Article ID Journal Published Year Pages File Type
6923728 Computers in Industry 2018 11 Pages PDF
Abstract
Hand gestures are extensively used to communicate based on non-verbal interaction with computers. This mode of communication is made possible by implementing machine learning algorithms for pattern recognition. A stochastic mathematical approach is used to interpret the hand gesture pattern for classification. In this work, a predominant method is used by 1-D hidden Markov model (1-D HMM) for classifying the patterns and to measure its performance. During training phase, 1-D HMM is used to predict its next state sequence of hand gestures using dynamic programming methods such as Baum-Welch algorithm and Viterbi algorithm. However, dynamic programming based prediction methodologies are complex. To enhance the performance of 1-D HMM model, its parameter and observation state sequence must be optimized using bio-inspired heuristic approaches. In this work, Artificial Bee Colony (ABC) algorithm is used for optimization. A hybrid 1-D HMM model with ABC optimization has been proposed which has yielded a better performance metrics like recognition rate and error rate for Cambridge hand gesture dataset.
Related Topics
Physical Sciences and Engineering Computer Science Computer Science Applications
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