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J Dinesh Babu

Bio: J Dinesh Babu is an academic researcher from International Institute of Information Technology, Bangalore. The author has contributed to research in topics: Electroencephalography & Expression (mathematics). The author has an hindex of 1, co-authored 4 publications receiving 8 citations.

Papers
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Proceedings ArticleDOI
28 Dec 2015
TL;DR: Non-linear features have been used here to capture the characteristics of EEG in each case since it portrays the non-linear dependencies of different parameters associated with EEG.
Abstract: Electroencephalographic (EEG) signals are produced in brain due to firing of the neurons. Any anomaly found in the EEG indicates abnormality associated with brain functioning. The efficacy of automated analysis of EEG depends on features chosen to represent the time series, classifier used and quality of training data. In this work, we present automated analysis of EEG time series acquired from two different groups. Non-linear features have been used here to capture the characteristics of EEG in each case since it portrays the non-linear dependencies of different parameters associated with EEG. In the first case, we present the classification between alcoholics and controls. In the second case, we present classification between epileptic and controls. In the classification, we have addressed the issue of quality of training data. In the proposed scheme prior to classification, we filter the training data. This approach led to minimum 10% improvement in the classification accuracy.

6 citations

Book ChapterDOI
19 Dec 2016
TL;DR: In the proposed method, Statistical Moments are utilized and the fact that MGF is able to differentiate between Eyes Open and Eyes Closed states indicate that the two states have different source distribution parameters.
Abstract: Electrical signals generated in the brain, known as Electroencephalographic (EEG) signals, form a non-invasive measure of brain functioning. Baseline states of EEG are Eyes Open (EO) and Eyes Closed (EC) relaxed states. The choice of baseline used in an experiment is of critical importance since they form a reference with which other states are measured. In Brain Machine Interface, it is imperative that the system should be able to distinguish between these states and hence the need for automated classification of EEG baselines. In the proposed method, Statistical Moments are utilized. The Moment Generating Functions (MGFs) obtained using these moments are given as features to SVM and k-NN classifiers resulting in mean accuracies of 86.71% and 86.54%. The fact that MGF is able to differentiate between these states indicate that the two states have different source distribution parameters. A Smirnov test verified that the data of two classes indeed come from different distributions.

4 citations

Proceedings ArticleDOI
01 Sep 2016
TL;DR: In this paper, the intensity values obtained from this tool for four distinct expressions (Joy, Surprise, Sad and Disgust) are used as their feature set for classification and predictive analysis.
Abstract: Bharatnatyam is an ancient Indian Classical Dance form consisting of complex postures and expressions. One of the main challenges in this dance form is to perform expression recognition and use the resulting data to predict the expertise of a test dancer. In this paper, expression recognition is carried out for the 6 basic expressions in Bharatnatyam using iMotions tool. The intensity values obtained from this tool for 4 distinct expressions — Joy, Surprise, Sad and Disgust are being used as our feature set for classification and predictive analysis. The recognition was performed on our own dataset consisting of 50 dancers with varied expertise ratings. Logistic Regression performed the best for Joy, Surprise and Disgust expressions giving an average accuracy of 80.78% whereas Support Vector Machine classifier with Radial Basis kernel function performed best for Sad expression giving an accuracy of 71.36%. A separate analysis on positive and negative emotions is carried out to determine the expertise of each rating on the basis of these emotions.

2 citations

Book ChapterDOI
21 Sep 2016
TL;DR: This object recognition algorithm achieves this goal using just one image per product for training, assuming that the category of the products is known, and outperforms product recognition that was implemented without logo detection.
Abstract: This paper addresses detecting, localizing and recognizing various grocery products in retail store images. Our object recognition algorithm achieves this goal using just one image per product for training, assuming that the category of the products (like cereals, rice, etc.) is known. This algorithm uses logo detection as a precursor to product recognition. So, the first step involves detecting and classifying products, at a broader level, based on their brands. The second step is the finer classification step for recognizing and localizing the exact product label, which involves using colour information. This hierarchical approach limits the confusion in classifying similar looking products and outperforms product recognition that was implemented without logo detection. The algorithm was tested on 80 annotated grocery shelf images containing 238 different products that fall under 3 categories. This facilitates smarter inventory management in retail stores on a large scale and on a day to day basis for the visually impaired people.

2 citations


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Proceedings ArticleDOI
08 Jan 2019
TL;DR: By unfolding the outdated EEG standard bandwidths in a fine-grade equidistant 99-point spectrum the authors can precisely detect alcoholism by using this novel pre-processing step prior to entering a random forests classifier, which substantially outperforms all previous results.
Abstract: We show that by unfolding the outdated EEG standard bandwidths in a fine-grade equidistant 99-point spectrum we can precisely detect alcoholism. Using this novel pre-processing step prior to entering a random forests classifier, our method substantially outperforms all previous results with a balanced accuracy of 97.4 percent. Our machine learning work contributes to healthcare and information systems. Due to its drastic and protracted consequences, alcohol consumption is always a critical issue in our society. Consequences of alcoholism in the brain can be recorded using electroencephalography (EEG). Our work can be used to automatically detect alcoholism in EEG mass data within milliseconds. In addition, our results challenge the medically outdated EEG standard bandwidths.

33 citations

Proceedings Article
31 Aug 2015

21 citations

Journal ArticleDOI
TL;DR: The obtained results support a general acceptance towards ARDTS among the users who are interested in exploring the cutting-edge technology of AR for gaining expertise in a dance skill.
Abstract: The advancement in Computer Vision (CV) has evolved drastically from image processing to object recognition, tracking video, restoration of images, three-dimensional (3D) pose recognition, and emotion analysis These advancements have eventually led to the birth of Augmented Reality (AR) technology, which means embedding virtual objects into the real-world environment The primary focus of this research was to solve the long-term learning retention and poor learning efficiency for mastering a dance skill through the AR technology based on constructivism learning theory, Dreyfus model and Technology Acceptance Model (TAM) The problem analysis carried out in this research had major research findings, in which the retention and learning efficiency of a dance training system were predominantly determined through the type of learning theory adopted, learning environment, training tools, skill acquisition technology and type of AR technique Therefore, the influential factors for the user acceptance of AR-based dance training system (ARDTS) were based on quantitative analysis These influential factors were determined to address the problem of knowledge gap on acceptance of AR-based systems for dance education through self-learning The evaluation and testing were conducted to validate the developed and implemented ARDTS system The Technology Acceptance Model (TAM) as the evaluation model and quantitative analysis was done with a research instrument that encompassed external and internal variables TAM consisted of 37 items, in which six factors were used to assess the new developed ARDTS by the authors and its acceptability among 86 subjects The current study had investigated the potential use of AR-based dance training system to promote a particular dance skill among a sample population with various backgrounds and interests The obtained results support a general acceptance towards ARDTS among the users who are interested in exploring the cutting-edge technology of AR for gaining expertise in a dance skill

13 citations

Journal ArticleDOI
TL;DR: In this article , three tasks requiring different levels of creativity are designed: Max Creative (TMC)-sketching, Less Creative(TLC)-repetitive geometric patterns, and Nil Creative-TNC-tally marks, and three different types of analysis are carried out using the features extracted from three different paradigms: Chaos Analysis, Distribution Analysis, and Statistical Analysis.

4 citations