Published: 31 March 2016
Ferti-Care Medical vibrator
Intro music by: Art of Decay
In the United States, there are approximately 17,000 new cases of spinal cord injury (SCI) every year. Of these, 20 percent result in complete paraplegia (paralysis of the legs and lower half of body) and over 13 percent result in tetraplegia (paralysis of all four limbs).
But SCI is not the only reason that people experience this type of disability. Stroke, multiple sclerosis, cerebral palsy, and a range of other neurological disorders can all lead to paralysis. In fact, a recent survey estimated that in the U.S., almost 5.4 million people live with paralysis, with stroke being the leading cause of this disability.
Now, researchers from the National Centre of Competence in Research Robotics at École Polytechnique Fédérale de Lausanne (EPFL), and at the Lausanne University Hospital in Switzerland, have come up with a groundbreaking technology that may help these patients to regain their locomotor skills.
The scientists came up with an algorithm that helps a robotic harness to facilitate the movements of the patients, thus enabling them to move naturally.
The new research has been published in the journal Science Translational Medicine, and the first author of the study is Jean-Baptiste Mignardot.
Current rehabilitation technologies for people with motor disabilities as a result of SCI or stroke involve walking on a treadmill, with the upper torso being supported by an apparatus. But existing technologies are either too rigid or do not allow the patients to move naturally in all directions.
As the authors of the new study explain, the challenge of locomotor rehabilitation resides in helping the nervous system to “relearn” the right movements. This is difficult due to the loss of muscle mass in the patients, as well as to the neurological wiring that has “forgotten” correct posture.
In order to overcome these obstacles and promote natural walking, Mignardot and colleagues designed an algorithm that coordinates with a robotic rehabilitation harness. The team tested the algorithm in more than 30 patients. The “smart walk assist” markedly and immediately improved the patients’ locomotor abilities.
This mobile harness, which is attached to the ceiling, enables patients to walk. This video shows how it works:
Additionally, after only 1 hour of training with the harness and algorithm, the “unsupported walking ability” of five of the patients improved considerably. By contrast, 1 hour on a conventional treadmill did not improve gait.
The researchers developed the so-called gravity-assist algorithm after carefully monitoring the movements of the patients and considering parameters such as “leg movement, length of stride, and muscle activity.”
As the authors explain, based on these measurements, the algorithm identifies the forces that must be applied to the upper half of the body in order to allow for natural walking.
The smart walk assist is an innovative body-weight support system because it manages to resist the force of gravity and push the patient back and forth, to the left and to the right, or in more of these directions at once, which recreates a natural gait and movement that the patients need in their day to day lives.
Grégoire Courtine, a neuroscientist at EPFL and the Lausanne University Hospital, comments on the significance of the findings, saying, “I expect that this platform will play a critical role in the rehabilitation of walking for people with neurological disorders.”
“This is a smart, discreet, and efficient assistance that will aid rehabilitation of many persons with neurological disorders.”
Prof. Jocelyne Bloch, Department of Neurosurgery, Lausanne University Hospital
For these people, neuroprosthetic devices can offer some much-needed hope.
Brain-computer interfaces (BCI) usually involve electrodes – placed on the human skull, on the brain’s surface, or in the brain’s tissue – that monitor and measure the brain activity that occurs when the brain “thinks” a thought. The pattern of this brain activity is then “translated” into a code, or algorithm, which is “fed” into a computer. The computer, in turn, transforms the code into commands that produce movement.
Neuroprosthetics are not just useful for people who cannot move their arms and legs; they also help those with sensory disabilities. The World Health Organization (WHO) estimate that approximately 360 million people across the globe have a disabling form of hearing loss, while another 39 million people are blind.
For some of these people, neuroprosthetics such as cochlear implants and bionic eyes have given them back their senses and, in some cases, they have enabled them to hear or see for the very first time.
Here, we review five of the most significant developments in neuroprosthetic technology, looking at how they work, why they are helpful, and how some of them will develop in the future.
Probably the “oldest” neuroprosthetic device out there, cochlear implants (or ear implants) have been around for a few decades and are the epitome of successful neuroprosthetics.
The U.S. Food and Drug Administration (FDA) approved cochlear implants as early as 1980, and by 2012, almost 60,000 U.S. individuals had had the implant. Worldwide, more than 320,000 people have had the device implanted.
A cochlear implant works by bypassing the damaged parts of the ear and stimulating the auditory nerve with signals obtained using electrodes. The signals relayed through the auditory nerve to the brain are perceived as sounds, although hearing through an ear implant is quite different from regular hearing.
Although imperfect, cochlear implants allow users to distinguish speech in person or over the phone, with the media abound with emotional accounts of people who were able to hear themselves for the first time using this sensory neuroprosthetic device.
Here, you can watch a video of a 29-year-old woman who hears herself for the first time using a cochlear implant:
The first artificial retina – called the Argus II – is made entirely from electrodes implanted in the eye and was approved by the FDA in February 2013. In much the same way as the cochlear implant, this neuroprosthetic bypasses the damaged part of the retina and transmits signals, captured by an attached camera, to the brain.
This is done by transforming the images into light and dark pixels that get turned into electrical signals. The electrical signals are then sent to the electrodes, which, in turn, send the signal to the brain’s optic nerve.
While Argus II does not restore vision completely, it does enable patients with retinitis pigmentosa – a condition that damages the eye’s photoreceptors – to distinguish contours and shapes, which, many patients report, makes a significant difference in their lives.
Retinitis pigmentosa is a neurodegenerative disease that affects around 100,000 people in the U.S. Since its approval, more than 200 patients with retinitis pigmentosa have had the Argus II implant, and the company that designed it is currently working to make color detection possible as well as improve the resolution of the device.
Almost 350,000 people in the U.S. are estimated to live with SCI, and 45 percent of those who had an SCI since 2010 are considered tetraplegic – that is, paralyzed from the neck down.
Bill Kochevar had electrodes surgically fitted into his brain. After training the BCI to “learn” the brain activity that matched the movements he thought about, this activity was turned into electrical pulses that were then transmitted back to the electrodes in his brain.
In much the same way that the cochlear and visual implants bypass the damaged area, so too does this BCI area avoid the “short circuit” between the brain and the patient’s muscles created by SCI.
With the help of this neuroprosthetic, the patient was able to successfully drink and feed himself. “It was amazing,” Kochevar says, “because I thought about moving my arm and it did.” Kochevar was the first patient in the world to test the neuroprosthetic device, which is currently only available for research purposes.
You can learn more about this neuroprosthetic from the video below:
However, this is not where SCI neuroprosthetics stop. The Courtine Lab – which is led by neuroscientist Gregoire Courtine in Lausanne, Switzerland – is tirelessly working to help injured people to regain control of their legs. Their research efforts with rats have enabled paralyzed rodents to walk, achieved by using electrical signals and making them stimulate nerves in the severed spinal cord.
“We believe that this technology could one day significantly improve the quality of life of people confronted with neurological disorders,” says Silvestro Micera, co-author of the experiment and neuroengineer at Courtine Labs.
Recently, Prof. Courtine has also led an international team of researchers to successfully create voluntary leg movement in rhesus monkeys. This was the first time that a neuroprosthetic was used to enable walking in nonhuman primates.
However, “it may take several years before all the components of this intervention can be tested in people,” Prof. Courtine says.
Silvestro Micera has also led other projects on neuroprosthetics, among which is the arm that “feels.” In 2014, MNT reportedon the first artificial hand that was enhanced with sensors.
Researchers measured the tension in the tendons of the artificial hand that control grasping movements and turned it into electric current. In turn, using an algorithm, this was translated into impulses that were then sent to the nerves in the arm, producing a sense of touch.
Since then, the prosthetic arm that “feels” has been improved even more. Researchers from the University of Pittsburgh and the University of Pittsburgh Medical Center, both in Pennsylvania, tested the BCI on a single patient with quadriplegia: Nathan Copeland.
The scientists implanted a sheath of microelectrodes below the surface of Copeland’s brain – namely, in his primary somatosensory cortex – and connected them to a prosthetic arm that was fitted with sensors. This enabled the patient to feel sensations of touch, which felt, to him, as though they belonged to his own paralyzed hand.
While blindfolded, Copeland was able to identify which finger on his prosthetic arm was being touched. The sensations he perceived varied in intensity and were felt as differing in pressure.
We have seen that brain-controlled prosthetics can restore patients’ sense of touch, hearing, sight, and movement, but could we build prosthetics for the brain itself?
Researchers from the Australian National University (ANU) in Canberra managed to artificially grow brain cells and create functional brain circuits, paving the way for neuroprosthetics for the brain.
By applying nanowire geometry to a semiconductor wafer, Dr. Vini Gautam, of ANU’s Research School of Engineering, and colleagues came up with a scaffolding that allows brain cells to grow and connect synaptically.
Project group leader Dr. Vincent Daria, from the John Curtin School of Medical Research in Australia, explains the success of their research:
“We were able to make predictive connections between the neurons and demonstrated them to be functional with neurons firing synchronously. This work could open up a new research model that builds up a stronger connection between materials nanotechnology with neuroscience.”
Neuroprosthetics for the brain might one day help patients who have experienced a stroke or who live with neurodegenerative diseases to recover neurologically.
Every year in the U.S., almost 800,000 people have had a stroke, and more than 130,000 people die from it. Neurodegenerative diseases are also widespread, with 5 million U.S. adults estimated to live with Alzheimer’s disease, 1 million to have Parkinson’s, and 400,000 to experience multiple sclerosis.
Live To Roll – This is my personal knowledge and experience about having sex after a SCI as well as how to best achieve ejaculation during sex and masturbation.
To date, rehabilitation robotics has come a long way effectively aiding the rehabilitation process of the patients suffering from paraplegia or hemiplegia due to spinal cord injury (SCI) or stroke respectively, through partial or even full functional recovery of the affected limb. The increased therapeutic outcome primarily results from a combination of increased patient independence and as well as reduced physical burden on the therapist. Especially for the case of gait rehabilitation following SCI or stroke, the rehab robots have the potential to significantly increase the independence of the patient during the rehabilitation process without the patient’s safety being compromised. An intensive gait-oriented rehabilitation therapy is often effective irrespective of the type of rehabilitation paradigm. However, eventually overground gait training, in comparison with body-weight supported treadmill training (BWSTT), has the potential of higher therapeutic outcome due its associated biomechanics being very close to that of the natural gait. Recognizing the apparent superiority of the overground gait training paradigms, a through literature survey on all the major overground robotic gait rehabilitation approaches was carried out and is presented in this paper. The survey includes an in-depth comparative study amongst these robotic approaches in terms of gait rehabilitation efficacy.
Published: 31 March 2016
Clinical scores for evaluating walking skills with lower limb exoskeletons are often based on a single variable, such as distance walked or speed, even in cases where a host of features are measured. We investigated how to combine multiple features such that the resulting score has high discriminatory power, in particular with few patients. A new score is introduced that allows quantifying the walking ability of patients with spinal cord injury when using a powered exoskeleton.
Four spinal cord injury patients were trained to walk over ground with the ReWalk™ exoskeleton. Body accelerations during use of the device were recorded by a wearable accelerometer and 4 features to evaluate walking skills were computed. The new score is the Gaussian naïve Bayes surprise, which evaluates patients relative to the features’ distribution measured in 7 expert users of the ReWalk™. We compared our score based on all the features with a standard outcome measure, which is based on number of steps only.
All 4 patients improved over the course of training, as their scores trended towards the expert users’ scores. The combined score (Gaussian naïve surprise) was considerably more discriminative than the one using only walked distance (steps). At the end of training, 3 out of 4 patients were significantly different from the experts, according to the combined score (p < .001, Wilcoxon Signed-Rank Test). In contrast, all but one patient were scored as experts when number of steps was the only feature.
Integrating multiple features could provide a more robust metric to measure patients’ skills while they learn to walk with a robotic exoskeleton. Testing this approach with other features and more subjects remains as future work.
Clinical scores of walking ability are crucial in many areas of physical rehabilitation to assess the efficacy of a therapeutic intervention or an assistive device, as well as to discriminate the ability between different patients [1, 2]. One domain of interest is evaluating functional ambulation in individuals who suffered a spinal cord injury (SCI). Even though many outcome measures target the SCI population [3, 4], currently there exist no specific measures targeting the ability of a patient to use a lower limb robotic exoskeleton to walk overground and achieve functional ambulation.
Lower limb exoskeletons are bilateral powered orthoses designed to provide assistance for sit-to-stand and for walking and, in some cases, to assist lower extremity function in individuals with incomplete or complete SCI [5–8]. Currently, several exoskeletons are transitioning from purely research and rehabilitation devices to personal mobility systems that individuals with SCI could use to walk inside their home and in their communities [9, 10]. A paradigmatic case is the ReWalk™, which has been approved by the Food and Drug Administration to be sold to individuals with SCI as a take-home personal mobility device.
Quantitative clinical assessment of exoskeletons is fundamental to evaluate their safety and effectiveness when used by individuals with disabilities. Specifically, individuals with complete SCI, who aim at taking an exoskeleton home as a personal mobility device, require an intensive training protocol to become independent users. Such training is typically delivered in a clinical setting and therefore clinicians need a robust metric to evaluate if a patient has reached a level of ability and expertise to independently use the device at home and in the community. Obtaining a robust index of the patients’ walking skills with an exoskeleton could also be used to inform health insurance companies about the actual improvements in functional mobility for potential reimbursement. This point is crucial as the cost of these devices is extremely high and therefore any support funding has to be justified.
The primary clinical outcome measures currently used to assess functional ambulation with exoskeletons are the 6-Minute-Walk-Test (6MWT) and the Ten-Meter-Walk-Test (10mWT) [11, 12]. These two tests measure, respectively, the distance walked in six minutes and the time to walk over a distance of 10 m, while walking at a constant speed. Despite being validated in spinal cord injury populations , it is questionable whether these measures are sufficient to fully evaluate a patient skill and the device efficiency. Indeed, other studies have measured additional features to characterize walking skills with robotic exoskeletons.
Specifically, amongst the features quantified there are: the kinematics of the hip, knee and ankle joints in patients trained to use the ReWalk™ , as recorded via a motion capture system; the exertion level based on the heart rate normalized to the walking speed (i.e. physiological cost index)  and the oxygen uptake [16, 17]. Other metrics used include the variation in vertical and lateral amplitude of the head motion , ground reaction forces analysis  and the ability to maintain eye contact to assess cognitive effort . Even when multiple features were measured, each study reports the values of each feature individually to characterize functional ambulation with exoskeletons. Therefore it is unclear how each feature contributes to the overall expertise of a subject. Furthermore, some of the captured features require complex and expensive lab equipment, commonly seen only in large hospitals and university settings.
In the current study, we propose to combine multiple features of walking performance by estimating their probability distribution over a set of expert users who have been previously trained extensively to use the exoskeleton. New participants are then scored based on how well their features fit the experts’ probability distribution. Building on this principle, we define a new score to quantify walking ability with exoskeletons: the Gaussian Naïve Bayes surprise. The term surprise is derived from information theory and represents the amount of unexpected information provided by an event . We apply our score to quantify the walking skills of four individuals with complete SCI, as they are trained to use the ReWalk™ exoskeleton. Four features are computed from the trunk accelerations, which are recorded using a commercial wearable accelerometer while subjects perform a 6MWT with the exoskeleton. We estimate the parameters of the features probability distribution from seven expert subjects (1 SCI and 6 able-bodied) that received extensive prior training with the device, and compute the Gaussian naïve Bayes surprise of the four SCI participants with respect to the experts. The score based on all four features is compared with one based only on number of steps (an equivalent of distance walked), in terms of the separation between experts and patients that is yielded by the two indices.
Recent neural science research suggests that a robotic device can be an effective tool to deliver the repetitive movement training that is needed to trigger neuroplasticity in the brain following neurologic injuries such as stroke and spinal cord injury (SCI).
In such scenario, adaptive control of the robotic device to provide assistance as needed along the intended motion trajectory with exact amount of force intensity, though complex, is a more effective approach. A critic-actor based reinforcement learning neural network (RLNN) control method is explored to provide adaptive control during post-stroke fine hand motion rehabilitation training.
The effectiveness of the method is verified through computer simulation and implementation on a hand rehabilitation robotic device.
Results suggest that the control system can fulfil the assist-as-needed (AAN) control with high performance and reliability. The method demonstrates potential to encourage active participation of the patient in the rehabilitation process and to improve the efficiency of the process.
Background: Direct brain control of overground walking in those with paraplegia due to spinal cord injury (SCI) has not been achieved. Invasive brain-computer interfaces (BCIs) may provide a permanent solution to this problem by directly linking the brain to lower extremity prostheses. To justify the pursuit of such invasive systems, the feasibility of BCI controlled overground walking should first be established in a noninvasive manner. To accomplish this goal, we developed an electroencephalogram (EEG)-based BCI to control a functional electrical stimulation (FES) system for overground walking and assessed its performance in an individual with paraplegia due to SCI.
Methods: An individual with SCI (T6 AIS B) was recruited for the study and was trained to operate an EEG-based BCI system using an attempted walking/idling control strategy. He also underwent muscle reconditioning to facilitate standing and overground walking with a commercial FES system. Subsequently, the BCI and FES systems were integrated and the participant engaged in several real-time walking tests using the BCI-FES system. This was done in both a suspended, off-the-ground condition, and an overground walking condition. BCI states, gyroscope, laser distance meter, and video recording data were used to assess the BCI performance.
Results: During the course of 19 weeks, the participant performed 30 real-time, BCI-FES controlled overground walking tests, and demonstrated the ability to purposefully operate the BCI-FES system by following verbal cues. Based on the comparison between the ground truth and decoded BCI states, he achieved information transfer rates >3 bit/s and correlations >0.9. No adverse events directly related to the study were observed.
Conclusion: This proof-of-concept study demonstrates for the first time that restoring brain-controlled overground walking after paraplegia due to SCI is feasible. Further studies are warranted to establish the generalizability of these results in a population of individuals with paraplegia due to SCI. If this noninvasive system is successfully tested in population studies, the pursuit of permanent, invasive BCI walking prostheses may be justified. In addition, a simplified version of the current system may be explored as a noninvasive neurorehabilitative therapy in those with incomplete motor SCI.
These electrical signals – the same as those a doctor looks at when running an electroencephalogram (EEG) test – were sent to a computer, which “decoded” the brain waves.
Although Fritz is now only able to walk a short distance with a mechanical aid, researchers at the University of California have said the technology represents a promising yet incremental achievement in the development of brain-computer interfaces.
Mental training was initially required to reactivate the participant’s ability to use his brain power to walk, according to the study.
First, the patient was taught to control a virtual reality “avatar” with his brainwaves and given exercises to recondition and strengthen his leg muscles.
The participant later practiced walking while suspended 5cm above ground, so he could freely move his legs without having to support himself. On his 20th visit, equipped with a support system to avoid falls and take some of his body weight, he managed to put one foot after the other along a 3.66 m (12 ft) walking course.
Spinal cord stimulation using BCIs offers hope of regaining voluntary lower extremity movements to those with SCI. It would enable intuitive and direct brain control of walking via an external device. “However, independent over-ground walking is still some way off, not least because the issue of maintaining balance hasn’t yet been addressed”.
Spinal cord injuries only sever the neural connection to the legs, but the region of the brain that is responsible for sending the command to move the legs is not affected.
The breakthrough is owed to a functional electric stimulation (FES) device, which essentially acts as a communicator between Fritz’s brain and legs.
Their novel approach permitted the young man, who has complete paralysis of both legs due to spinal cord injury, to take steps without relying on manually controlled robotic limbs.
“Walking is a very fundamental behavior for us”, he said, pointing out that sitting can affect a person’s cardiovascular health or their bladder control. The computer works in such a way that it interprets received brain waves as an intention to either walk or stand still.
Researchers said the goal of testing a BCI system is to develop a brain implant that can communicate with electrodes in the legs, however researchers said a noninvasive version allows for better testing of the method.
“We hope that an implant could achieve an even greater level of prosthesis control because brain waves are recorded with higher quality”, he added.
Dr. Miguel Nicolelis, professor of neurobiology and the director for the Center of Neuroengineering at Duke University, said the study was exciting, but emphasized that the dramatic results will need to be replicated in other paraplegic patients.
Functional Electrical Stimulation (FES) is a technique of eliciting controlled neural activation through the application of low levels of electrical current. FES was initially referred to as Functional Electrotherapy by Liberson  and it was not until 1967 that the term Functional Electrical Stimulation was established by Moe and Post . In 1965 Offner patented a system used to treat foot drop with the title “Electrical stimulation of muscle deprived of nervous control with a view of providing muscular contraction and producing a functionally useful moment” . Another term often used equally to FES is Functional Neuromusclular Stimulation (FNS or FNMS).
The first commercially available FES devices treated foot drop in hemiplegic patients by stimulating the peroneal nerve during gait. In this case, a switch, located in the heel end of a user’s shoe, would activate a stimulator worn by the user.
Structural discontinuity in the spinal cord after injury results in a disruption in the impulse conduction resulting in loss of various bodily functions depending upon the level of injury. The initial goal of FES technology was to provide greater mobility to the patients after SCI. However, with the advances in biomedical engineering within the last 2 decades, FES is no more limited to locomotion alone. Therefore, the definition of FES has changed considerably and is now considered to be the technique of applying safe levels of electric current to stimulate various organs of the body rendered disabled due to SCI. Electrical stimulation in the form of functional electrical stimulation (FES) can help facilitate and improve limb mobility along with other body functions lost due to injury e.g. sexual, bladder or bowel functions.