Online, Interactive Learning of Gestures for Human/Robot Interfaces - Lee and Xu

Summary
Lee and Xu seek to create an HMM-based system that recognizes hand gestures with little up-front training that can learn from its mistakes and add new gestures on the fly. First they segment the input stream from a CyberGlove into discrete symbols using a fast Fourier transform and vector quantization. They collect one example of a set gestures and train several left-to-right HMMs to recognize these gestures. Next, they classify several test gestures using a confidence measure. If this measure is below a threshold, the classifier is certain of its classification and an action is taken; otherwise, it is uncertain and prompts the user for the correct classification. The uncertain example is then either used to create a new HMM and class or to update the appropriate HMM by iterating through Baum-Welch with the additional example. Their iterative method achieves high accuracy (>99%) after a small number of examples and performs on par with batch methods (based on the likelihood that the HMMs would generate the training data).

Discussion
This is a good extension of HMMs allowing for tuning the system to a user while in use; however, they do not provide a test accuracy of batch trained HMMs for comparison making it difficult to determine which performs more accurately. Their ideal to probabilistically determine the certainty of a classification seems like a very good (useful) idea. I'd like to know if their evaluation function is just something that they thought up that works pretty well or if it has some statistical basis.

Reference
Lee, C. and X. Yangsheng (1996). Online, interactive learning of gestures for human/robot interfaces. Robotics and Automation, 1996. Proceedings., 1996 IEEE International Conference on.