Ajat Shatru Arora

Multi-Class SVM Classification of Surface EMG Signal for Upper Limb Function

Electromyography (EMG) signal is electrical manifestation of neuromuscular activation that provides access to physiological processes which cause the muscle to generate force and produce movement and allow us to interact with the world. In this paper, an identification of six degree of freedom for evaluating and recording physiologic properties of muscles of the forearm at rest and while contracting is presented. The first step of this method is to analyze the surface EMG signal from the subject’s forearm using wavelet packet transform and extract features using the singular value decomposition. In this way, a new feature space is generated from wavelet packet coefficients. The second step is to import the feature values into multi class Support Vector Machine as a classifier, to identify six degree of freedom viz. open to close, close to open, supination, pronation, flexion and extension.

Analysis of EMG signal using wavelet coefficients for upper limb function

The amplitude of the surface electromyogram (sEMG) is frequently used as the control input to myoelectric prostheses, as a measure of muscular effort and has also been investigated as an indicator of muscle force. To cope with the non stationary property of sEMG, features were extracted using wavelet packet transform. The wavelet packet transform provide an effective representation for multi class Support Vector Machine(SVM), when they are subjected to dimensionality reduction by wavelet packet energy and singular value decomposition.

Paranasal sinusitis detection using thermal imaging

This paper focuses on the identification of paranasal sinusitis using thermal imaging. Thermal imaging is a non-invasive technique which provides a 2D temperature profile of a body under measurement. The experiments have been performed by IR camera on eight subjects of different age group under consideration of constant ambient temperature and resting position. The Region of Interest for imaging is human face. The average temperature of different parts of a face is measured by circle and polygon analysis. The line analysis is performed horizontally and vertically across the nose to show the temperature variations between nose and adjacent parts to it. Image thresholding is done by using Otsus method to differentiate between normal and sinusitis affected patients. The preliminary investigation results show encouraging signs for use of thermal imaging for paranasal sinusitis.

Efficient load forecasting optimized by fuzzy programming and OFDM transmission

Today, it is very important for developed and developing countries to consume electricity more efficiently. Though developed countries do not want to waste electricity and developing countries cannot waste electricity. This leads to the concept: load forecasting. This paper is written for the short-term load forecasting on daily basis, hourly, or half-hourly basis or real time load forecasting. But as we move from daily to hourly basis of load forecasting, the error of load forecasting increases. The analysis of this paper is done on previous years load data records of an engineering college in India using the concept of fuzzy methods. The analysis has been done on Mamdani-type membership functions and OFDM (Orthogonal Frequency Division Multiplexing) transmission scheme. To reduce the error of load forecasting, fuzzy method has been used with Artificial Neural Network (ANN) and OFDM transmission is used to get data from outer world and send outputs to outer world accurately and quickly. The error has been reduced to a considerable level in the range of 2-3%. For further reducing the error, Orthogonal Frequency Division Multiplexing (OFDM) can be used with Reed-Solomon (RS) encoding. Further studies are going on with Fuzzy Regression methods to reduce the error more.

Prediction of BP Reactivity to Talking Using Hybrid Soft Computing Approaches

High blood pressure (BP) is associated with an increased risk of cardiovascular diseases. Therefore, optimal precision in measurement of BP is appropriate in clinical and research studies. In this work, anthropometric characteristics including age, height, weight, body mass index (BMI), and arm circumference (AC) were used as independent predictor variables for the prediction of BP reactivity to talking. Principal component analysis (PCA) was fused with artificial neural network (ANN), adaptive neurofuzzy inference system (ANFIS), and least square-support vector machine (LS-SVM) model to remove the multicollinearity effect among anthropometric predictor variables. The statistical tests in terms of coefficient of determination (R 2), root mean square error (RMSE), and mean absolute percentage error (MAPE) revealed that PCA based LS-SVM (PCA-LS-SVM) model produced a more efficient prediction of BP reactivity as compared to other models. This assessment presents the importance and advantages posed by PCA fused prediction models for prediction of biological variables.

OPTIMIZING THE ROTATION AND TRANSLATION OF FINGERPRINT IMAGES USING GENETIC ALGORITHM

In the minutiae-based fingerprint authentication system, the minutiae in the query image are required to be matched with the minutiae of the reference image that is stored in the database. Ideally, the minutiae extracted from the different impressions of the same fingerprint must match with each other, but practically, because of displacement, rotation, and other linear/ nonlinear distortions, minutiae extracted from different impressions of the same fingerprint do not match with each other. In order to maximize the number of matching minutiae, the alignment of the two fingerprints is required. Correctly aligning the fingerprints requires the translation and rotation to be recovered exactly. In this article, a new genetic-algorithm (GA)-based relative alignment algorithm for the alignment of reference and query fingerprint images is proposed. With the proposed algorithm there is no need to find the reference core or delta point because reliable detection of these reference points is a difficult task. In the proposed algorithm, all the three parameters x, y (translation), and θ (rotational) have been optimized separately. In order to improve the processing time, two acceleration steps have also been implemented. The experiments conducted on the FVC2002/Db1_a database reveal that a high accuracy has been achieved with the proposed method.

Symptom Based Analysis for Carpal Tunnel Syndrome in Assembly Line Workers Using ANOVA with Orthogonal Array

Carpal Tunnel Syndrome (CTS), a type of Repetitive Strain Injury (RSI) is the most commonly work related musculoskeletal disorder (WMSD) that can lead to temporary as well as permanent disabilities. The CTS is marked by pain and paresthesia. Present study has been conducted on the 60 workers comprising of 46 men (mean age of 34.43 ± 8.40 years, range 18-52 years) and 14 women (mean age of 34.78 ± 9.14 years, range 22-50 years) engaged in Ethylene Propylene Diene Monomer (E.P.D.M.) assembly unit. Symptoms present are nocturnal pain, numbness, tingling and low hand grip strength. The study has been done in actual industrial environment through health surveillance, Phalen’s and Tinel’s Tests, hand grip strength tester, and weighing machine. The aim of this study is to do analysis to check the susceptibility of CTS symptoms amongst men and women on the basis of repetitive and non repetitive work. The F-Test and ANOVA using orthogonal array have been applied for statistical data analysis in order to quantify and evaluate the importance of possible symptoms on CTS risk factor. Analysis shows that the women workers are more susceptible to CTS symptoms than their male counterparts. It also reveals that grip strength below 35 Kg and high tingling prevalence ratio are the most alarming symptoms for CTS occurrence among workers in E.P.D.M assembly unit.

Contrast Enhancement Algorithm for IR Thermograms Using Optimal Temperature Thresholding and Contrast Stretching

IR thermography is a noninvasive and non-contact type radiometric technique which creates the 2D thermal images based on infrared radiations. Usually, these are gray-level images which provide poor color contrast. However, various pseudo-coloring algorithms are available to transform these images into RGB space, but contrast enhancement is still required for better visualization of thermograms. In this study, the non-training contrast enhancement algorithm is proposed for IR thermograms. The contrast enhancement in this proposed methodology is achieved by: (i) eliminating the background interference using optimal temperature thresholding and (ii) color enhancement using decorrelation contrast stretching. The performance of proposed methodology has been evaluated based on variations in entropy values. The increasing trend in entropy values indicates the contrast enhancement achieved by using this method.

An automated approach to enhance the thermographic evaluation on orofacial regions in lateral facial thermograms

Segmentation of characteristic facial regions has often been an initial step of thermographic analysis in face recognition and clinical diagnosis. Moreover, fast and accurate thermographic analysis on characteristic areas is highly reliant on segmentation approach. Usually, frontal and lateral projections are used to capture the facial thermograms. The significant role of lateral facial thermography to diagnose the various problems associated with orofacial regions has been remarkable in many studies. So far, no study has presented an automatic approach for the segmentation of characteristic areas in lateral facial thermograms. For this purpose, an automatic approach to segment the characteristic areas in lateral facial thermograms is proposed. The dataset of 140 facial thermograms with 1 left and 1 right lateral view per subject is created. Initially, image binarization is performed using optimal temperature thresholding for better visualization of facial geometry. Then, the automatic localization of characteristic points is performed at two levels, based on (a) geometrical features of the face, and (b) local thermal patterns. At last, the characteristic points and auxiliary points are used to segment the characteristic areas in the orofacial region of the face. To evaluate the segmentation performance of the proposed methodology, the automatically localized characteristic points are compared with manually marked using Euclidean distance based comparison criterion. With the localization error δch_pt≤0.05, the proposed automatic approach shows 92.04% of overall localization accuracy and 85% of overall segmentation accuracy. The key conclusion is that the proposed algorithm can potentially automate the process of thermographic analysis on characteristic areas to assist the diagnosis of problems in the orofacial region.

Comparative Analysis of Hybrid Models for Prediction of BP Reactivity to Crossed Legs

Crossing the legs at the knees, during BP measurement, is one of the several physiological stimuli that considerably influence the accuracy of BP measurements. Therefore, it is paramount to develop an appropriate prediction model for interpreting influence of crossed legs on BP. This research work described the use of principal component analysis- (PCA-) fused forward stepwise regression (FSWR), artificial neural network (ANN), adaptive neuro fuzzy inference system (ANFIS), and least squares support vector machine (LS-SVM) models for prediction of BP reactivity to crossed legs among the normotensive and hypertensive participants. The evaluation of the performance of the proposed prediction models using appropriate statistical indices showed that the PCA-based LS-SVM (PCA-LS-SVM) model has the highest prediction accuracy with coefficient of determination (R2) = 93.16%, root mean square error (RMSE) = 0.27, and mean absolute percentage error (MAPE) = 5.71 for SBP prediction in normotensive subjects. Furthermore, R2 = 96.46%, RMSE = 0.19, and MAPE = 1.76 for SBP prediction and R2 = 95.44%, RMSE = 0.21, and MAPE = 2.78 for DBP prediction in hypertensive subjects using the PCA-LSSVM model. This assessment presents the importance and advantages posed by hybrid computing models for the prediction of variables in biomedical research studies.