device to determine the level of severity efficiently. Using Linear Regression, the dataset is modelled by the algorithm to estimate the number of cyanobacteria present on the water sample, while Support Vector Machine (SVM) algorithm for severity level classifier. This study achieved high accuracy in estimating the cyanobacteria using linear regression and classifying the level of severity by support vector machine.

BI-FEATURE VERIFICATION FOR PALMPRINT IMAGES CAPTURED IN PEGLESS SCENARIOS

2013 ◽  
Vol 27 (05) ◽  
pp. 1356007 ◽  
Author(s):  
CHIH-LUNG LIN ◽  
HSU-YUNG CHENG ◽  
KUO-CHIN FAN ◽  
CHUN-WEI LU ◽  
CHANG-JUNG JUAN ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Access Control ◽  
Control Systems ◽  
Low Cost ◽  
Experimental Results ◽  
Support Vector ◽  
Low Resolution ◽  
Classification Rate ◽  
Histogram Of Oriented Gradient ◽  
Feature Information

This paper presents a reliable and robust palmprint verification approach that involves using a bi-feature, biometric, palmprint feature-point number (FPN) and a histogram of oriented gradient (HOG). The bi-feature was fused and verified using a support vector machine (SVM) at the feature level. The approach has the advantages of capturing palm images in pegless scenarios with a low cost and low-resolution (100 dpi) digital scanner, and one sensor can capture palmprint bi-feature information. The low-resolution images result in a smaller database. Nine thousand palmprint images were collected from 300 people to verify the validity of the proposed approach. The results showed an accurate classification rate of 99.04%. The experimental results demonstrated that the proposed approach is feasible and effective in palmprint verification. Our findings will help extend palmprint verification technology to security access control systems.

Palmprint Verification for Images Captured in Peg-Less Scenarios

2013 ◽  
Vol 284-287 ◽  
pp. 3178-3183 ◽  
Author(s):  
Chun Wei Lu ◽  
Chih Lung Lin ◽  
Kuo Chin Fan ◽  
Hsu Yung Cheng ◽  
Chang Jung Juan
Keyword(s):  
Support Vector Machine ◽  
Access Control ◽  
Control Systems ◽  
Low Cost ◽  
Experimental Results ◽  
Feature Point ◽  
Support Vector ◽  
Low Resolution ◽  
Point Number ◽  
Database Size

This paper presents a reliable and robust palmprint verification approach using palmprint feature point number (FPN). The feature verified by support vector machine (SVM). It has the advantages of capturing palm images in peg-less scenarios and by a low cost and low-resolution (100dpi) digital scanner. The low-resolution images lead a less database size. There are 4800 palmprint images were collected from 160 persons to verify the validity of the proposed approach and the results are satisfactory with 98.30% classification correct rate (CCR). Experimental results demonstrate that the proposed approach is feasible and effective in palmprint verification. Our findings will help to extend palmprint verification technologies to security access control systems.

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 Rapid and Low-Cost Detection of Thyroid Dysfunction Using Raman Spectroscopy and an Improved Support Vector Machine

2018 ◽  
Vol 10 (6) ◽  
pp. 1-12 ◽  
Author(s):  
Xiangxiang Zheng ◽  
Guodong Lv ◽  
Guoli Du ◽  
Zhengang Zhai ◽  
Jiaqing Mo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Raman Spectroscopy ◽  
Thyroid Dysfunction ◽  
Low Cost ◽  
Support Vector ◽  
Improved Support Vector Machine

scholarly journals Measurement of food colour in L*a*b* units from RGB digital image using least squares support vector machine regression

2015 ◽  
Vol 46 (4) ◽  
pp. 138 ◽  
Author(s):  
Roberto Romaniello ◽  
Alessandro Leone ◽  
Giorgio Peri
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Low Cost ◽  
Digital Camera ◽  
Support Vector ◽  
Feed Forward Neural Network ◽  
Data Set ◽  
Food Colour ◽  
Input Variables ◽  
Validation Set

The aim of this work is to evaluate the potential of least squares support vector machine (LS-SVM) regression to develop an efficient method to measure the colour of food materials in L*a*b* units by means of a computer vision systems (CVS). A laboratory CVS, based on colour digital camera (CDC), was implemented and three LS-SVM models were trained and validated, one for each output variables (L*, a*, and b*) required by this problem, using the RGB signals generated by the CDC as input variables to these models. The colour target-based approach was used to camera characterization and a standard reference target of 242 colour samples was acquired using the CVS and a colorimeter. This data set was split in two sets of equal sizes, for training and validating the LS-SVM models. An effective two-stage grid search process on the parameters space was performed in MATLAB to tune the regularization parameters γ and the kernel parameters σ<sup>2</sup> of the three LS-SVM models. A 3-8-3 multilayer feed-forward neural network (MFNN), according to the research conducted by León <em>et al.</em> (2006), was also trained in order to compare its performance with those of LS-SVM models. The LS-SVM models developed in this research have been shown better generalization capability then the MFNN, allowed to obtain high correlations between L*a*b* data acquired using the colorimeter and the corresponding data obtained by transformation of the RGB data acquired by the CVS. In particular, for the validation set, R<sup>2</sup> values equal to 0.9989, 0.9987, and 0.9994 for L*, a* and b* parameters were obtained. The root mean square error values were 0.6443, 0.3226, and 0.2702 for L*, a*, and b* respectively, and the average of colour differences ΔE<sub>ab</sub> was 0.8232±0.5033 units. Thus, LS-SVM regression seems to be a useful tool to measurement of food colour using a low cost CVS.

Device-Independent Discrimination of Falsified Amoxicillin Capsules Using Heterogeneous Near-Infrared Spectroscopic Devices for Training and Testing of a Support Vector Machine

2021 ◽  
pp. 000370282199965
Author(s):  
Yusuke Hattori ◽  
Yuka Hoshi ◽  
Yasunori Ichimura ◽  
Yasuo Sugiura ◽  
Makoto Otsuka
Keyword(s):  
Support Vector Machine ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Low Cost ◽  
Discrimination Performance ◽  
Principal Component ◽  
Support Vector ◽  
Reference Library ◽  
Infrared Spectrophotometer

The objective of this work is to demonstrate the potential of near-infrared spectroscopy for common screening of falsified medicines in the field by means of a device-independent universal discrimination approach. In order to provide a useful discrimination tool to protect people from low-quality medical products, not only is a low-cost and portable screening device necessary, but a reference library is also essential. The authors believe that a device-dependent reference library inhibits near-infrared spectroscopy from becoming a popular screening tool. In this study, to develop a device-independent method, discrimination performance is evaluated using different devices for training and testing. The training data sets for the reference library were prepared using a bench-top Fourier transform near-infrared spectrophotometer, and predictive discrimination was performed using the spectral data by a low-cost and portable wavelength dispersive near-infrared spectrophotometer. A near-infrared spectrum-based support vector machine was used for these purposes, but the screening resulted in low accuracy thought to be caused by the intrinsically device-dependent features of the spectra data. Thus, principal component analysis was performed to collect the proper components to discriminate low-quality products from standard products. The principal component score-based support vector machine was able to produce highly accurate results, identifying falsified products with no false positive cases.

Sign in / Sign up

Export Citation Format

Share Document