Samraj A

Rajendran K.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Communication by gestures in personal emergency response system

2013 1st International Conference on Emerging Trends and Applications in Computer Science

Journal of Signal and Information Processing

Emergency Gesture Communication by Patients, Elderly and Differently Abled with Care Takers Using Wearable Data Gloves

A brain computer interface BCI enables direct communication between a brain and a computer translating brain activity into computer commands using preprocessing, feature extraction, and classification operations. Feature extraction is crucial, as it has a substantial effect on the classification accuracy and speed. While fractal dimension has been successfully used in various domains to characterize data exhibiting fractal properties, its usage in motor imagery-based BCI has been more recent. In this study, commonly used fractal dimension estimation methods to characterize time series Katz's method, Higuchi's method, rescaled range method, and Renyi's entropy were evaluated for feature extraction in motor imagery-based BCI by conducting offline analyses of a two class motor imagery dataset. Different classifiers fuzzy k-nearest neighbours FKNN, support vector machine, and linear discriminant analysis were tested in combination with these methods to determine the methodology with the best performance. This methodology was then modified by implementing the time-dependent fractal dimension TDFD, differential fractal dimension, and differential signals methods to determine if the results could be further improved. Katz's method with FKNN resulted in the highest classification accuracy of 85%, and further improvements by 3% were achieved by implementing the TDFD method.

Fulltext

Discrete Dynamics in Nature and Society

Evaluation of Methods for Estimating Fractal Dimension in Motor Imagery-Based Brain Computer Interface

Engineering Applications of Artificial Intelligence

American Sign Language word recognition with a sensory glove using artificial neural networks

Procedia Computer Science

Evaluation of fractal dimension estimation methods for feature extraction in motor imagery based brain computer interface

Layered architecture for real time sign recognition: Hand gesture and movement

Journal	Data powered by Typeset2013 1st International Conference on Emerging Trends and Applications in Computer Science
Publisher	Data powered by TypesetIEEE
Open Access	0