A Dynamic Gesture Interface for Virtual Environments Based on Hidden Markov Models (Qing 2005)

Summary:
This paper points out that dynamic gestures are stochastic and while repeating the same gesture will result in different measurements per trial, there are statistical properties which can describe the motion. The paper suggests using HMMs to extract defining characteristics from gesture data in order to classify the gestures. An Expectation-Modification algorithm (another name for Baum-Welch) is used to solve the HMMs. The characteristics examined by their HMM algorithm are the standard deviation of the angle variation of each finger.
An experiment examined the recognition of three gestures, named "Great", "Quote", and "Trigger", which were used to control the rotation of a cube about three axes. The GLUT framework was used to create a simple colored cube which could be rendered and rotated using the OpenGL library. Gesture data was sampled at 10 Hz from a CyberGlove and then normalized. The data are quantized and their standard deviation is calculated before being sent to the HMM to be recognized. Each gesture HMM was trained with 10 data sets each. An observed gesture sequence is compared to each of the trained gestures, and the one with the highest probability is reported as the most likely match.

Discussion:
The authors use several abbreviations throughout the paper, and their overuse just adds another step required for translating the meaning of the paper. The overview of HMMs is a bit brief, but does refer the reader to a paper by Rabiner covering a tutorial of HMMs. The choice to use HMMs was supposedly made to be able to recognize dynamic gestures instead of static postures, but the three gestures used in the study can be distinguished without the use of HMMs. The motion of the hands was unnecessary to distinguish the gestures since the position of the hands is different enough to separate them. Also, there was no user study or reporting of recognition accuracy rates.