Jose Millan and EPFL colleagues have combined a brain computer interface with functional electrical stimulation in a system that, in a study, showed the ability to enhance the restoration of limb use after a stroke.
According to Millan: “The key is to stimulate the nerves of the paralyzed arm precisely when the stroke-affected part of the brain activates to move the limb, even if the patient can’t actually carry out the movement. That helps re-establish the link between the two nerve pathways where the signal comes in and goes out.”
27 patients with a similar lesion that resulted in moderate to severe arm paralysis following a stroke participated in the trial. Half were treated with the dual-therapy approach, and reported clinically significant improvements. A BCI system enabled the researchers to pinpoint where the electrical activity occurred in the brain when they tried to extend their hands. Each time the electrical activity was identified, the system stimulated the muscle controlling the corresponding wrist and finger movements.
The control group received FES only, and had their arm muscles stimulated randomly. This allowed the scientists to understand how much additional motor function improvement could be attributed to the BCI system.
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Ipsihand, developed by Eric Leuthardt and Washington University colleagues, is a brain controlled glove that helps reroute hand control to an undamaged part of the brain. The system uses a glove or brace on the hand, an EEG cap, and an amplifier.
One’s hands are controlled by the opposite side of the brain. If one hemisphere is damaged, it is difficult to control the other hand.
According to Leuthard, the idea of Ipsihand is that if one can “couple those motor signals that are associated with moving the same-sided limb with the actual movements of the hand, new connections will be made in your brain that allow the uninjured areas of your brain to take over control of the paralyzed hand.”
Ipsihand’s cap detects intention signals to open or close the hand, then the computer amplifies them. The brace then opens or closes in a pincer-like grip with the hand inside, bending the fingers and thumb to meet.
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Georgia Tech’s Ayanna Howard has developed Darwin, a socially interactive robot that encourages children to play an active role in physical therapy.
Specific targeting children with cerebral palsy (who are involved in current studies), autism, or tbi, the robot is designed to function in the home, to supplement services provided by clinicians. It engages users as their human therapist would — monitoring performance, and providing motivation and feedback.In a recent experiment, motion trackers monitored user movements as Darwin offered encouragement, and demonstrated movements when they were not performed correctly. Researchers claimed that wth the exception of one case, the robot’s impact was “significantly positive.
Darwin is still evolving (pun intended) and has not yet been commercialized.
At MIT, Newman Lab researcher Hermano Igo Krebs has been using robots for gait and balance neurorehabilitation in stroke and cerebral palsy patients since 1989. Krebs’s technology continues to be incorporated into Burke Rehabilitation hospital treatment plans.
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UNC and NC State researchers have developed a promising, self-regulating, Heparin releasing patch, meant to optimize levels of the blood thinner in one’s body. It has only been tested on animals, but was found to be more effective at preventing thrombosis than traditional drug delivery methods.
Current protocol requires regular blood testing, to prevent hemorrhaging from a too-high dose, or, of course, thrombosis from an inadequate one.
The patch uses microneedles made of a polymer that consists of hyaluronic acid and Heparin. It responds to thrombin, an enzyme that initiates blood clotting. When elevated thrombin levels come into contact with a microneedle, the enzymes break the amino acid chains that bind the Heparin to the HA, releasing the Heparin into the blood stream.
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MedyMatch uses big data and artificial intelligence to improve stroke diagnosis, with the goal of faster treatment.
Patient CT photos are scanned and immediately compared with hundreds of thousands of other patient results. Almost any deviation from a normal CT is quickly detected.
With current methods, medical imaging errors can occur when emergency room radiologists miss subtle aspects of brain scans, leading to delayed treatment. Fast detection of stroke can prevent paralysis and death.
The company claims that it can detect irregularities more accurately than a human can. Findings are presented as 3D brain images, enabling a doctor to make better informed decisions. The cloud-based system allows scans to be uploaded from any location.
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Samsung’s Early Detection Sensor & Algorithm Package (EDSAP), developed by Se-hoon Lim, is meant to detect early signs of stroke.
A multiple sensor headset records electrical impulses in the brain, algorithms determine the likelihood of a stroke in one minute, and results are presented in a mobile app. EDSAP can also analyze stress and sleep patterns, and potentially be used to monitor heart activity. The company believes that the system can one day be built into one’s own glasses.
Wearable Tech + Digital Health NYC 2015 – June 30 @ New York Academy of Sciences
Medfield Diagnostics and Chalmers University have developed “Strokefinder,” a microwave helmet that quickly determines whether a person has had a stroke, enabling early and appropriate treatment. It has been tested on 45 patients.
The helmet uses microwave typography to determine whether a stroke is caused by a clot or bleeding. Strokes caused by clots require a drug to dissolve the clot within 4.5 hours. Less than 10% of patients diagnosed by CT or MRI get anti-clotting drugs on time, as too much time often elapses between a patient’s hospital arrival and a diagnostic scan. Strokes caused by bleeding require different treatment.
An early prototype involved a modified bike helmet and was able to differentiate between the two types of stroke accurately some of the time. The team has since refined the device, building a custom helmet that better adapts to different skulls. The plan is to carry out a large scale study in order to improve the predictive power of the algorithms.