Human Gesture Recognition using Hidden Markov Models and Sensor Fusion

Hidden Markov Models for Gesture Recognition

Understanding human motions can be posed as a pattern recognition problem. Humans express time-varying motion patterns (gestures), such as a wave, in order to convey a message to a recipient. If a computer can detect and distinguish these human motion patterns, the desired message can be reconstructed, and the computer can respond appropriately. This thesis describes an approach to recognize do...

Parametric Hidden Markov Models for Gesture Recognition

ÐA new method for the representation, recognition, and interpretation of parameterized gesture is presented. By parameterized gesture we mean gestures that exhibit a systematic spatial variation; one example is a point gesture where the relevant parameter is the two-dimensional direction. Our approach is to extend the standard hidden Markov model method of gesture recognition by including a glo...

Hand Gesture Recognition Using Input-Output Hidden Markov Models

A new hand gesture recognition method based on Input– Output Hidden Markov Models is presented. This method deals with the dynamic aspects of gestures. Gestures are extracted from a sequence of video images by tracking the skin–color blobs corresponding to the hand into a body– face space centered on the face of the user. Our goal is to recognize two classes of gestures: deictic and symbolic.

Gesture Recognition Using Hidden Markov Model

Hand gestures are used by many applications, such as mobile phone, robots, television and gaming purposes. In order to employ hand gestures as input for a control application, it requires recognition of gesture with a high precision rate, with minimum probability of error. In the research presented, the signs are executed with one hand as it is sufficient for many everyday applications. The ges...

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words