A Dynamic Gesture Interface for Virtual Environments Based on Hidden Markov Models (Chen, El-Sawah, Joslin & Georganas)

Summary:

This paper describes a system based on HMMs to do continuous dynamic gesture recognition, motivated by natural interaction in virtual environments. They review the major points of an HMM. They collect data from a CyberGlove and use three different dynamic gestures to control a cube's rotation. They use a multi-dimensional HMM and use the standard deviation of the angle variation for each finger joint as an alternative to requiring pauses in gesturing to split the data into meaningful pieces. They collected 10 data sets for each of the three gestures they wanted to recognize in order to train the HMMs. They have a 3D hand bone structure model to give extra feedback and show what the data from the glove looks like.


Discussion:

The difference between this paper and others we've recently read is that it deals with continuous gestures rather than requiring a single brief gesture at a time with a pause before the next. I find it hard to tell exactly what the gestures they have chosen are from the image, or a way to make sense of any intuitive meaning the gestures have in relation to the idea of a 3D cube rotating, though it did seem they only used a rotating cube to have some visualization of how the commands are being recognized.

The idea of a repetitive, continuous gesture is something we haven't considered very much so far. Is it useful to be able to break up a graph and look for repetition, like we do with overtraced circles and spirals? Are there many natural gestures that are repetitive and continuous like this? Waving to instruct somebody to move or be quiet might fall under this pattern, but what other things are there?