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.
Summary:
Lee and Yangsheng created a HMM system that allows for online updating of gestures. If the system is certain about a gesture (i.e., above or below a threshold), then the system performs the action associated with the gesture. Otherwise, the system asks the user for the gesture's confirmation. The HMM then updates through using the Baum-Welch algorithm (an EM algorithm for finding state and transition probabilities for an HMM given data).
Their system uses a CyberGlove to capture the hand gestures. The gestures are first captured from the glove, then resampled and smoothed before performing vector quantization. Gestures are segmented by having the user stop or remain still for a short time.
Gestures are evaluated on a logarithmic scale of the sums of the probability of the model / probability of the observation sequence. If the gesture is below a threshold it is considered correct, and if it us above the threshold it is considered suspect or incorrect.
The domain for testing the system was 14 sign language letters that were distinct enough to be used with VQ.
Discussion:
I'm very confused by the graphs they give. They mention that if their "V" values corresponding to the correct/incorrect threshold are below -2, then the gesture is correct. Yet their graphs only show 2 examples ever even bordering on the -2 mark; all other values were way below -2. Does this mean that their system was always confident?
I also have an issue with telling the computer what the correct gesture is. Although I've done almost the exact same thing in recent work, hand-gesturing systems are geared toward non-keyboard-monitor use. For instance, to control a robot, I'd probably be looking at the robot and not a monitor. In the field I would not want to turn around, find my keyboard, punch up the correct gesture, and continue.
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2 comments:
yeah the whole train as you go thing kind of reminded me of the $1 recognizer, but you're right. chances are this sort of system would only be beneficial if you didn't have your computer with you. that would be tedious to try and train on the fly.
I question the validity of their confidence value as well. Also, you can get into trouble with HMMs if you overtrain, since you water down the probabilities at each state and lose the ability to recognize specific gestures.
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