Learn More
The present work proposes an artificial system capable of preventing Computer Vision Syndrome from the analysis of eye movements. Ocular data is recorded using an Electrooculogram signal acquisition system developed in the laboratory. Wavelet detail coefficients obtained using Haar and Daubechies order 4 mother wavelets are used as signal features. From the(More)
A simple method to detect gestures revealing muscle and joint pain is described in this paper. Kinect Sensor is used for data acquisition. This sensor only processes twenty joint coordinates in three dimensional space for feature extraction. The recognition part is achieved using a neural network optimized by Levenberg-Marquardt learning rule. A high(More)
The MnFe2O4 nanoparticle has been among the most frequently chosen systems due to its diverse applications in the fields ranging from medical diagnostics to magnetic hyperthermia and site-specific drug delivery. Although numerous efforts have been directed in the synthesis of monodisperse MnFe2O4 nanocrystals, unfortunately, however, studies regarding the(More)
The aim of this novel work is to recognize 12 health care linked gestures from young individuals of 20-40 years of age group. Due to constant sitting in a specific posture for deskbound jobs, functioning of joints and muscles of persons are deteriorated. The scope of this work is to recognize the early stage symptoms of those physical disorders and notify(More)
The emergence of brain-computer interfacing has made the control of robots through thought a reality. Such real-time application calls for fast processing and accurate classification of brain signals. In this paper, we address the two-level classification of motor imagery signals, where the user differentiates between clockwise/ counter-clockwise movement(More)
In this paper a system for detecting the possibility of eye dystonia, a neural disorder that causes a person to blink excessively, by eye movement analysis is proposed. The designed system counts the number of blinks for a particular time interval and thus detecting the risk of eye dystonia. Electrooculogram (EOG) signal is recorded to collect eye movement(More)
Selection of relevant features is an open problem in Brain-computer interfacing (BCI) research. Sometimes, features extracted from brain signals are high dimensional which in turn affects the accuracy of the classifier. Selection of the most relevant features improves the performance of the classifier and reduces the computational cost of the system. In(More)
In this work, we analyse the Electroencephalogram (EEG) and tactile signals acquired during dynamic exploration of objects of seven different geometric shapes and observe that classification performance using features from both the domains together is better than using the either alone. Classification is done by Support Vector Machine and Naïve Bayesian(More)