Tag Archives: human

#439766 Understanding human-robot interaction ...

Robotic body-weight support (BWS) devices can play a key role in helping people with neurological disorders to improve their walking. The team that developed the advanced body-weight support device RYSEN in 2018 has since gained more fundamental insight in BWS but also concludes that improvement in this field is necessary. They find that recommendations for the optimal therapy settings have to be customized to each device and that developers should be more aware of the interaction between patient and the device. The researchers have published the results of their evaluation in Science Robotics on Wednesday September 22. Continue reading

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#439730 Faster Microfiber Actuators Mimic Human ...

Robotics, prosthetics, and other engineering applications routinely use actuators that imitate the contraction of animal muscles. However, the speed and efficiency of natural muscle fibers is a demanding benchmark. Despite new developments in actuation technologies, for the most past artificial muscles are either too large, too slow, or too weak.

Recently, a team of engineers from the University of California San Diego (UCSD) have described a new artificial microfiber made from liquid crystal elastomer (LCE) that replicates the tensile strength, quick responsiveness, and high power density of human muscles. “[The LCE] polymer is a soft material and very stretchable,” says Qiguang He, the first author of their research paper. “If we apply external stimuli such as light or heat, this material will contract along one direction.”
Though LCE-based soft actuators are common and can generate excellent actuation strain—between 50 and 80 percent—their response time, says He, is typically “very, very slow.” The simplest way to make the fibers both responsive and fast was to reduce their diameter. To do so, the UCSD researchers used a technique called electrospinning, which involves the ejection of a polymer solution through a syringe or spinneret under high voltage to produce ultra-fine fibers. Electrospinning is used for the fabrication of small-scale materials, to produce microfibers with diameters between 10 and 100 micrometers. It is favored for its ability to create fibers with different morphological structures, and is routinely used in various research and commercial contexts.
The microfibers fabricated by the UCSD researchers were between 40 and 50 micrometers, about the width of human hair, and much smaller than existing LCE fibers, some of which can be more than 0.3 millimeters thick. “We are not the first to use this technique to fabricate LCE fibers, but we are the first…to push this fiber further,” He says. “We demonstrate how to control the actuation of the [fibers and measure their] actuation performance.”

University of California, San Diego/Science Robotics
As proof-of-concept, the researchers constructed three different microrobotic devices using their electrospun LCE fibers. Their LSE actuators can be controlled thermo-electrically or using a near-infrared laser. When the LCE material is at room temperature, it is in a nematic phase: He explains that in this state, “the liquid crystals are randomly [located] with all their long axes pointing in essentially the same direction.” When the temperature is increased, the material transitions into what is called an isotropic phase, in which its properties are uniform in all directions, resulting in a contraction of the fiber.
The results showed an actuation strain of up to 60 percent—which means, a 10-centimeter-long fiber will contract to 4 centimeters—with a response speed of less than 0.2 seconds, and a power density of 400 watts per kilogram. This is comparable to human muscle fibers.
An electrically controlled soft actuator, the researchers note, allows easy integrations with low-cost electronic devices, which is a plus for microrobotic systems and devices. Electrospinning is a very efficient fabrication technique as well: “You can get 10,000 fibers in 15 minutes,” He says.
That said, there are a number of challenges that need to be addressed still. “The one limitation of this work is…[when we] apply heat or light to the LCE microfiber, the energy efficiency is very small—it's less than 1 percent,” says He. “So, in future work, we may think about how to trigger the actuation in a more energy-efficient way.”
Another constraint is that the nematic–isotropic phase transition in the electrospun LCE material takes place at a very high temperature, over 90 C. “So, we cannot directly put the fiber into the human body [which] is at 35 degrees.” One way to address this issue might be to use a different kind of liquid crystal: “Right now we use RM 257 as a liquid crystal [but] we can change [it] to another type [to reduce] the phase transition temperature.”
He, though, is optimistic about the possibilities to expand this research in electrospun LCE microfiber actuators. “We have also demonstrated [that] we can arrange multiple LCE fibers in parallel…and trigger them simultaneously [to increase force output]… This is a future work [in which] we will try to see if it's possible for us to integrate these muscle fiber bundles into biomedical tissue.” Continue reading

Posted in Human Robots

#439698 Artificial fiber spun from liquid ...

A team of researchers at the University of California has developed a way to create an artificial fiber that performs very much like human muscle fibers. In their paper published in the journal Science Robotics, the researchers describe their process and how well the fiber worked when tested. Continue reading

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#439574 A theoretical approach for designing a ...

Swarm robotics is a relatively new and highly promising research field, which entails the development of multi-robot teams that can move and complete tasks together. Robot swarms could have numerous valuable applications. For instance, they could support humans during search and rescue missions or allow them to monitor geographical areas that are difficult to access. Continue reading

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#439400 A Neuron’s Sense of Timing Encodes ...

We like to think of brains as computers: A physical system that processes inputs and spits out outputs. But, obviously, what’s between your ears bears little resemblance to your laptop.

Computer scientists know the intimate details of how computers store and process information because they design and build them. But neuroscientists didn’t build brains, which makes them a bit like a piece of alien technology they’ve found and are trying to reverse engineer.

At this point, researchers have catalogued the components fairly well. We know the brain is a vast and intricate network of cells called neurons that communicate by way of electrical and chemical signals. What’s harder to figure out is how this network makes sense of the world.

To do that, scientists try to tie behavior to activity in the brain by listening to the chatter of its neurons firing. If neurons in a region get rowdy when a person is eating chocolate, well, those cells might be processing taste or directing chewing. This method has mostly focused on the frequency at which neurons fire—that is, how often they fire in a given period of time.

But frequency alone is an imprecise measure. For years, research in rats has suggested that when neurons fire relative to their peers—during navigation of spaces in particular—may also encode information. This process, in which the timing of some neurons grows increasingly out of step with their neighbors, is called “phase precession.”

It wasn’t known if phase precession was widespread in mammals, but recent studies have found it in bats and marmosets. And now, a new study has shown that it happens in humans too, strengthening the case that phase precession may occur across species.

The new study also found evidence of phase precession outside of spatial tasks, lending some weight to the idea it may be a more general process in learning throughout the brain.

The paper was published in the journal Cell last month by a Columbia University team of researchers led by neuroscientist and biomedical engineer Josh Jacobs.

The researchers say more studies are needed to flesh out the role of phase precession in the brain, and how or if it contributes to learning is still uncertain.

But to Salman Qasim, a post-doctoral fellow on Jacobs’ team and lead author of the paper, the patterns are tantalizing. “[Phase precession is] so prominent and prevalent in the rodent brain that it makes you want to assume it’s a generalizable mechanism,” he told Quanta Magazine this month.

Rat Brains to Human Brains
Though phase precession in rats has been studied for decades, it’s taken longer to unearth it in humans for a couple reasons. For one, it’s more challenging to study in humans at the level of neurons because it requires placing electrodes deep in the brain. Also, our patterns of brain activity are subtler and more complex, making them harder to untangle.

To solve the first challenge, the team analyzed decade-old recordings of neural chatter from 13 patients with drug-resistant epilepsy. As a part of their treatment, the patients had electrodes implanted to map the storms of activity during a seizure.

In one test, they navigated a two-dimensional virtual world—like a simple video game—on a laptop. Their brain activity was recorded as they were instructed to drive and drop off “passengers” at six stores around the perimeter of a rectangular track.

The team combed through this activity for hints of phase precession.

Active regions of the brain tend to fire together at a steady rate. These rhythms, called brain waves, are like a metronome or internal clock. Phase precession occurs when individual neurons fall out of step with the prevailing brain waves nearby. In navigation of spaces, like in this study, a particular type of neuron, called a “place cell,” fires earlier and earlier compared to its peers as the subject approaches and passes through a region. Its early firing eventually links up with the late firing of the next place cell in the chain, strengthening the synapse between the two and encoding a path through space.

In rats, theta waves in the hippocampus, which is a region associated with navigation, are strong and clear, making precession easier to pick out. In humans, they’re weaker and more variable. So the team used a clever statistical analysis to widen the observed wave frequencies into a range. And that’s when the phase precession clearly stood out.

This result lined up with prior navigation studies in rats. But the team went a step further.

In another part of the brain, the frontal cortex, they found phase precession in neurons not involved in navigation. The timing of these cells fell out of step with their neighbors as the subject achieved the goal of dropping passengers off at one of the stores. This indicated phase precession may also encode the sequence of steps leading up to a goal.

The findings, therefore, extend the occurrence of phase precession to humans and to new tasks and regions in the brain. The researchers say this suggests the phenomenon may be a general mechanism that encodes experiences over time. Indeed, other research—some very recent and not yet peer-reviewed—validates this idea, tying it to the processing of sounds, smells, and series of images.

And, the cherry on top, the process compresses experience to the length of a single brain wave. That is, an experience that takes seconds—say, a rat moving through several locations in the real world—is compressed to the fraction of a second it takes the associated neurons to fire in sequence.

In theory, this could help explain how we learn so fast from so few examples. Something artificial intelligence algorithms struggle to do.

As enticing as the research is, however, both the team involved in the study and other researchers say it’s still too early to draw definitive conclusions. There are other theories for how humans learn so quickly, and it’s possible phase precession is an artifact of the way the brain functions as opposed to a driver of its information processing.

That said, the results justify more serious investigation.

“Anyone who looks at brain activity as much as we do knows that it’s often a chaotic, stochastic mess,” Qasim told Wired last month. “So when you see some order emerge in that chaos, you want to ascribe to it some sort of functional purpose.”

Only time will tell if that order is a fundamental neural algorithm or something else.

Image Credit: Daniele Franchi / Unsplash Continue reading

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