[08] A Dynamic Gesture Interface for Virtual Environments Based on Hidden Markov Models (Chen, et al – 2005)

Current Mood: slightly peeved

Blogs I Commented On:

• Aaron Wolin
• Katie Dahmen
• Josh Peschel

Rant:
The papers...they never end...

Summary:
Meaningful hand gestures consist of two types: static postures (e.g., ASL) and continuous dynamic gestures. The latter consists of global hand motions (i.e., large hand rotations and translations) and local finger motions (i.e., parameterized with a set of joint angles). The focus of this paper is a continuous dynamic gesture recognition system based on HMMs. The prototype for continuous dynamic gesture recognition involves rotating a cube with three different gestures.

The implementation of the system contains three steps. The first step involves collecting raw data and preprocessing them. Dynamic gestures are modeled with discrete HMMs, and observation signals are standard deviations of angle variations for each finger joint. The standard deviation describes the dynamic character of angle variation for each finger joint, thus transforming multi-dimensional observation signals into easier-to-process single discrete dimensional ones. The second step is training the HMMs using the Baum-Welch algorithm. Ten data sets were taken for each dynamic gesture, resulting in three HMMs. The third step focused on the gesture recognition, which uses the forward-backward algorithm. The paper gave no results to their system.

Discussion:
What an interesting paper. There were neither any results to show the performance of their system, nor were any convincing arguments as to why HMMs were used with their standard deviation technique. I do think that the author’s standard deviation technique using HMMs could work well for other applications -- in theory. In theory, communism works. In theory.