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Electromyogram signals have been used for various applications in the healthcare sector for developing various methodologies and techniques in rehabilitation and prosthetics. This paper focuses on the use of EMG signals of trans-radial amputees for developing a myoelectric lower limb prosthesis capable of individual finger movement. The aim of this work is to develop proper hardware and software systems for real-time EMG classification. An improved double thresholding method for onset and offset detections has been developed to ensure its applicability in real-time. The proposed algorithm has been tested with real-time patient EMG signals using a three-lead electrode system from flexor digitalis region of the hand. Around 3000 samples of usable data corresponding to the flexion of each finger (Thumb-553, Index-655, Middle-723, Ring-720, Little-655) were acquired from 10 healthy subjects. The resultant extracted features were classified using various classifiers (KNN, KNN with PCA and LDA) and a comparison was done between the accuracies acquired from a commonly shared dataset against a subject-specific dataset. A robust onset signal processing algorithm enabled the real-time classification of EMG in noisy environments.

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Thyroid is a butterfly shaped gland located in the neck region. Hormones are secreted by the thyroid gland that is responsible for various functions that maintain metabolism of the body. The variance in secretion of the hormones causes disorders such as Hyperthyroidism or Hypothyroidism. Electroglottography signal is a bio signal which represents the impedance that exist between the glottis regions. The study aims at design and development of an hardware circuit for the acquisition of Electroglottogram signal from normal and thyroid subjects is proposed followed by feature extraction from the acquired bio signal is performed. Further, machine learning classifiers were used to classify the normal and thyroid individuals. This modality of acquisition is non-invasive. Performance evaluation is done by testing various classifiers to study the accuracy. The classifiers tested were Random Forest, Random Tree, Bayes Net, Multilayer Perceptron, Simple Logistic classifier, and One-R classifier. Classifiers such as Random Forest, Random Tree, and Multilayer Perceptron showed high accuracy. The accuracy estimated by these classifiers was tested and its ROC curves with AUC scores were derived. The highest accuracy was reported for Simple Logistic classifier which was about 95.1%. Random Forest and Random Tree reported 93.5% and 91.9% respectively. Similarly, Multilayer Perceptron and Bayes Net gave 93.5% and 91.9%. The One-R classifier algorithm reported the lowest accuracy of 90.3% among the studied classifier algorithms. The ROC-AUC score for the classifiers were also reported to be more than 0.9 which is considered more promising and supports the acquisition and processing methodology. Hence the proposed technique can be efficiently used to diagnose thyroid non-invasively.

We can solve electromagnetic problems using two main mathematical tools: vector calculus and differential equations. These tools command the computational electromagnetic domain. However, these tools are not always needed for the realistic modeling of electromagnetic problems. In reality, we are interested in the measurement of scalar quantities in electromagnetics, not vector quantities. Conventional electromagnetic simulation approaches are proving to be more mathematical than physical. Furthermore, the use of differential equations leads us along a different route for modeling fundamental physics. Since computers need discrete formulations, we can't directly transform continuous differential equations into numerical algorithms. The algebraic topological method is a direct discrete and computationally ambitious technique that uses only physically measurable scalar quantities. This paper simulates a parallel plate capacitor using global variables and calculating and comparing the potentials with the analytical method. The measured results show a good agreement between the analytical and the algebraic topological methods.