Posts Tagged FES
[Abstract] Gait rehabilitation using functional electrical stimulation induces changes in ankle muscle coordination in stroke survivors: a preliminary study
Background: Previous studies have demonstrated that post-stroke gait rehabilitation combining functional electrical stimulation applied to the ankle muscles during fast treadmill walking (FastFES) improves gait biomechanics and clinical walking function. However, there is considerable inter-individual variability in response to FastFES. Although FastFES aims to sculpt ankle muscle coordination, whether changes in ankle muscle activity underlie observed gait improvements is unknown. The aim of this study was to investigate three cases illustrating how FastFES modulates ankle muscle recruitment during walking.
Methods: We conducted a preliminary case series study on three individuals (53-70y; 2M; 35-60 months post-stroke; 19-22 lower extremity Fugl-Meyer) who participated in 18 sessions of FastFES (3 sessions/week; ClinicalTrials.gov: NCT01668602). Clinical walking function (speed, six-minute walk test, and Timed-Up-and-Go test), gait biomechanics (paretic propulsion and ankle angle at initial-contact), and plantarflexor (soleus) / dorsiflexor (tibialis anterior) muscle recruitment were assessed pre- and post-FastFES while walking without stimulation.
Results: Two participants (R1, R2) were categorized as responders based on improvements in clinical walking function. Consistent with heterogeneity of clinical and biomechanical changes commonly observed following gait rehabilitation, how muscle activity was altered with FastFES differed between responders.R1 exhibited improved plantarflexor recruitment during stance accompanied by increased paretic propulsion. R2 exhibited improved dorsiflexor recruitment during swing accompanied by improved paretic ankle angle at initial-contact. In contrast, the third participant (NR1), classified as a non-responder, demonstrated increased ankle muscle activity during inappropriate phases of the gait cycle. Across all participants, there was a positive relationship between increased walking speeds after FastFES and reduced SOL/TA muscle coactivation.
Conclusion: Our preliminary case series study is the first to demonstrate that improvements in ankle plantarflexor and dorsiflexor muscle recruitment (muscles targeted by FastFES) accompanied improvements in gait biomechanics and walking function following FastFES in individuals post-stroke. Our results also suggest that inducing more appropriate (i.e., reduced) ankle plantar/dorsi-flexor muscle coactivation may be an important neuromuscular mechanism underlying improvements in gait function after FastFES training, suggesting that pre-treatment ankle muscle status could be used for inclusion into FastFES. The findings of this case-series study, albeit preliminary, provide the rationale and foundations for larger-sample studies using similar methodology.
[VIDEO] Split-Crank Functional Electrical Stimulation Cycling: An Adapting Admitting Rehabilitation Robot – YouTube
Electronic devices are helping stroke patients walk and move their hands again.
This may bode well for the 20 percent of survivors that have foot drop, and 87 percent of stroke survivors that have lost the use of their hands.
When a person has a stroke, multiple sclerosis or brain injury, most of the neurons that help signal muscles to move are broken. This keeps the brain from being able to send signals to certain muscle groups telling them to move.
A stroke, for example, can destroy millions of brain cells that you need to tie your shoes, pick up a grandchild or reach into your closet. To gain lost function, rehabilitation used to focus on teaching patients how to compensate for their physical deficits.
Today, research shows that neural plasticity (the ability of the brain to repair itself) can be applied effectively for improved outcomes and enhanced functional abilities.
To do this successfully, the central nervous system must seek other neural pathways and find new connections that bypass the damaged areas. With a little help from functional electrical stimulation (FES), which is low energy electrical pulses, the process to find the new connections is a bit easier.
New electrical orthotics target muscles with FES and can help accelerate muscle-nerve recovery. The electronic orthosis and its control unit transmit synchronized electric pulses to the peripheral nerves through electrodes built into the orthosis — these pulses are driven in precise sequence and accurately activate five muscles in the forearm.
“Muscles relearn when electrical stimulation provides feedback to the brain that can facilitate neuro re-education and promote neuroplasticity, which is the ability of the central nervous system to remodel itself,” says physical therapist Imelda Ungos, director of rehabilitation for Melbourne Terrace, a facility that specializes in the active and aging population. “And patients can learn a better way to function just by having new input, regardless of age.”
Ungos reports that the ultimate goal with this method of therapy is to restore voluntary movement. Patients with a history of brain lesions, such as stroke conditions and movement disorders, may have the most to gain with the neuro-orthotics and the rehab to learn how to use them.
“The latest therapy equipment from Bioness can drive the brain to new connections, and newer technology and techniques encourage the neuronal changes necessary for improved function,” says Ungos. “This kind of therapy is very specialized, and we’re the only sub-acute facility in the Space Coast area with the Bioness FES technologies,” says Ungos.
For improved hand function, the orthosis fits to the forearm and wrist, and communicates wirelessly with the control unit. Inside the orthosis, electrodes deliver mild pulses to stimulate muscle contraction.
The level of stimulation can be adjusted toward each function. With an intuitive interface, clinicians are better able to help their patients obtain simple control of desired hand activation.
The wireless device is portable and allows for quick detection of the best electrode position for each individual. A control unit enables easy programming of functional modes and training regimens.
For patients with poor safety and balance due to foot drop, which is the inability to lift the foot during walking, there’s an electronic orthosis that fits below the knee. The unit has stimulating electrodes placed over the correct nerve and fits below the knee. A heel sensor sends a muscle-contracting signal during the correct step phase to enable the foot to lift.
After the initial custom fitting of the orthosis, patients can enhance their abilities to perform daily activities, and the carry-over results from continued use will improve voluntary movement.
Ungos adds that the other benefits of interacting with the device include a reduction in muscle spasm, an increase in range of motion, and improved blood circulation. “That all goes towards retarding disuse atrophy,” she says.
“Efforts must be directed towards preventing complications and learning how to use affected limb along with active rehabilitation… especially when the use is started early in post stroke rehabilitation,” says online Bioness reports from Harold Weingarden, MD, Director of Rehabilitation Day Hospital Sheba Medical Center in Israel.
“An early start to rehab gives patients hope of what is possible in terms of present and future improvement,” says Ungos. She adds that the devices allow patients to move in more natural ways.
Feeling “normal” again can improve mood, function, and quality of life.
For more information, call Melbourne Terrace Rehabilitation Center at 321-725-3990. They offer comprehensive rehabilitative outpatient and inpatient services for short or long term care located at 251 East Florida Ave., Melbourne, FL 32901
[BOOK] 22 ANNUAL CONFERENCE OF THE INTERNATIONAL FUCTIONAL ELECTRICAL STIMULATION SOCIETY – Abstracts
Enhancing quality of life
through electrical stimulation technology
through electrical stimulation technology
22. ANNUAL CONFERENCE OF THE
ELECTRICAL STIMULATION SOCIETY
All of the abstracts presented are available on line at http://ifess2018.com/down/IFESS2018_program.pdf
[ARTICLE] Speed-adaptive control of functional electrical stimulation for dropfoot correction – Full Text
Functional electrical stimulation is an important therapy technique for dropfoot correction. In order to achieve natural control, the parameter setting of FES should be associated with the activation of the tibialis anterior.
This study recruited nine healthy subjects and investigated the relations of walking speed with the onset timing and duration of tibialis anterior activation. Linear models were built for the walking speed with respect to these two parameters. Based on these models, the speed-adaptive onset timing and duration were applied in FES-assisted walking for nine healthy subjects and ten subjects with dropfoot. The kinematic performance of FES-assisted walking triggered by speed-adaptive stimulation were compared with those triggered by the heel-off event, and no-stimulation walking at different walking speeds.
Higher ankle dorsiflexion angle was observed in heel-off stimulation and speed-adaptive stimulation conditions than that in no-stimulation walking condition at all the speeds. For subjects with stroke, the ankle plantarflexion angle in speed-adaptive stimulation condition was similar to that in no-stimulation walking condition, and it was significant larger than that in heel-off stimulation condition at all speeds.
The improvement in ankle dorsiflexion without worsening ankle plantarflexion in speed-adaptive stimulation condition could be attributed to the appropriate stimulation timing and duration. These results provide evidence that the proposed stimulation system with speed-related parameters is more physiologically appropriate in dropfoot correction, and it may have great potential value in future clinical applications.
About three quarters of stroke survivors experience different levels of brain dysfunction and movement disorder , which result in lower living quality and limited ability in social activities . Of these subjects, 20% suffer from impaired motor function in the lower extremities. One of such impairments is dropfoot, which is characterized by poor ankle dorsiflexion during the swing phase and an inability to achieve heel strike at the initial contact [3, 4]. Abnormal gaits such as circumduction gait and abnormal foot clearance on the affected side are often found as a method of compensating for excessive hip abduction and pelvis elevation on the unaffected side . This results in gait asymmetry and slow walking speed .
Functional electrical stimulation was a representative intervention to correct dropfoot and Liberson et al. first introduced functional electrical stimulation (FES) to correct dropfoot for chronic hemiplegic subjects in the 1960s . An electrical charge is delivered via a pair of electrodes to activate the tibialis anterior (TA), which results in ankle dorsiflexion. Yan et al. applied two dual-channel stimulators to the quadriceps, hamstring, gastrocnemius, and TA to recover motor function of the lower extremities in an early stage after stroke . The stimulation was followed by a predetermined sequence of muscle activations that mimic a healthy gait cycle . The duration of stimulation was five seconds in Yan et al.’s study. However, subjects with different severities of impairment might have different walking speeds , which means that a fixed stimulation duration might not be able to account for different walking patterns.
Liberson et al. used the heel-off event detected by a footswitch to trigger the stimulation . However, the reliability of the footswitch controller was significantly reduced when subjects who dragged their feet during walking encountered a slope or an obstacle . Bhadra et al. proposed a manual switch to trigger stimulation as a walking aid for subjects with spinal cord injury (SCI) . However, manual control may distract subjects from maintaining balance and lead to an increased risk of falls [13, 14]. Furthermore, the cable between the control sensor and stimulator was inconvenient for walking .
Instead of a footswitch, Mansfield et al.  and Monaghan et al.  detected the heel event of the gait cycle in FES-assisted walking using an accelerometer and a uniaxial gyroscope, respectively. The commercially available product WalkAide also uses an accelerometer for this purpose . Electromyography (EMG) signal is also applied as a control source in FES-assisted walking for the detection of volitional intent of muscle . Yeom et al. amplified the EMG signal of the TA and modulated the stimulation intensity in proportion to the integrated EMG envelope. The electrical pulses are then sent to the common peroneal nerve for dropfoot correction .
In these studies, FES applied to the TA was mainly triggered by the heel-off event. However, this event occurs during the push-off phase and before TA activation . An earlier start of TA stimulation results in reduced ankle plantarflexion . Spaich et al. suggested implementing a constant time interval before the onset timing of TA stimulation to extend the push-off phase before the ankle dorsiflexion . Some studies have found that walking speed can affect the activation of TA [22, 23]. Shiavi et al. found that the duration of EMG activity decreased as speed increased . In Winter et al.’s study, the shape of the EMG patterns generally remained similar at the different walking speeds and the duration of EMG activity was closely related to the normalized stride time . Although the duration of TA activation changes with the walking speeds has been reported , the selection of speed-adaptive FES parameters for TA has not been investigated.
The objective of this study is to find a more physiologically appropriate FES design for dropfoot correction. Firstly, speed-related changes in onset timing and the duration of TA activation were examined. Next, linear models were built for the walking speed and time interval from the heel-off event to the onset timing of TA activation, as well as for the walking speed and the duration of the TA activation. The speed-adaptive stimulation (SAS) timing and duration were then calculated based on the two models and applied for FES-assisted walking. Finally, the performance of stimulation triggered by SAS, heel-off event (HOS) and no stimulation (NS) were compared during FES-assisted walking on both subjects with stroke and healthy subjects at different walking speeds.[…]
Community Regional Medical Center is currently part of the first study on the west coast working with a device that helps stimulate muscles when a patient is not able to do it themselves.
Brain-computer interfaces, or BCIs, represent relatively recent advances in neurotechnology that allow computer systems to interact directly with human or animal brains. This technology is particularly promising for use in cases of spinal cord injury or paralysis. In these situations, patients may be able to use neural decoders that access part of their brain to operate a prosthetic limb or even to re-animate a paralyzed limb through functional electrical stimulation (FES).
Michael A. Schwemmer and colleagues, in a recent Nature Medicine article, detail their research on BCIs using deep neural network decoders with a participant with tetraplegia due to spinal cord injury. Their research focuses on addressing several key needs identified by end-users of BCI systems, namely: high accuracy, minimal daily setup, rapid response time, and multifunctionality—all of which are characteristics heavily influenced by a BCI’s particular neural decoding algorithm.
Schwemmer’s group describes several different approaches to training and testing three variations on neural network decoders (NN-BCI) in comparison with each other and a benchmark support vector machine (SVM) decoder. The four BCI decoder paradigms were developed and tested over the course of several years in association with a 27-year-old male participant with tetraplegia. The participant had a 96-channel microelectrode array implanted in the area of his left primary motor cortex corresponding to the hand and arm. Using intracortical data collected from 80 sessions over 865 days, the investigators trained and evaluated these BCI decoders. These sessions consisted of two 104-second blocks of a four-movement task: index extension, index flexion, wrist extension, and wrist flexion.
The initial neural network (NN) model was developed and calibrated using data from the first 40 sessions (80 blocks); it was not updated over the second half of the training/testing period, and is referred to here as the fixed neural network (fNN) model. From the fNN, two other neural network models were created: a supervised updating (sNN) model and an unsupervised updating (uNN) model. Both models used data from the first block of the second 40-session (updating/testing) period. The sNN model’s algorithm relies on explicit training labels, that is, known timing and type of movement, whereas the uNN model relies on undifferentiated or unknown direct input in relation to intended action of the limb. The second block of the second 40-session period was used for accuracy testing of all models—fNN, sNN, uNN, and SVM.
The purpose of using four separate models here was to test and demonstrate various aspects of the three neural network models in relation to each other and the benchmark SVM model. For instance, the supervised neural network (sNN) model was updated daily (during the first block of the second 40-session period) and compared directly with the daily-retrained SVM model. The fixed neural network (fNN) model was provided to demonstrate that a BCI could sustain accuracy for over a year with no updates.
The unsupervised neural network (uNN) was perhaps the most interesting comparator, as we shall see, because it attempted to combine the improved accuracy gained from daily updates but without the consequent daily setup time required by the sNN model. Accuracy was the key performance measure in all tests, defined here as a percentage of correctly predicted time-bins in the second block of the second 40 sessions; the criterion of greater than 90% accuracy was one of the four end-user requirements originally articulated at the outset of the study.
The sNN consistently outperformed the daily-retrained SVM: in 37 out of 40 sessions, its accuracy was > 90%, whereas the SVM only achieved > 90% accuracy in 12 sessions. The fNN also outperformed the SVM in 36 of 40 sessions; it achieved > 90% accuracy in 32 sessions. The fNN accuracy was, not surprisingly, lower than the accuracy of the sNN, and both fixed decoders, fNN and SVM, declined in accuracy over the course of the study period, in contrast to the daily-updated decoders.
Perhaps the most interesting finding of this research however, is the performance of the unsupervised neural network (uNN), which outperformed both fixed models in terms of accuracy, while also meeting the end-user requirement of minimal daily set-up. Where the sNN model required explicit daily training, the uNN incorporated data from general use in its update schema, which required no such daily set-up. In comparison with the fNN, a performance gap emerged over time, and the benefits of the uNN distinguished themselves. The uNN also outperformed the SVM in terms of response time, another key end-user requirement.
Another important aspect of this study with regard to NNs focused on transfer learning, whereby new movements can be added to the existing repertoire with minimal additional training and data. In this case, “hand open” and “hand close” were added to the previous four movements, and all decoders were rebuilt. Here too, unsupervised updating was used to build an unsupervised transfer neural network (utNN), which, after only one session of training oupterformed the SVM model.
Finally, the previous research—all of which was conducted in an “offline” setting—was applied, via the participant’s FES-controlled hand and forearm, to show that a transfer learning uNN trained on the original four-movement task could be used to quickly create a new decoder to control, in real time, an open hand and three grips (can, fork, and peg). In a test of the system, the participant was able to perform all three hand movement grip tasks, with no failures, in 45 attempts. Previously, he was only able to perform one grip task successfully.
In summarizing how the results of their study relate to the main end-user expectations previously described, the investigators cite the following achievements: “(i) using deep NNs to create robust neural decoders that sustain high fidelity BCI control for more than a year without retraining; (ii) introducing a new updating procedure that can improve performance using data obtained through regular system use; (iii) extension of functionality through transfer learning using minimal additional data; and (iv) introducing a decoding framework that simultaneously addresses these four competing aspects of BCI performance (accuracy, speed, longevity, and multifunctionality). In addition, we provide a clinical demonstration that a decoder calibrated using historical data of imagined hand movements with no feedback can be successfully used in real-time to control FES-evoked grasp function for object manipulation.”
Schwemmer and colleagues go on to offer a more in-depth discussion of their results amidst the broader landscape of BCI research, and offer commentary on some of the specific challenges and limitations of their experiment. While noting that the median response time for uNN decoders (0.9 s) is still faster than that of SVM decoders (1.1 s), they acknowledge that a target of 750 ms or less is probably closer to realistic end-user expectations.
Ultimately they conclude: “We have demonstrated that decoders based on NNs may be superior to other implementations because new functions can be easily added after the initial decoder calibration using transfer learning. Crucially, we show that this secondary update to add more movements requires a minimal amount of additional data.” And “insights gained from offline data and analyses can carry over to a realistic online BCI scenario with minimal additional data collection.”
Explore further: Using multi-task learning for low-latency speech translation
More information: Michael A. Schwemmer et al. Meeting brain–computer interface user performance expectations using a deep neural network decoding framework, Nature Medicine(2018). DOI: 10.1038/s41591-018-0171-y
September 14, 2018 by Ioannis Dimitrios Zoulias, The Conversation
Could the answer to mobility problems one day be as easy as pulling on a pair of trousers? A research team led by Bristol University’s Professor Jonathan Rossiter has recently unveiled a prototype pair of robotic trousers that they hope could help some disabled people walk without other assistance.
As an engineer who researches ways of helping people with spinal chord injuries move their limbs again, I’m acutely aware of how the loss of mobility can affect a person’s quality of life, and how restoring that movement can help. Given the staggering number of people with disabilities (over 6.5m people with mobility problems in the UK alone) and our ageing population, devices that improve mobility could help a large segment of the population.
Yet despite 50 years of research, this kind of technology has rarely been adopted outside the lab. So is the novel development of robotic trousers on course to finally take a working mobility technology into the home?
Unlike the rigid robotic device in the Wallace and Gromit animated film The Wrong Trousers, the new so-called “Right Trousers” use soft artificial muscles to create movement, as well as harnessing the wearer’s real muscles. These mimic human muscles in producing a force simply by becoming shorter and pulling on both ends.
By bundling several artificial muscles together, the assistive trousers can move a joint such as the knee, and help the user with movements such as standing up from a chair. Because the artificial muscles are elastic and soft they are safer than traditional motors used in rigid robotic exoskeletons that, although powerful, are stiff and uncomfortable.
The researchers have put forward several different ideas for how to shorten the artificial muscles and create movement. One design adapts the concept of air muscles, which are effectively balloons that expand sideways and shorten in length as they fill with air.
Another proposed design uses electricity to shorten an artificial muscle made from a gel placed between two copper plates. The gel is attracted to areas of high electrical voltage. So creating two different voltages in the plates forces the gel to shrink around one of them, bringing them closer together and shortening the muscle.
Another technology integrated in the assistive trousers is functional electrical stimulation (FES). Electrodes woven into the trousers strategically located over muscles can send specially designed electrical impulses into the body to hijack the communication channel between the brain and the muscles and directly command muscles to contract. By using existing muscles and bypassing the brain, the assistive trousers can even command muscles that the wearers might have difficulty using on their own (for example due to stroke).
The trousers can also help users who struggle to stand for any length of time thanks to specially made plastic knee braces that stiffen as they cool. Controlling the temperature of the braces allows the knee to move or lock in position to maintain standing without much effort needed by the muscles (real or artificial).
Other features include an automatic belt, using a mechanism similar to the air muscles, to make it easy and safe to put on and take off the trousers.
The researchers suggest creating an embedded electronic system that receives information about the wearer’s motion and state from sensors embedded throughout the trousers, and controls all of the systems to tailor movements to the user’s needs. The electronics would allow users to control their movement via controls directly woven onto the trousers. The challenge will be to time the movement of the artificial muscles and the electrical stimulation of the real muscles in response to the user’s posture.
The Right Trousers are unique in their approach to merging cutting edge research and well-established techniques in a single prototype. Aside from the novelty of robotic trousers, what makes the device so compelling as a practical assistive technology is the fact it can be adapted to many different users. This raises the hope it could be widely adopted where other previous ventures have failed.
However, this is only the prototype. A working product is probably at least five years away and significant questions must be answered to get to that stage. Where will it store all the power it needs? How can all the systems be miniaturised and embedded in the trousers so they don’t become bulky and awkward to wear? Can the controller predict the best action to take amid the ever-changing complexity of real environments where users will be walking?
Yet other technologies have the potential to improve the trousers even further. Brain-computer interfaces that can decode brain signals are now being used in systems that help paralysed people move again. Controlling the assistive trousers by thought could make taking a step effortless again for many people.
Functional Electrical Stimulation (FES) is an innovation in the field of muscle stimulation, which allows people with a complete spinal cord injury and paralyzed muscles to move again. It can be combined with a BerkelBike or EasyLegs. The technology allows patients with a spinal cord injury to bike using their own leg muscles.
What is Functional Electrical Stimulation? This video posted by Active Linx demonstrates the benefits of FES.