Real‐Time Gesture–Based Communication Using Possibility Theory–Based Hidden Markov Model. (28th April 2017)

• Record Type: Journal Article
• Authors: Baranwal, Neha; Nandi, G. C.; Singh, Avinash Kumar
• Is Part Of: Computational intelligence. Volume 33:Number 4(2017)
• Page Start: 843
• Page End: 862
• Publication Date: 2017-04-28
• Subjects: hidden Markov model -- possibility theory -- gesture recognition -- stochastic process; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12116
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract : Exploring correct patterns from low‐frequency time‐series data is challenging. For resolving this problem, the concept of possibility theory–based hidden Markov model (PTBHMM) has been proposed. In this article, all three fundamental problems (evaluation, decoding, and learning) of conventional HMM have been addressed using possibility theory. For handling uncertainty, we have used an axiomatic approach of possibility theory proposed by Zadeh. The time complexity of existing solutions of HMM (forward, backward, Viterbi, and Baum Welch) and proposed possibility‐based solutions has been calculated and compared. From the comparison result, it has been found that PTBHMM has lesser time complexity and hence will be more suitable for real‐time gesture–based communication.