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Posts Tagged FES
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.
When there is a loss of muscular functioning in an area or sensory loss on area resulting usually from any damage to central nervous system, there is paralysis. Some of the probable causes of this dangerous condition are polio, stroke, excessive trauma or multiple sclerosis, etc. There may be complete paralysis or partial paralysis. It is mainly of two kinds, namely, paraplegia and quadriplegia. Paralysis is the consequence when the brain fails to send signals to various regions of the body. This may result from a variety of reasons. Stroke accounts for 30% of paralysis cases and is the major cause. However, one can choose paralysis treatment depending on the severity of the condition and the region which is paralyzed.
How is paralysis diagnosed?
On the event of any failure of muscular functioning or sensory loss on certain area, it is important to visit a medical practitioner immediately. To diagnose the condition, he prescribes a series of tests including CT Scan, MRI, X-Ray, Electromyography. If at all it is necessary, the patient may be suggested a neurologist. After paralysis is confirmed, the treatment begins. Certain types of paralysis may be cured and this mainly includes partial paralysis. You can ask the doctor whether the recovery is possible or not. No matter what the cause of the condition, the treatment procedure will be almost the same. Whatever treatment you choose for recovery, the treatment provider will try and restore brain and body connection. This is the only way to bring about recovery.
Some of the basic treatment options for paralysis
Wearable device running on electricity is the most basic treatment for paralysis. This wearable electronic device is also used for stroke treatment. It improves arm functioning and restores motion in the arms. When you wear this device, it delivers electrical current to activate the muscles of arms and legs. This technique of motion restoration is also termed as FES or Functional Electrical Stimulation. It can recover the feet or lower legs from paralysis. The use of FES along with specific exercises can bring about a relief.
Some of the best treatment options for paralysis
If anyone of your loved one is suffering from paralysis, read the following section to learn how to reduce the symptoms:
- Surgery can address physical barriers. It may be that there is an object fixed in the brain or spinal cord of the person. It needs to be got rid of. Through the surgery, certain portions of the spinal cord can also be fused together.
- Some paralysis medication may be used to reduce swelling, inflammation and infection on the area. If there is chronic pain, it may be addressed with medicines.
- Continuous monitoring of the person is mandatory to ensure that this condition does not get worse
- Psychotherapy can help a lot. Support groups may teach you how to cope with this critical situation.
- To restore muscular and nerve functioning, you may be asked to do certain exercises. Occupational therapy can also help a lot. Work on the injuries and practice them as much as possible. Physical therapy may reverse paralysis by rewiring the brain.
- Some people got great results from alternative treatments like chiropractic care, massage therapy and acupuncture treatment.
If there are breathing difficulties, problem in the bowel movement, take immediate treatment for them. Again, surgery is an effective sleep apnea treatment. Whether it is sleep apnea or paralysis, immediate medical attention is required.
[BLOG POST] Understanding the factors that impact the effectiveness of Functional Electrical Stimulation (FES) – pulse width and charge & torque
In the final of a series of blog articles, we are going to look at the factors that impact the effectiveness of FES. This one covers pulse width and charge & torque.
The available pulse widths in FES devices vary, most commonly between 150 and 300us, however much wider variations (50us to 2500us) in pulse width can have differing effects upon the target muscle tissue.
Practically, a longer pulse width causes the stimulus to remain in the tissues for longer, depolarising a greater number of nerve fibres, indiscriminate of motor, sensory or pain. Higher pulse widths have been shown to generate greater levels of torque and can often allow tetanic muscle contractions resulting in physiological joint movement at lower levels of amplitude, which can be useful when attempting to maximise torque in those with intact sensation.
However, when looking for a specific muscle contraction, for example a bicep’s, if too great a pulse width is applied it is common to see overflow into surrounding or opposing muscle groups. Compared to pulse frequency and current amplitude, the role of pulse duration is less appreciated in its possible influence on maximising torque output.
Alon et al back in 1983 showed that motor stimulation could be achieved with pulse durations in the range of 20 to 200 microseconds, without stimulation of pain response. In contrast, Hultman et al (1983) showed that a pulse duration of 500 microseconds resulted in 40% greater torque output compared to 150 microseconds.
Moreover, a pulse duration of 450 microseconds has been shown to be effective in conducting electrically induced resistance training in individuals with spinal cord injury (Kendell et al., 2006, Burnham et al., 1997, cited by Dolbow and Gorgey, 2016).
However, despite this evidence, most researchers have used pulse durations of 300 microseconds or below in their studies, which could potentially limit the outcome of Neuromuscular Electric Stimulation (NMES) protocols in maximising elicited torque output. The controversy regarding pulse duration selection reflects the limited amount of knowledge regarding the optimal pulse duration required to maximise torque output.
Charge & Torque
Total charge, the product of combined amplitude and pulse width, determines the force produced from the resultant muscle contraction. Maximising the charge, by applying maximal amplitude and pulse width, is likely to result in the maximum torque.
However, as stated above, patient tolerance is the determinant of how much charge may be applied. Manipulating both amplitude and pulse width can help to generate sufficient charge to result in a forceful muscle contraction, without becoming unbearable for the patient.
This article is taken from our white paper “The integration of Functional Electrical Stimulation (FES) technology and neurorehabilitation”.
[Abstract + References] A Multi-channel EMG-Driven FES Solution for Stroke Rehabilitation – Conference paper
Functional electrical stimulation (FES) has been applied to stroke rehabilitation for many years. However, users are usually involved in open-loop fixed cycle FES systems in clinical, which is easy to cause muscle fatigue and reduce rehabilitation efficacy. This paper proposes a multi-surface EMG-driven FES integration solution for enhancing upper-limb stroke rehabilitation. This wireless portable system consists of sEMG data acquisition module and FES module, the former is used to capture sEMG signals, the latter of multi-channel FES output can be driven by the sEMG. Preliminary experiments proved that the system has outperformed existing similar systems and that sEMG can be effectively employed to achieve different FES intensity, demonstrating the potential for active stroke rehabilitation.
[Abstract] Combining functional electrical stimulation and mirror therapy for upper limb motor recovery following stroke: a randomised trial
Introduction: There is a growing need to develop effective rehabilitation interventions for people presenting with stroke as healthcare services experience ever-increasing pressures on staff and resources. The primary objective of this research is to examine the effect that mirror therapy combined with functional electrical stimulation has on upper limb motor recovery and functional outcome for a sample of people admitted to an inpatient stroke unit.
Methods: A total of 50 participants were randomised to one of three treatment arms; Functional Electrical Stimulation, Mirror therapy or a combined intervention of Functional Electrical Stimulation with Mirror therapy. Socio-demographic and health information was collected at recruitment together with admission dates, medical diagnoses and baseline measures. Blinded assessments were undertaken at baseline and at discharge post-stroke by a registered physiotherapist and a clinical nurse specialist.
Results: The Action Research Arm Test and the Fugl–Meyer Upper Extremity assessment revealed statistically superior results for Functional Electrical Stimulation compared with Mirror therapy alone (p = 0.03). There were no other significant differences between the three groups.
Conclusion: The theory of combining interventions requires further investigation and warrants further research. Combining current interventions may have the potential to enhance stroke rehabilitation, improve functional outcomes and help reduce the overall burden of stroke.
[Abstract + References] Using Orientation Sensors to Control a FES System for Upper-Limb Motor Rehabilitation
Contralaterally controlled functional electrical stimulation (CCFES) is a recent therapy aimed at improving the recovery of impaired limbs after stroke. For hemiplegic patients, CCFES uses a control signal from the non-impaired side of the body to regulate the intensity of electrical stimulation delivered to the affected muscles of the homologous limb on the opposite side of the body. CCFES permits an artificial muscular contraction synchronized with the patient’s intentionality to carry out functional tasks, which is a way to enhance neuroplasticity and to promote motor learning. This work presents an upper extremity motor rehabilitation system based on CCFES, using orientation sensors for control. Thus, the stimulation intensity (current amplitude) delivered to the paretic extremity is proportional to the degree of joint amplitude of the unaffected extremity. The implemented controller uses a control strategy that allows the delivered electrical stimulation intensity, to be comparable to the magnitude of movement. It was carried out a set of experiments to validate the overall system, for executing five bilateral mirror movements that include human wrist and elbow joints. Obtained results showed that movements voluntary signals acquired from right upper-limb were replicated successfully on left upper-limb using the FES system.
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[Abstract] Within-session effects of selected physical rehabilitation interventions for a dysfunctional arm post-stroke on arm movement and muscle firing patterns
Upper extremity (UE) impairments and activity limitations are a common problem in individuals following a cerebrovascular accident (CVA). Eighty-five percent of individuals with CVA report UE functional limitations that are associated with decreased health-related quality of life. Occupational therapy (OT) and physical therapy (PT) approaches are typically aimed to treat impairments, activity limitations, and participation restrictions following a CVA. This study examines the effects of five therapeutic approaches on upper extremity (UE) movement and muscle activation patterns in persons with CVAs: (1) proprioceptive neuromuscular facilitation (PNF); (2) neurodevelopmental treatment (NDT); (3) functional electrical stimulation (FES); (4) weight-bearing and (5) modified Constraint-Induced Movement Therapy (mCIMT).
Material and method
This is a case report involving a 61-year-old male who underwent 30-minute intervention sessions for each approach stated above. Electromyography (EMG) and 3D motion capture data were collected pre- and post-intervention and at 30 minute follow-up. Data were analyzed for reaching a cup at waist level, maximum shoulder flexion, and moving cup to mouth as in drinking.
No significant differences were seen for UE movements across all interventions for kinematic or EMG data. There appears to be a trend towards normal elbow movement following NMES, mCIMT and PNF and increased variability in shoulder flexion in mCIMT and NDT interventions. Weight-bearing provided the least amount of evidence for improved kinematic motion. Improvement in elbow kinematics may indicate proximal stability following PNF, FES, and mCIMT allows for increased distal mobility at the elbow.
Some interventions produced trends that indicate better UE movement. Increased proximal stability may have caused better distal mobility as shown by improved elbow movement. Increased variability of shoulder flexion may indicate the participant learned different options to perform the same movement. Further research is needed o provide a more transparent understanding of the efficacy of interventions for individuals with hemiparesis following a CVA.