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Title: Gesture recognition using Hidden Markov models
Authors: Shubhashree Sangameswaran.
Keywords: DRNTU::Engineering::Electrical and electronic engineering
Issue Date: 2006
Abstract: In this project the aim is to develop a system to recognize a set of gestures using Hidden Markov models. The gestures chosen are 5 movements from Tai Chi. The system can serve as a platform for a complete, real time, interactive gesture recognition system with feedback. Tai Chi is a popular martial arts form in China. Features are extracted for a set of chosen moves, known as the training set and Hidden Markov models are trained for these gestures. Once training is complete, the model is tested for accuracy and robustness using a new set of input feature vectors, known as the test set.
Description: 69 p.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Theses

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