This paper presents a system that can recognize gestures and learn new ones with one or two examples online, using HMMs. They base their idea on the procedure: (1) the user makes a series of gestures, (2) the system segments the data into separate gestures, then either reacts to the gesture if it recognizes it, or asks for clarification from the user, and (3) the system adds the new example to a list of examples it has seen and retrains the HMM on the data so far seen using the Baum-Welch algorithm. They chose to represent gestures by reducing the data to a one-dimensional sequence of symbols, after resampling at even intervals, dividing into time windows, and undergoing vector quantization. They generate a codebook for this using the LBG algorithm, offline. Their segmentation process requires that the hand be still for a short time between gestures, though they believe an acceleration threshold would be useful if the hand does not stop. They have a simple function to give them a confidence measure for each gesture's classification, and they tested the system on 14 letters of the sign language alphabet which they chose for not being ambiguous without hand orientation data. They found 1%-2.4% error after 2 examples and close to none after 4 or 6 examples in their two tests. Their future goals include increasing vocabulary size by using 5 dimensions of symbols (one per finger).
Discussion:
I am curious how natural pausing between gestures will be in how many applications. As we've discussed in class, applications like sign language might use a very fluid series of gestures. But in the case of some kinds of commands, pauses are probably very natural, unless you want to do a fast sequence of commands and not have to wait for confirmation of comprehension between them. I can imagine "corner finding" based on direction and speed could be another useful tool to segment gestures into more manageable pieces.
I think resampling at even intervals as in this paper will be a very good thing to keep in mind, along with jitter reduction.
Showing posts with label robotics. Show all posts
Showing posts with label robotics. Show all posts
Wednesday, January 30, 2008
Monday, January 28, 2008
An Architecture for Gesture-Based Control of Mobile Robots (Iba, Weghe, Paredis, Khosla)
This paper describes an approach for controlling mobile robots with hand gestures. The authors believe that capturing the intent of the user's 3D motions is a challenging but important goal for improving interaction between human and machine, and their gesture-based programming research is aimed at this long term goal. They have made a gesture spotting and recognition algorithm based on a HMM. Their system setup includes a mobile robot, a CyberGlove, a Polhemus 6DOF position sensor and a geolocation system to track the position and orientation of the robot. The robot can be directed to move by hand gestures: opening and closing are represented by going between flat hand and closed fist; pointing; and waving left or right. Closing slows and eventually stops the robot; opening maintains its current state; pointing makes the robot accelerate forwards (local control) or "go there" (global control); waving left or right makes the robot increase its rotational velocity in that direction (local) or go in the direction the hand is waving (global).
They pre-process the data to improve speed and performance by reducing the glove data from an 18-dimensional vector to a 10-dimensional feature vector, augment it with its derivatives, and maps the input vector to an integer codeword -- they chose to use 32 codewords. They partitioned a data set of 5000 measurements that cover the whole range of possible hand movements into 32 clusters, a centroid is calculated for each, and so at run time a feature vector can be mapped to a code word and a gesture can be treated as a sequence of codewords.
Discussion:
I think the most potentially useful part of this paper is the idea of reducing gestures to a sequence of codewords, since that would simplify the data we'd have to deal with by a lot. However, I wonder if they really got as thorough a sampling as they think they did, and if it allows for subtly different gestures. I don't buy that 32 codewords would be enough for all conceivable gestures, especially for something complex -- obviously ASL has 24 different static gestures that mean different things, and I'd bet we could find 9 more distinct gestures. Maybe increasing the number of codewords would help, but I'd still be wary.
I also think that while their system is a good step toward robot control using hand tracking, it isn't convincing that hand gestures are a good way to control robots doing important things that require much precision. I think I've played games where a character is controlled by using the up arrow to walk forward from the character's perspective and right and left keys to turn the character to his left or right, and that is hard enough to control without the input being interpreted as adding velocity rather than just turning or moving at a fixed speed. As for the global controls, pointing is imprecise, especially at a distance. Think about trying to have people point out a constellation to you: their arm is neither aligned with their line of sight nor yours. Waving seems even harder to be precise about. For circumstances that require accuracy in the robot's movement, I think a different set of controls would be necessary, though it might still be possible to do it using a glove or hand tracking.
Also of note, the 8th reference in this paper refers to public domain HMM C++ code by Myers and Whitson, which might be nice to look at if we need HMM code.
They pre-process the data to improve speed and performance by reducing the glove data from an 18-dimensional vector to a 10-dimensional feature vector, augment it with its derivatives, and maps the input vector to an integer codeword -- they chose to use 32 codewords. They partitioned a data set of 5000 measurements that cover the whole range of possible hand movements into 32 clusters, a centroid is calculated for each, and so at run time a feature vector can be mapped to a code word and a gesture can be treated as a sequence of codewords.
Discussion:
I think the most potentially useful part of this paper is the idea of reducing gestures to a sequence of codewords, since that would simplify the data we'd have to deal with by a lot. However, I wonder if they really got as thorough a sampling as they think they did, and if it allows for subtly different gestures. I don't buy that 32 codewords would be enough for all conceivable gestures, especially for something complex -- obviously ASL has 24 different static gestures that mean different things, and I'd bet we could find 9 more distinct gestures. Maybe increasing the number of codewords would help, but I'd still be wary.
I also think that while their system is a good step toward robot control using hand tracking, it isn't convincing that hand gestures are a good way to control robots doing important things that require much precision. I think I've played games where a character is controlled by using the up arrow to walk forward from the character's perspective and right and left keys to turn the character to his left or right, and that is hard enough to control without the input being interpreted as adding velocity rather than just turning or moving at a fixed speed. As for the global controls, pointing is imprecise, especially at a distance. Think about trying to have people point out a constellation to you: their arm is neither aligned with their line of sight nor yours. Waving seems even harder to be precise about. For circumstances that require accuracy in the robot's movement, I think a different set of controls would be necessary, though it might still be possible to do it using a glove or hand tracking.
Also of note, the 8th reference in this paper refers to public domain HMM C++ code by Myers and Whitson, which might be nice to look at if we need HMM code.
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