Pratyusha Das

Single channel electrooculogram(EOG) based interface for mobility aid

Human computer interfacing (HCI) technology has emerged as a new pathway towards the improvement of different rehabilitative aids. In this paper, new approach to control the motorized human computer interface using electrooculogram (EOG) is proposed. A mobility interface controlled by eye movements has been developed to help the disabled individuals with motor impairment who cannot even speak. Electrooculogram(EOG) is the potential generated in due the movement of the eyeballs and can be acquired from the surrounding region of eye socket. The signal is easy to acquire noninvasively and has a simple pattern. A low cost data acquisition system for EOG is designed. Horizontal electrooculographic signal is recorded by placing electrodes at the outer region of the orbit of eyes, and a reference electrode at neck. Using different combinations of eye movements in right and left direction a simple control strategy has been developed to drive motors. Control signals have been first generated using 8051 microcontroller. To meet the problems occurred while using 8051, ATMEGA microcontroller has been adapted. Directional movements of a small prototype of mobility aid (a toy car) with DC motors in right, left and forward is controlled and start and stop of movement is also implemented with ATMEGA. These control signals can be further used to command rehabilitative assistive device with eye movement sequences.

Secondary factor induced stock index time-series prediction using Self-Adaptive Interval Type-2 Fuzzy Sets

The paper introduces an alternative approach to time-series prediction for stock index data using Interval Type-2 Fuzzy Sets. The work differs from the existing research on time-series prediction by the following counts. First, partitions of the time-series, obtained by fragmenting its valuation space over disjoint equal sized intervals, are represented by Interval Type-2 Fuzzy Sets (or Type-1 fuzzy sets in absence of sufficient data points in the partitions). Second, an Interval Type-2 (or type-1) fuzzy reasoning is performed using prediction rules, extracted from the (main factor) time-series. Third, a type-2 (or type-1) centroidal defuzzification is undertaken to determine crisp measure of inferences obtained from the fired rules, and lastly a weighted averaging of the defuzzified outcomes of the fired rules is performed to predict the time-series at the next time point from its current value. Besides the above three main prediction steps, the other issues considered in the paper include: (i) employing a new strategy to induce the main factor time-series prediction by its secondary factors (other reference time-series) and (ii) self-adaptation of membership functions to properly tune them to capture the sudden changes in the main-factor time-series. Performance analysis undertaken reveals that the proposed prediction algorithm outperforms existing algorithms with respect to root mean-square error by a large margin (?23%). A statistical analysis undertaken with paired t-test confirms that the proposed method is superior in performance at 95% confidence level to most of the existing techniques with root mean square error as the key metric.

Arduino based multi-robot stick carrying by Artificial Bee Colony optimization algorithm

Cooperation of the multi-robots is an upcoming appealing area of research in the field of robotics. In this paper, two arduino based mobile robots are carrying a stick by cooperation towards their goal avoiding obstacles. The path planning algorithm is designed with the help of Artificial Bee Colony Optimization (ABCO) algorithm which chooses the optimized path by minimizing the distance between the robots and maximizing the distance from the obstacles. The ultrasonic sensors, encoder, 3-axis compass and XBee module are embedded in the robot to detect obstacle in the path of the robots, the distance travelled by the robot, calculate the direction (coordinate) of the robot and to communicate with other robots respectively. We have also designed our algorithm with the help of differential evolutionary (DE) algorithm. Analyzing the performance of ABCO and DE algorithms, it is observed that ABCO outperforms DE in real-robot experiment with respect to distance metric.

Data-point and feature selection of motor imagery EEG signals for neural classification of cognitive tasks in car-driving

This paper proposes novel algorithms for data-point and feature selection of motor imagery electroencephalographic signals for classifying motor plannings involved in car- driving including braking, acceleration, left steering control and right steering control. Variants of neural network classifiers such as linear support vector machines, and kernel-based support vector machines including radial basis function kernel, polynomial kernel and hyperbolic kernel have been applied to classify the various cognitive tasks. Experimental finding reveals that the proposed data-point and feature selection technique altogether provides better classification accuracies (more than 88%) for all cognitive tasks in comparison with using factor analysis for data-point reduction and feature selection. It is also observed that power spectral density and discrete wavelet transform features are selected among the list of electroencephalographic features for holding the top two rank values for cognitive task classification during car-driving. From the experimental result, it is confirmed that support vector machines with radial basis function along with power spectral density outperforms the remaining feature-classifier pairs in terms of average classification accuracy.

Real Time Electro-Oculogram Driven Rehabilitation Aid

Human computer interfacing technology based rehabilitation aids have shown a new horizon towards intelligent systems to improve the quality of life of physically challenged people. Research is going on to utilize biosignals to interface the movement based signals with machines. Electro-oculogram is the signal to detect eye ball movements and can be used to control mobility aids. Electro-oculogram is the potential difference around the eyes due to movement of the eye balls in different directions. In this study an acquisition system for electro-oculogram is designed to collect the desired signal with low noise and then signal processing is done for control application. The contribution of this paper lies in the development of two new strategies to use electrooculographic signal based control of motors in real time.

Type 2 fuzzy induced person identification using Kinect sensor

Automatic person recognition problem draws significant popularity in the last decade in the field of human-robot interaction. This paper introduces a novel approach to identify a person automatically whom the robot has already met, based on its walking pattern as gait is a unique characteristic for every individual. Here, the Kinect sensor is used to record the gait pattern of a person by storing 20 3-D joint coordinates in each time stamps. The features like joint angle and joint length are obtained from each complete walk cycle. Among all these features, most significant features are selected using principal component analysis. Later, these features are fuzzified constructing a Gaussian membership function with the mean and standard deviation of each feature at different gait cycle. An Interval Type-2 membership is constructed with all these membership values for a particular feature in different trials. 10 walking data set of 10 subjects are processed here. Now, when any person out of these 10 persons is walking in front of Kinect, features are calculated. But as more than one feature value for a particular feature (each feature corresponds to each gait cycle in a complete walking task) is obtained, mean of all these values for a particular feature is considered as measurement point. Defuzzification is done using t-norm and average operators. The person corresponding to highest defuzzified value is considered as the unknown person. The classification accuracy is 89.667%. The proposed method is also compared with few existing person identification techniques and the results obtained prove the superiority of the proposed algorithm.

Online Template Matching Using Fuzzy Moment Descriptor

In this paper a real-time template matching algorithm has been developed using Fuzzy (Type-1 Fuzzy Logic) approach. The Fuzzy membership-distance products, called Fuzzy moment descriptors are estimated using three common image features, namely edge, shade and mixed range. Fuzzy moment description matching is used instead of existing matching algorithms to reduce real-time template matching time. In the proposed matching technique template matching is done invariant to size, rotation and color of the image. For real time application the same algorithm is applied on an Arduino based mobile robot having wireless camera. Camera fetches frames online and sends them to a remote computer for template matching with already stored template in the database using MATLAB. The remote computer sends computed steering and motor signals to the mobile robot wirelessly, to maintain mobility of the robot. As a result, the mobile robot follows a particular object using proposed template matching algorithm in real time.

Electrooculogram Based Online Control Signal Generation for Wheelchair

Human computer interfacing technology has paved a new way in providing services to people with special needs (i.e., elderly, people with impairments, or people with disabilities). Research is going on to interface the movement based biosignals with machines. Electrooculogram is the signal produced due to eye ball movements and can be used to control mobility aids. Electrooculogram is the potential difference around the eyes due to movement of the eye balls in different directions. In this study an acquisition system for electrooculogram is designed to collect the desired signal with low noise and then signal processing is done for control application. The contribution of this paper lies in the development of two new strategies of electrooculographic signal based control of motors in real time.

Evolutionary approach for designing protein-protein interaction network using artificial bee colony optimization

In the new paradigm for studying biological phenomena represented by System Biology, cellular components are not considered in isolation but as forming complex networks of relationships. Protein-Protein Interaction (PPI) networks are among the first objects studied from this new point of view. The paper addresses an interesting approach to protein-protein interaction problem using Artificial Bee Colony (ABC) optimization algorithm. In this work, PPI is formulated as an optimization problem. The binding energy and mismatch in phylogenetic profiles of two bound proteins are used as a scoring function for the solutions. Results are demonstrated for three different networks both numerically and pictorially. Experimental results reveal that the proposed method outperforms Differential Evolution (DE) based PPI network design method considering the intra- and inter-molecular energies of the evolved molecules and the phylogenetic profiles of the proteins in the network.

Fuzzy rule enhanced support vector machines for classification of emotions from brain networks

Support Vector Machines are widely accepted in the field of pattern recognition because of their superiority in performing supervised classification. It is known that all kernel parameters may be used for classification more-or-less precisely (giving rise to vagueness) and also for the same classification problem, there are a number of kernel parameters which give the best accuracy (giving rise to uncertainty). Hence, an appropriate scheme of representing best suited kernel parameters for a given classification problem requires an Interval-type 2 approach. In this work the authors introduce a fuzzy rule-based kernel parameter selection technique which is based on the variability (inter-class and intra-class scatter) of the dataset to be classified. A significant advantage of using the proposed fuzzy kernel parameter selection technique is that one can identify the kernel parameter which has least curvature and hence avoid over fitting. The introduced method of kernel parameter selection is tested in an emotion recognition problem by brain network analysis. Experiments undertaken indicate that selection of appropriate kernel parameters can increase accuracy up to 30%.