Summary:
Kim et al. use dual-direction antennas to find the direction of arrival for RF signals transmitted from an RFID tag. The two spiral antennas are perpendicular to each other and their signal strengths are different depending on the angle to the RFID tag.
Obstacles in front of the antennas/tag increase the error in determining the direction. The object can still be tracked, though. In experimental results it worked pretty well.
Discussion:
It works pretty well for its domain. Probably less accurate for incredibly small movements (e.g., finger bends). Seems like every now and then it goes crazy off-track (Figure 8).
Showing posts with label robotics. Show all posts
Showing posts with label robotics. Show all posts
Wednesday, April 9, 2008
Wednesday, January 30, 2008
Online, Interactive Learning of Gestures for Human/Robot Interfaces
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.
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.
Monday, January 28, 2008
An Architecture for Gesture-Based Control of Mobile Robots
Iba, S., J. M. V. Weghe, et al. (1999). An architecture for gesture-based control of mobile robots. Intelligent Robots and Systems, 1999. IROS '99. Proceedings. 1999 IEEE/RSJ International Conference on.
Summary:
Iba et al. describe a gesture-based control scheme for robots. HMMs are used to define seven gestures: closed fist, open hand, wave left, wave right, pointing, opening, and "wait". These gestures correspond to actions that a robot can take, such as accelerating and turning.
The mobile robot that the system uses has IR sensors, sonar sensors, a camera, and a wireless transmitter. The gesture capturing is done with a CyberGlove with 18 sensors.
Gesture recognition is performed with an HMM-based recognizer. The recognizer first preprocesses the sensor data to change the 18-dimensional sensor data into a 10-dimensional feature vector. The derivatives of each feature are computed as well, to produce a 20-dimensional column. Each column is then reduced to a "codeword" that maps the input to one of 32 possible codewords, or actions. This codebook is trained offline, and at runtime the feature vectors are mapped to a codeword.
The HMM takes a sequence of codewords and determines which gesture the user is performing. It is important to note that if no suitable gesture is found, the recognizer can return "none". To overcome some HMM problems, the "wait state" is the first node in the model and transitions to the other 6 gestures. If no gesture is currently seen, the wait state is the most probable. As more observations push the gesture toward another state, the correct gesture probability is altered and the gesture spotter picks the gesture with the highest score.
Discussion:
I'd have liked to know the intuition behind using 32 codewords. The inclusion of the wait state is also odd in combination with the "opening" state, which does not seemed to be mapped to anything. So technically the opening state is a wait+1 for either the close or opened state. I don't have much more to say on this one.
Summary:
Iba et al. describe a gesture-based control scheme for robots. HMMs are used to define seven gestures: closed fist, open hand, wave left, wave right, pointing, opening, and "wait". These gestures correspond to actions that a robot can take, such as accelerating and turning.
The mobile robot that the system uses has IR sensors, sonar sensors, a camera, and a wireless transmitter. The gesture capturing is done with a CyberGlove with 18 sensors.
Gesture recognition is performed with an HMM-based recognizer. The recognizer first preprocesses the sensor data to change the 18-dimensional sensor data into a 10-dimensional feature vector. The derivatives of each feature are computed as well, to produce a 20-dimensional column. Each column is then reduced to a "codeword" that maps the input to one of 32 possible codewords, or actions. This codebook is trained offline, and at runtime the feature vectors are mapped to a codeword.
The HMM takes a sequence of codewords and determines which gesture the user is performing. It is important to note that if no suitable gesture is found, the recognizer can return "none". To overcome some HMM problems, the "wait state" is the first node in the model and transitions to the other 6 gestures. If no gesture is currently seen, the wait state is the most probable. As more observations push the gesture toward another state, the correct gesture probability is altered and the gesture spotter picks the gesture with the highest score.
Discussion:
I'd have liked to know the intuition behind using 32 codewords. The inclusion of the wait state is also odd in combination with the "opening" state, which does not seemed to be mapped to anything. So technically the opening state is a wait+1 for either the close or opened state. I don't have much more to say on this one.
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