Archive for the ‘paralysis’ Category

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Human Rights group Amnesty International has condemned a reported Saudi court ruling sentencing a man to be paralyzed as retribution for having paralyzed another man as “outrageous.” In a statement issued Tuesday, the rights group called the punishment “torture,” adding that it “should on no account be carried out.”

The Saudi Gazette, an English language daily paper, reported that Ali Al-Khawahir was 14 when he stabbed and paralyzed his best friend 10 years ago. Al-Khawahir has been in prison ever since, and has been sentenced to be paralyzed if he cannot come up with one million Saudi Riyals ($266,000) in compensation to be paid to the victim, the newspaper reported.

“Paralyzing someone as punishment for a crime would be torture,” said Ann Harrison, Amnesty International’s Middle East and North Africa deputy director. “That such a punishment might be implemented is utterly shocking, even in a context where flogging is frequently imposed as a punishment for some offenses, as happens in Saudi Arabia.”

The rights group calls this an example of a “qisas,” or retribution, case, adding that “other sentences passed have included eye-gouging, tooth extraction, and death in cases of murder. “In such cases, the victim can demand the punishment be carried out, request financial compensation or grant a conditional or unconditional pardon.” Despite repeated attempts, the Saudi Justice Ministry could not be reached for comment on the case.

“If implemented, the paralysis sentence would contravene the U.N. Convention against Torture to which Saudi Arabia is a state party and the Principles of Medical Ethics adopted by the UN General Assembly,” Amnesty International said.

http://www.reuters.com/article/2013/04/03/us-saudi-sentence-paralysis-idUSBRE9320OL20130403?feedType=RSS&feedName=oddlyEnoughNews

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A Chinese man has been kept alive for the last five years thanks to a homemade ventilator that his family have to manually squeeze hundreds of times a day. Fu Xuepeng was 25 when he collided with a car while riding his motorbike to a supermarket. He was diagnosed with severe damage to his nervous system and has been paralysed from the neck down and unable to breathe unaided ever since. Instead, he must rely on a ventilator with a breathing tube in his airway.

But after four months on breathing equipment in Taizhou First People’s Hospital, his parents were forced to bring him home because of the unbearably high medical expenses. Despite receiving 300,000 yuan (£30,0000) in compensation from the driver, it cost more than 10,000 (£1,000) yuan per week to keep Fu on a medical ventilator, according to a report by the website china.org.cn. His mother Wang Lanqin and father Fu Minzu were left with only one option – to remove him from hospital and try to care for him at home. They bought a bag valve mask ventilator and have manually pumped lifesaving oxygen into his lungs by hand ever since. To keep Fu, now, 30, alive, the attached air ball must be squeezed at even intervals to manually pump oxygen into the body.

His parents, two sisters and brothers-in-law all take it in turns to squeeze the resuscitator bag 18 times per minute. Incredibly, if they stop for just three minutes Fu would die. As a result of such tireless work, their hands have now been deformed by constantly squeezing the device. Their only break is at night, when a home built DIY ventilator, crafted by Fu’s younger brother in 2009 after watching how to make one on TV, is used. This comprises an electric motor and a pushing pole attaching the device to the bag valve mask. However the high cost of electricity means they cannot use it all day, forcing them to continue their bed side vigils throughout the day.

But the family’s fortunes are now set to change after a blog documenting his heart-wrenching story was spotted by a Chinese company that makes ventilators.
It has now pledged to donate a ventilator to him and other well-wishers have set up a fund to raise money for him. Government staff and doctors from the local hospital are also set to visit the family now its plight has come to light.

Last week MailOnline told the story of Hu Songwen, a Chinese man who has been kept alive by his homemade dialysis machine for 13 years. Hu, who suffers from kidney disease, made it from kitchen utensils and old medical instruments after he could no long afford hospital fees. He was a college student when he was diagnosed in 1993 with kidney disease, which means waste products cannot be removed from his blood. He underwent dialysis treatment in hospital but ran out of savings after six years. His solution was to create his own machine to slash his costs.

Read more: http://www.dailymail.co.uk/health/article-2270178/Chinese-man-kept-alive-years-HOMEMADE-ventilator-family-squeeze-18-times-minute.html#ixzz2KzGBncHK

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A woman who is paralysed from the neck down has stunned doctors with her extraordinary skill at using a robotic arm that is controlled by her thoughts alone.

The 52-year-old patient, called Jan, lost the use of her limbs more than 10 years ago to a degenerative disease that damaged her spinal cord. The disruption to her nervous system was the equivalent to having a broken neck.

But in training sessions at the University of Pittsburgh, doctors found she quickly learned to make fluid movements with the brain-controlled robotic arm, reaching levels of performance never seen before.

Doctors recruited the woman to test a robotic arm that is controlled by a new kind of computer program that translates the natural brain activity used to move our limbs into commands to move the robotic arm.

The design is intended to make the robotic arm more intuitive for patients to use. Instead of having to think where to move the arm, a patient can simply focus on the goal, such as “pick up the ball”.

Several groups around the world are developing so-called brain-machine interfaces to control robotic arms and other devices, such as computers, but none has achieved such impressive results.

Writing in the Lancet, researchers said Jan was able to move the robotic arm back, forward, right, left, and up and down only two days into her training. Within weeks she could reach out, and change the position of the hand to pick up objects on a table, including cones, blocks and small balls, and put them down at another location.

“We were blown away by how fast she was able to acquire her skill, that was completely unexpected,” said Andrew Schwartz, professor of neurobiology at the University of Pittsburgh. “At the end of a good day, when she was making these beautiful movements, she was ecstatic.”

To wire the woman up to the arm, doctors performed a four-hour operation to implant two tiny grids of electrodes, measuring 4mm on each side, into Jan’s brain. Each grid has 96 little electrodes that stick out 1.5mm. The electrodes were pushed just beneath the surface of the brain, near neurons that control hand and arm movement in the motor cortex.

Once the surgeons had implanted the electrodes, they replaced the part of the skull they had removed to expose the brain. Wires from the electrodes ran to connectors on the patient’s head, which doctors could then use to plug the patient into the computer system and robotic arm.

Before Jan could use the arm, doctors had to record her brain activity imagining various arm movements. To do this, they asked her to watch the robotic arm as it performed various moves, and got her to imagine moving her own arm in the same way.

While she was thinking, the computer recorded the electrical activity from individual neurons in her brain.

Neurons that control movement tend to have a preferred direction, and fire their electrical pulses more frequently to perform a movement in that direction. “Once we understand which direction each neuron likes to fire in, we can look at a larger group of neurons and figure out what direction the patient is trying to move the arm in,” Schwartz said.

To begin with, the robotic arm was programmed to help Jan’s movements, by ignoring small mistakes in movements. But she quickly progressed to controlling the arm without help. After three months of training, she completed tasks with the robotic arm 91.6% of the time, and 30 seconds faster than when the trial began.

In an accompanying article, Grégoire Courtine, at the Swiss Federal Institute of Technology in Lausanne, said: “This bioinspired brain-machine interface is a remarkable technological and biomedical achievement.”

There are hurdles ahead for mind-controlled robot limbs. Though Jan’s performance continued to improve after the Lancet study was written, she has plateaued recently, because scar tissue that forms around the tips of the electrodes degrades the brain signals the computer receives.

Schwartz said that using thinner electrodes, around five thousandths of a millimetre thick, should solve this problem, as they will be too small to trigger the scarring process in the body.

The researchers now hope to build senses into the robotic arm, so the patient can feel the texture and temperature of the objects they are handling. To do this, sensors on the fingers of the robotic hand could send information back to the sensory regions of the brain.

Another major focus of future work is to develop a wireless system, so the patient does not have to be physically plugged into the computer that controls the robotic arm.

Thanks to Kebmodee AND Dr. Rajadhyaksha for bringing this to the attention of the It’s Interesting community.

http://www.guardian.co.uk/science/2012/dec/17/paralysed-woman-robotic-arm-pittsburgh

 

 

Stanford researchers have designed the fastest, most accurate mathematical algorithm yet for brain-implantable prosthetic systems that can help disabled people maneuver computer cursors with their thoughts. The algorithm’s speed, accuracy and natural movement approach those of a real arm.

 

 

On each side of the screen, a monkey moves a cursor with its thoughts, using the cursor to make contact with the colored ball. On the left, the monkey’s thoughts are decoded with the use of a mathematical algorithm known as Velocity. On the right, the monkey’s thoughts are decoded with a new algorithm known as ReFITT, with better results. The ReFIT system helps the monkey to click on 21 targets in 21 seconds, as opposed to just 10 clicks with the older system.

 

 

When a paralyzed person imagines moving a limb, cells in the part of the brain that controls movement activate, as if trying to make the immobile limb work again.

Despite a neurological injury or disease that has severed the pathway between brain and muscle, the region where the signals originate remains intact and functional.

In recent years, neuroscientists and neuroengineers working in prosthetics have begun to develop brain-implantable sensors that can measure signals from individual neurons.

After those signals have been decoded through a mathematical algorithm, they can be used to control the movement of a cursor on a computer screen – in essence, the cursor is controlled by thoughts.

The work is part of a field known as neural prosthetics.

A team of Stanford researchers have now developed a new algorithm, known as ReFIT, that vastly improves the speed and accuracy of neural prosthetics that control computer cursors. The results were published Nov. 18 in the journal Nature Neuroscience in a paper by Krishna Shenoy, a professor of electrical engineering, bioengineering and neurobiology at Stanford, and a team led by research associate Dr. Vikash Gilja and bioengineering doctoral candidate Paul Nuyujukian.

In side-by-side demonstrations with rhesus monkeys, cursors controlled by the new algorithm doubled the performance of existing systems and approached performance of the monkey’s actual arm in controlling the cursor. Better yet, more than four years after implantation, the new system is still going strong, while previous systems have seen a steady decline in performance over time.

“These findings could lead to greatly improved prosthetic system performance and robustness in paralyzed people, which we are actively pursuing as part of the FDA Phase-I BrainGate2 clinical trial here at Stanford,” said Shenoy.

The system relies on a sensor implanted into the brain, which records “action potentials” in neural activity from an array of electrode sensors and sends data to a computer. The frequency with which action potentials are generated provides the computer important information about the direction and speed of the user’s intended movement.

The ReFIT algorithm that decodes these signals represents a departure from earlier models. In most neural prosthetics research, scientists have recorded brain activity while the subject moves or imagines moving an arm, analyzing the data after the fact. “Quite a bit of the work in neural prosthetics has focused on this sort of offline reconstruction,” said Gilja, the first author of the paper.

The Stanford team wanted to understand how the system worked “online,” under closed-loop control conditions in which the computer analyzes and implements visual feedback gathered in real time as the monkey neurally controls the cursor toward an onscreen target.

The system is able to make adjustments on the fly when guiding the cursor to a target, just as a hand and eye would work in tandem to move a mouse-cursor onto an icon on a computer desktop.

If the cursor were straying too far to the left, for instance, the user likely adjusts the imagined movements to redirect the cursor to the right. The team designed the system to learn from the user’s corrective movements, allowing the cursor to move more precisely than it could in earlier prosthetics.

To test the new system, the team gave monkeys the task of mentally directing a cursor to a target – an onscreen dot – and holding the cursor there for half a second. ReFIT performed vastly better than previous technology in terms of both speed and accuracy.

The path of the cursor from the starting point to the target was straighter and it reached the target twice as quickly as earlier systems, achieving 75 to 85 percent of the speed of the monkey’s arm.

“This paper reports very exciting innovations in closed-loop decoding for brain-machine interfaces. These innovations should lead to a significant boost in the control of neuroprosthetic devices and increase the clinical viability of this technology,” said Jose Carmena, an associate professor of electrical engineering and neuroscience at the University of California-Berkeley.

Critical to ReFIT’s time-to-target improvement was its superior ability to stop the cursor. While the old model’s cursor reached the target almost as fast as ReFIT, it often overshot the destination, requiring additional time and multiple passes to hold the target.

The key to this efficiency was in the step-by-step calculation that transforms electrical signals from the brain into movements of the cursor onscreen. The team had a unique way of “training” the algorithm about movement. When the monkey used his arm to move the cursor, the computer used signals from the implant to match the arm movements with neural activity.

Next, the monkey simply thought about moving the cursor, and the computer translated that neural activity into onscreen movement of the cursor. The team then used the monkey’s brain activity to refine their algorithm, increasing its accuracy.

The team introduced a second innovation in the way ReFIT encodes information about the position and velocity of the cursor. Gilja said that previous algorithms could interpret neural signals about either the cursor’s position or its velocity, but not both at once. ReFIT can do both, resulting in faster, cleaner movements of the cursor.

Early research in neural prosthetics had the goal of understanding the brain and its systems more thoroughly, Gilja said, but he and his team wanted to build on this approach by taking a more pragmatic engineering perspective. “The core engineering goal is to achieve highest possible performance and robustness for a potential clinical device,” he said.

To create such a responsive system, the team decided to abandon one of the traditional methods in neural prosthetics.

Much of the existing research in this field has focused on differentiating among individual neurons in the brain. Importantly, such a detailed approach has allowed neuroscientists to create a detailed understanding of the individual neurons that control arm movement.

But the individual neuron approach has its drawbacks, Gilja said. “From an engineering perspective, the process of isolating single neurons is difficult, due to minute physical movements between the electrode and nearby neurons, making it error prone,” he said. ReFIT focuses on small groups of neurons instead of single neurons.

By abandoning the single-neuron approach, the team also reaped a surprising benefit: performance longevity. Neural implant systems that are fine-tuned to specific neurons degrade over time. It is a common belief in the field that after six months to a year they can no longer accurately interpret the brain’s intended movement. Gilja said the Stanford system is working very well more than four years later.

“Despite great progress in brain-computer interfaces to control the movement of devices such as prosthetic limbs, we’ve been left so far with halting, jerky, Etch-a-Sketch-like movements. Dr. Shenoy’s study is a big step toward clinically useful brain-machine technology that has faster, smoother, more natural movements,” said James Gnadt, a program director in Systems and Cognitive Neuroscience at the National Institute of Neurological Disorders and Stroke, part of the National Institutes of Health.

For the time being, the team has been focused on improving cursor movement rather than the creation of robotic limbs, but that is not out of the question, Gilja said. Near term, precise, accurate control of a cursor is a simplified task with enormous value for people with paralysis.

“We think we have a good chance of giving them something very useful,” he said. The team is now translating these innovations to people with paralysis as part of a clinical trial.

This research was funded by the Christopher and Dana Reeve Paralysis Foundation, the National Science Foundation, National Defense Science and Engineering Graduate Fellowships, Stanford Graduate Fellowships, Defense Advanced Research Projects Agency (“Revolutionizing Prosthetics” and “REPAIR”) and the National Institutes of Health (NINDS-CRCNS and Director’s Pioneer Award).

Other contributing researchers include Cynthia Chestek, John Cunningham, Byron Yu, Joline Fan, Mark Churchland, Matthew Kaufman, Jonathan Kao and Stephen Ryu.

http://news.stanford.edu/news/2012/november/thought-control-cursor-111812.html

Thanks to Kebmodee for bringing this to the attention of the It’s Interesting community