A team of University of Minnesota surgeons and biomedical engineers are using new technologies to create a digital library of human heart specimens and enable 3D computer modeling and mapping of hearts. This capability could let researchers see the structure and function of cardiac tissue, enabling them to better understand variations in the heart and how it changes in the presence of disease. It could also aid in the design of new cardiac devices. The University of Minnesota techniques use contrast-computed tomography, which uses dyes in the imaging process to allow the blood vessels and other structures to be better seen. The researchers are using human heart specimens from organ donors that have been found not to be usable for transplant. They published their work as a Journal of Visualized Experiments video article. (EurekAlert)(http://www.eurekalert.org/pub_releases/2013-04/tjov-sst041613.php)
Tuesday, May 21, 2013
New Techniques Perform 3D Modeling of the Human Heart
Thursday, October 18, 2012
Training computers to understand the human brain
Understanding how the human brain categorizes information through signs and language is a key part of developing computers that can 'think' and 'see' in the same way as humans. Hiroyuki Akama at the Graduate School of Decision Science and Technology, Tokyo Institute of Technology, together with co-workers in Yokohama, the USA, Italy and the UK, have completed a study using fMRI datasets to train a computer to predict the semantic category of an image originally viewed by five different people.
The participants were asked to look at pictures of animals and hand tools together with an auditory or written (orthographic) description. They were asked to silently 'label' each pictured object with certain properties, whilst undergoing an fMRI brain scan. The resulting scans were analysed using algorithms that identified patterns relating to the two separate semantic groups (animal or tool).
After 'training' the algorithms in this way using some of the auditory session data, the computer correctly identified the remaining scans 80-90% of the time. Similar results were obtained with the orthographic session data. A cross-modal approach, namely training the computer using auditory data but testing it using orthographic, reduced performance to 65-75%. Continued research in this area could lead to systems that allow people to speak through a computer simply by thinking about what they want to say.
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The above story is reprinted from materials provided by Tokyo Institute of Technology, via ResearchSEA.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Journal Reference:
Hiroyuki Akama, Brian Murphy, Li Na, Yumiko Shimizu, Massimo Poesio. Decoding semantics across fMRI sessions with different stimulus modalities: a practical MVPA study. Frontiers in Neuroinformatics, 2012; 6 DOI: 10.3389/fninf.2012.00024Note: If no author is given, the source is cited instead.
Disclaimer: This article is not intended to provide medical advice, diagnosis or treatment. Views expressed here do not necessarily reflect those of ScienceDaily or its staff.
Friday, August 31, 2012
Robots will quickly recognize and respond to human gestures, with new algorithms
Many works of science fiction have imagined robots that could interact directly with people to provide entertainment, services or even health care. Robotics is now at a stage where some of these ideas can be realized, but it remains difficult to make robots easy to operate.
One option is to train robots to recognize and respond to human gestures. In practice, however, this is difficult because a simple gesture such as waving a hand may appear very different between different people. Designers must develop intelligent computer algorithms that can be 'trained' to identify general patterns of motion and relate them correctly to individual commands.
Now, Rui Yan and co-workers at the A*STAR Institute for Infocomm Research in Singapore have adapted a cognitive memory model called a localist attractor network (LAN) to develop a new system that recognize gestures quickly and accurately, and requires very little training.
"Since many social robots will be operated by non-expert users, it is essential for them to be equipped with natural interfaces for interaction with humans," says Yan. "Gestures are an obvious, natural means of human communication. Our LAN gesture recognition system only requires a small amount of training data, and avoids tedious training processes."
Yan and co-workers tested their software by integrating it with ShapeTape, a special jacket that uses fibre optics and inertial sensors to monitor the bending and twisting of hands and arms. They programmed the ShapeTape to provide data 80 times per second on the three-dimensional orientation of shoulders, elbows and wrists, and applied velocity thresholds to detect when gestures were starting.
In tests, five different users wore the ShapeTape jacket and used it to control a virtual robot through simple arm motions that represented commands such as forward, backwards, faster or slower. The researchers found that 99.15% of gestures were correctly translated by their system. It is also easy to add new commands, by demonstrating a new control gesture just a few times.
The next step in improving the gesture recognition system is to allow humans to control robots without the need to wear any special devices. Yan and co-workers are tackling this problem by replacing the ShapeTape jacket with motion-sensitive cameras.
"Currently we are building a new gesture recognition system by incorporating our method with a Microsoft Kinect camera," says Yan. "We will implement the proposed system on an autonomous robot to test its usability in the context of a realistic service task, such as cleaning!"
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The above story is reprinted from materials provided by The Agency for Science, Technology and Research (A*STAR).
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Journal Reference:
Rui Yan, Keng Peng Tee, Yuanwei Chua, Haizhou Li, Huajin Tang. Gesture Recognition Based on Localist Attractor Networks with Application to Robot Control [Application Notes]. IEEE Computational Intelligence Magazine, 2012; 7 (1): 64 DOI: 10.1109/MCI.2011.2176767Note: If no author is given, the source is cited instead.
Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.
Thursday, June 28, 2012
Robots will quickly recognize and respond to human gestures, with new algorithms
Many works of science fiction have imagined robots that could interact directly with people to provide entertainment, services or even health care. Robotics is now at a stage where some of these ideas can be realized, but it remains difficult to make robots easy to operate.
One option is to train robots to recognize and respond to human gestures. In practice, however, this is difficult because a simple gesture such as waving a hand may appear very different between different people. Designers must develop intelligent computer algorithms that can be 'trained' to identify general patterns of motion and relate them correctly to individual commands.
Now, Rui Yan and co-workers at the A*STAR Institute for Infocomm Research in Singapore have adapted a cognitive memory model called a localist attractor network (LAN) to develop a new system that recognize gestures quickly and accurately, and requires very little training.
"Since many social robots will be operated by non-expert users, it is essential for them to be equipped with natural interfaces for interaction with humans," says Yan. "Gestures are an obvious, natural means of human communication. Our LAN gesture recognition system only requires a small amount of training data, and avoids tedious training processes."
Yan and co-workers tested their software by integrating it with ShapeTape, a special jacket that uses fibre optics and inertial sensors to monitor the bending and twisting of hands and arms. They programmed the ShapeTape to provide data 80 times per second on the three-dimensional orientation of shoulders, elbows and wrists, and applied velocity thresholds to detect when gestures were starting.
In tests, five different users wore the ShapeTape jacket and used it to control a virtual robot through simple arm motions that represented commands such as forward, backwards, faster or slower. The researchers found that 99.15% of gestures were correctly translated by their system. It is also easy to add new commands, by demonstrating a new control gesture just a few times.
The next step in improving the gesture recognition system is to allow humans to control robots without the need to wear any special devices. Yan and co-workers are tackling this problem by replacing the ShapeTape jacket with motion-sensitive cameras.
"Currently we are building a new gesture recognition system by incorporating our method with a Microsoft Kinect camera," says Yan. "We will implement the proposed system on an autonomous robot to test its usability in the context of a realistic service task, such as cleaning!"
Share this story on Facebook, Twitter, and Google:Other social bookmarking and sharing tools:
Story Source:
The above story is reprinted from materials provided by The Agency for Science, Technology and Research (A*STAR), via ResearchSEA.
Note: Materials may be edited for content and length. For further information, please contact the source cited above.
Journal Reference:
Rui Yan, Keng Peng Tee, Yuanwei Chua, Haizhou Li, Huajin Tang. Gesture Recognition Based on Localist Attractor Networks with Application to Robot Control [Application Notes]. IEEE Computational Intelligence Magazine, 2012; 7 (1): 64 DOI: 10.1109/MCI.2011.2176767Note: If no author is given, the source is cited instead.
Disclaimer: Views expressed in this article do not necessarily reflect those of ScienceDaily or its staff.