Tuesday June 18, 2019

Researchers Examine Patterns of Back Pain

The bad news is that one in five experienced persistent back pain, said Canizares

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Researchers study patterns of back pain. IANS

Researchers have examined the patterns of back pain over time and patient characteristics in relation to the disability.

In addition, they have identified the extent of healthcare and medication use (including opioids) associated with different patterns.

Back pain is among the most frequently reported health problems in the world.

For the study, researchers from the University Health Network’s Krembil Research Institute in Toronto, Canada studied 12,782 participants for 16 years.

They provided data on factors including comorbidities, pain, disability, opioid and other medication use, and healthcare visits.

The results showed that almost half (45.6 per cent) of the participants reported back pain at least once.

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The study included four groups of pain: persistent (18 per cent), developing (28.1 per cent), recovery (20.5 per cent), and occasional (33.4 per cent).

The findings, published in Arthritis Care and Research, showed that the persistent and developing groups tended to have more pain and disability, as well as more healthcare visits and medication use than those in the recovery and occasional trajectory groups.

In addition, the recovery trajectory group increased the use of opioids and antidepressants over time.

“The good news is that one in five people with back pain recovered. However, they continued to use opioids and antidepressants, suggesting that people recovering from back pain need ongoing monitoring,” said lead author Mayilee Canizares, postdoctoral candidate from the varsity.

The bad news is that one in five experienced persistent back pain, said Canizares.

People with back pain are a heterogeneous group that may benefit from different approaches to management rather than a traditional one size fits all approach. The distinct groups identified in the study may represent opportunities for more individualised treatment and preventative strategies, Canizares noted. (IANS)

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Researchers Teaching Artificial Intelligence to Connect Senses Like Vision and Touch

The new AI-based system can create realistic tactile signals from visual inputs

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Tool, Humans, Robots
Members of that same MIT team applied the new algorithm to the BMW factory floor experiments and found that instead of freezing in place, the robot simply rolled on . Pixabay

A team of researchers at the Massachusetts Institute of Technology (MIT) have come up with a predictive Artificial Intelligence (AI) that can learn to see by touching and to feel by seeing.

While our sense of touch gives us capabilities to feel the physical world, our eyes help us understand the full picture of these tactile signals.

Robots, however, that have been programmed to see or feel can’t use these signals quite as interchangeably.

The new AI-based system can create realistic tactile signals from visual inputs, and predict which object and what part is being touched directly from those tactile inputs.

Teaching, Artificial Intelligence, Researchers
) A team of researchers at the Massachusetts Institute of Technology (MIT) have come up with a predictive Artificial Intelligence (AI). Pixabay

In the future, this could help with a more harmonious relationship between vision and robotics, especially for object recognition, grasping, better scene understanding and helping with seamless human-robot integration in an assistive or manufacturing setting.

“By looking at the scene, our model can imagine the feeling of touching a flat surface or a sharp edge”, said Yunzhu Li, PhD student and lead author from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL).

“By blindly touching around, our model can predict the interaction with the environment purely from tactile feelings,” Li added.

The team used a KUKA robot arm with a special tactile sensor called GelSight, designed by another group at MIT.

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Using a simple web camera, the team recorded nearly 200 objects, such as tools, household products, fabrics, and more, being touched more than 12,000 times.

Breaking those 12,000 video clips down into static frames, the team compiled “VisGel,” a dataset of more than three million visual/tactile-paired images.

“Bringing these two senses (vision and touch) together could empower the robot and reduce the data we might need for tasks involving manipulating and grasping objects,” said Li.

The current dataset only has examples of interactions in a controlled environment.

Teaching, Artificial Intelligence, Researchers
While our sense of touch gives us capabilities to feel the physical world, our eyes help us understand the full picture of these tactile signals. Pixabay

The team hopes to improve this by collecting data in more unstructured areas, or by using a new MIT-designed tactile glove, to better increase the size and diversity of the dataset.

“This is the first method that can convincingly translate between visual and touch signals”, said Andrew Owens, a post-doc at the University of California at Berkeley.

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The team is set to present the findings next week at the “Conference on Computer Vision and Pattern Recognition” in Long Beach, California. (IANS)