State of the Art Robotic Foot Fully Controlled by the Brain
Neuropsychiatr Dis Treat. 2017; thirteen: 1303–1311.
Robot-assisted gait training for stroke patients: electric current land of the fine art and perspectives of robotics
Giovanni Morone
1Individual Inpatient Unit
2Clinical Laboratory of Experimental Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italian republic
Stefano Paolucci
1Individual Inpatient Unit
2Clinical Laboratory of Experimental Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italian republic
Andrea Cherubini
3Section of Robotics, LIRMM UM-CNRS, Montpellier, France
Domenico De Angelis
iPrivate Inpatient Unit
Vincenzo Venturiero
anePrivate Inpatient Unit
Paola Coiro
iPrivate Inpatient Unit
Marco Iosa
1Private Inpatient Unit
2Clinical Laboratory of Experimental Neurorehabilitation, IRCCS Santa Lucia Foundation, Rome, Italian republic
Abstract
In this review, we give a brief outline of robot-mediated gait grooming for stroke patients, as an important emerging field in rehabilitation. Technological innovations are assuasive rehabilitation to move toward more integrated processes, with improved efficiency and less long-term impairments. In particular, robot-mediated neurorehabilitation is a quickly advancing field, which uses robotic systems to define new methods for treating neurological injuries, peculiarly stroke. The use of robots in gait preparation can enhance rehabilitation, but it needs to be used according to well-divers neuroscientific principles. The field of robot-mediated neurorehabilitation brings challenges to both bioengineering and clinical practice. This article reviews the state of the art (including commercially available systems) and perspectives of robotics in poststroke rehabilitation for walking recovery. A critical revision, including the problems at stake regarding robotic clinical use, is also presented.
Keywords: exoskeleton, neurorehabilitation, robot-assisted walking training, wear robot, activities of daily living, motor learning, plasticity
Video abstract
Introduction
Stroke is a leading cause of motion inability in the U.s.a. and Europe.1 By 2030, it has been estimated that at that place could exist as many equally lxx million stroke survivors around the world.2 The proportion of patients achieving independence by one year after a stroke ranges from ~60% to 83% in cocky-care and between 10% and fifteen% in a residential clinical institution.3 Apropos mobility recovery, a 2008 study showed that ~l% of patients with stroke leave the rehabilitation infirmary on a wheelchair, <15% are able to walk indoor without aids, <x% are able to walk outdoor, and <5% are able to climb stairs.4 Poststroke rehabilitation demand will increment in the near time to come, leading to stronger pressure on health intendance budgets. For example, in the U.s.a., the estimated direct and indirect cost of stroke in 2010 was $73.7 billion, and the hateful lifetime price of ischemic stroke was estimated at $140.048.v For upstanding reasons, in adjunction to these economic reasons, an increase of rehabilitation efficacy is mandatory. New technologies, early belch after intensive training, and domicile rehabilitation are among the innovations proposed for achieving this. Electric current literature suggests that rehabilitative interventions are more effective if they ensure early, intensive, chore-specific, and multisensory stimulation, with both lesser-upwards and top-downwards integration, favoring brain plasticity.6 , 7 In fact, there is growing evidence that the motor system is plastic following stroke and that motor training tin can be of help, especially in the showtime 3 months.eight Neuroplasticity can atomic number 82 to recovery mechanisms and functional accommodation resulting from global changes in neuronal organization. It is associated with changes in excitatory/inhibitory balance every bit well as the spatial extent and activation of cortical maps and structural remodeling.ix , 10
In this scenario, the emerging field of robotic rehabilitation needs to be integrated with the neurological principles supporting the scientific evidences that a robot may improve specific abilities of neurological patients. Figure 1 shows the determinants of gait rehabilitation of patients with stroke that may benefit from robotic training, including those related to other technologies such every bit serious video games and augmented biofeedback.
The determinants of gait and balance by multisystem rehabilitation of patients with stroke who may benefit from robotic training.
This review aims to exploit, by post-obit user-centered principles, the clinical efficacy of robotic devices and enhance their role in the next generation of rehabilitation protocols.
Machines for walking rehabilitation
A complete review of all the machines developed worldwide is very difficult to achieve because of the number of prototypes tested within the scientific community.
Beginning, it is necessary to clarify the deviation between a robot and other electromechanical devices. The Robot Institute of America defines a robot as
a programmable, multi-functional manipulator designed to move fabric, parts or specialized devices through variable programmed motions for the operation of a variety of tasks.11
Hence, in contrast with the popular – and erroneous–perception (which includes, eg, kitchen aids), a robot is by definition capable of mobility, with various levels of autonomy.
Based on this definition, an incomplete listing of commercial robot walk trainers includes the following: Gait Trainer (RehaStim, Berlin, Germany), G-EO (Reha Engineering science AG, Olten, Switzerland), Lokomat (Hocoma, Volketswil, Switzerland), Bionic Leg (Tibion Bionic Technologies, Moffett Field, CA, USA), eLEGS (University of California Berkeley/Ekso Bionics, Richmond, CA, U.s.a.), ReWalk (Argo Medical Technologies, Yokneam, Israel), and REX (Rex Bionics, Auckland, New Zealand). Another list may include prototypes not yet fully commercialized, such equally Lopes, Lopes 2 (developed at the University of Twente, Enschede, the Netherlands), Knexo (Vrije University Brussel, Ixelles, Belgium), Alex (University of Delaware, Newark, NJ, USA), Mindwalker (Delft University, Delft, the Netherlands), VanderBilt Exoskeleton (VanderBilt Academy, Nashville, TN, Usa), Hercule (CEA-LIST/RB3D, Paris, French republic), i-Walker (Universitat Politècnica de Catalunya, Barcelona, Spain), Walkbot (P&S Mechanics Co, Ltd, Seoul, S Korea), Walk Assist Robot (Toyota, Tokyo, Japan), Honda'southward walking assist device (Honda, Tokyo, Nihon), Anklebot (Massachusetts Found of Technology, Cambridge, MA, USA), and Indego (Parker Hannifin Corporation, El Segundo, CA, USA).7 , 12 – 14
These devices can be classified according to the movement they apply to the patient'southward body. For example, "exoskeletons" motion joints, such as hip, knee, and ankle, controlled during the gait phases, whereas "end-effector robots" move but the anxiety, often placed on a support (footplate), which imposes specific trajectories, simulating the stance and swing phases during gait grooming.fifteen Another possible nomenclature is between devices in which the patient is moved in a stock-still identify and those moving the patient around the surroundings. We could ascertain these devices every bit static and dynamic ones, respectively (Figure 2).
Theoretical schema combining patient'southward level of ability defined by functional classification of ambulation (FAC) with best possible solution in terms of walking grooming and auto constriction.
Abbreviation: BWS-TT, body weight-supported treadmill preparation.
Among static devices, ie, devices designed for performing motility in place, and not around the environs, the nigh common ones are the Lokomat, which is a robotic exoskeleton, and the Gait Trainer and Grand-EO, these last two being stop effectors.
Lokomat16 is a robotic gait orthosis combined with a harness-supported torso weight arrangement, used in combination with a treadmill. The chief difference with treadmill training with body weight support is that the patient'southward leg joints are guided according to a preprogrammed gait kinematic pattern.
Gait Trainer is based on a double creepo and rocker gear organization. In contrast with treadmills, it consists of two footplates positioned on two confined, two rockers, and two cranks, which provide the propulsion. The footplates symmetrically generate the opinion and swing phases.17 The main departure with treadmill training with body weight back up is that feet are always in contact with the platform, moving the anxiety for simulating the gait phases.
Thou-EO System ("eo" comes from Latin for "I go") is based on the end-effector principle and was designed to minimize the therapeutic effort needed for relearning walking and also stair climbing. The trajectories of the footplates as well as the vertical and horizontal movements of the middle of mass are fully programmable.18
In recent years, extensive research efforts have been dedicated to dynamic exoskeletons for neurorehabilitation, as well as war machine applications (eg, to broaden the soldiers' walking functions). Robotic hip–genu–ankle–human foot exoskeletal orthoses accept become commercially bachelor and may help poststroke patients to stand and walk over again. These devices likewise have applications beyond mobility, eg, do, amelioration of secondary complications related to lack of airing, and promotion of neuroplasticity. Wearable exoskeletons are recently developed technologies that let walking even on a difficult apartment surface.12
Exoskeletons contain the actuators that motility the patient's legs during the gait cycle, through a preprogrammed and near-normal gait cycle.nineteen Preliminary results showed the possibility of performing individual walking grooming in patients with subacute and chronic stroke.19
Almost all functional exoskeletons rely on additional support aids to ensure remainder. Healthy users would perform proper foot placement and other actions to ensure balance stability. However, impaired people would demand additional devices such as crutches.
New findings in neuroscience and translational researches from animal models showed that neurorehabilitation requires the following: increasing the therapy dosage and intensity,xx high repetitiveness,21 job-oriented exercises,22 and combination of elevation-down and bottom-upwardly approaches (eg, noninvasive brain stimulation with robot therapy).23
As aforementioned, the best time for boosting plasticity-dependent recovery is within 3 months from the stroke event. On the other paw, animal models have shown that increased therapy within 5 days from the stroke can enlarge brain damage and favor spasticity.23
Robotic systems are well suited to produce intensive, chore-oriented motor training for moving the patient's limbs under the supervision/assistance of a therapist, as office of an integrated set of rehabilitation tools that would include other new devices as well as simpler and more traditional ones.xv , 24 , 25 In this context, robots would raise conventional poststroke rehabilitation via intense and task-oriented training.
Theoretical and practical robotic support for gait rehabilitation
A common feature of gait training robots is the possibility to back up (partially or totally) the body weight and the movement of patients. Torso weight support seems to be the condition sine qua non for facilitating gait recovery with robotic devices.26 To restore gait, clinicians prefer a task-specific repetitive approach and, in recent years, better outcomes have been achieved with higher intensities of walking practice programs.27 – 29 Another office of robotic devices is to facilitate the administration, to nonambulatory patients, of intensive and highly repetitive training of complex gait cycles, something a unmarried therapist cannot easily practice alone. With respect to treadmill preparation with fractional body weight back up, still some other reward of these robotic devices may exist the reduced endeavor for therapists: they no longer need to set the paretic limbs or help body movements.30 A secondary but important characteristic related to body weight support and to robotic rehabilitation in general is the possibility of favoring the restoration of an adequate level of cardiorespiratory efficiency. Despite this attribute existence rarely taken into account in evaluating robotic efficiency,31 previous results have shown that robotic gait training reduces energy consumption and cardiorespiratory load. In fact, for severely impaired neurological patients, robotic walk training allows an early verticalization without the risk of increasing spasticity on antigravitational muscles, hence avoiding deconditioning, which would worsen cardiologic comorbidities. This is a very important feature, if one considers that cardiovascular disease is the leading prospective cause of death in people with chronic stroke.32 Information technology is well known that persons with stroke suffer an extremely poor cardiovascular fitness, with a reduction of the mobility and a consequent reduction of the quality of life.33
Energy consumption and cardiorespiratory load during walking with robot assist seems to depend non only on body weight back up but also on factors such as robot type, walking speed, and amount of try. These parameters could be adjusted during robotic rehabilitation to get in either more or less energy consuming and stressful for the cardiorespiratory system.34
Robotic rehabilitation "versus" or "together" with physiotherapy?
A recently updated Cochrane revision of 23 trials involving 999 participants showed that robotic gait training combined with physiotherapy might improve recovery of independent walking in poststroke patients. In particular, people in the offset iii months after stroke and those who are not able to walk seem to benefit nigh from this type of intervention. This review also highlighted that determining the frequency, elapsing, and timing (after stroke) for the robotic gait training to exist the most effective is still an open problem. Assessing the benefit elapsing also requires further research.35
The utilise of robots should not supercede the neurorehabilitation therapy performed past a physiotherapist. Robots, every bit all technological devices, must be considered as tools in the hands of the physiotherapist and never rehabilitative per se.36 In fact, the robot tin convalesce all labor-intensive phases of physical rehabilitation, hence allowing the physiotherapist to focus on functional rehabilitation during private training and to supervise several patients at the aforementioned time during robot-assisted therapy sessions. With this approach, the expertise and time of physiotherapists is optimized, increasing the rehabilitation program's efficacy and efficiency at the same time.37
With respect to conventional therapy alone, the addition of robotic intervention brings another important advantage: it allows an online and offline instrumented, quantitative (hence, objective) evaluation of several parameters related to patient functioning. These include range of movement, velocity, smoothness of movements, amount of forces, and then on. Thus, robotic systems may be used non just to produce simple and repetitive stereotyped movement patterns, as in the case of near of the existing devices, just likewise to generate a more than complex, controlled multisensory stimulation of the patient. This includes, simply is not limited to, the cess of the patient's performance with a biofeedback or with a report.
A contempo review, inspired by Isaac Asimov'south famous three laws of robotics and based on the nigh recent studies in neurorobotics, proposed three similar laws valid for neurorehabilitation robots. The objective was to propose guidelines for designing and using such robots.38 These laws were driven by the ethical need for rubber and effective robots, by the redefinition of their role as therapist helpers, and by the need for articulate and transparent man–machine interfaces. The 3 laws are as follows:
-
a neurorobot shall non injure a patient or let him/her come up to damage;
-
a neurorobot must obey the therapist's orders, except if such order is in conflict with the Beginning Law;
-
a neurorobot must adapt its behavior to the patients' abilities in a transparent manner, except if this is in conflict with the First or 2d law.
Although the offset constabulary may seem obvious, in the report by Iosa et al,38 the term "harm" has been redefined to include all possible damage to patients, including fourth dimension wasted on an ineffective, inefficient, or fifty-fifty detrimental robot. In fact, many robots have been commercialized without proving their quality. Hence, this law implies that robot usage should exist at least equally safe and constructive equally other treatments, implying that information technology should have a higher benefit-to-risk ratio than conventional treatments.
The second police force recalls that neurorobots are, in the first place, tools in the hands of therapists, just equally medical robots for surgeons. Robots should "disobey" clinicians' orders simply if their sensors highlight that a potential risk for the patient can be provoked past that order. This highlights the importance of sensors, which is at the base of the adaptability and autonomy of whatsoever robotic organisation.
This last aspect is reinforced in the third constabulary, which claims the importance of artificial intelligence as a support for human intelligence, with real-time accommodation to the continuously monitored and measured patient's ability.
An aspect rarely taken into business relationship in robotic rehabilitation is the psychology of the patient, who often needs not only to exist cured, simply besides to be cared. It is well known that patients' engagement and participation in conventional exercises is considered a key factor to increase rehabilitation performances and thereby heave plasticity.39 During robot-assisted therapy, this tin be accomplished via extrinsic feedback of serious game scenarios, where the scores obtained appraise the patients' operation.40 The acceptance of robotic technology by patients and physiotherapists may exist an issue per se, although there is no evidence of this for the devices adult to engagement. Nevertheless, not all patients, specially the elderly, have to be treated with a robot, and Bragoni et al41 have shown that anxiety may reduce the efficacy of robotic walking training. In the future, the cultural gap amidst technology providers, rehabilitation professionals, and terminate users should be filled by improving the broadcasting of technological knowledge and the improvidence of increasingly user-friendly and safer technology.
From "efficacy for all" to "all for efficacy"
Most studies on walking neurorehabilitation robots focus on their effectiveness, giving controversial results. For instance, Mehrholz and Pohl42 showed that patients who receive robot-assisted gait training in combination with physiotherapy achieve contained walking more hands than patients trained without these devices. However, clinical trials suggest that manual therapy may still be more than effective than robotic gait training in both subacute and chronic phases.43 , 44 The reason may be a reduction in voluntary postural command during robot-assisted gait preparation, often due to constraints presented by robots, with the passive swing assistance provided past the robotic system used in the studies.43
In the circuitous scenario of gait recovery robots, it is primal to sympathise the clinical meaning of each blueprint characteristic, such as torso weight back up, especially for training nonambulatory patients in an intensive and safety fashion.41
Both stop effectors and exoskeleton robotic devices have their ain strengths and weaknesses. It is, therefore, important to consider the rationale of the ii types of devices and the related benefits or disadvantages of each.
In particular, end-effector walking devices allow the patient to extend his/her knee with more freedom. In improver, the job of maintaining balance may be more demanding (since the required caste of residue depends on the harness setup and on whether or not the patient is belongings the hand rails). An advantage of exoskeletons is that gait cycles can be controlled more easily. We are not aware of whatever studies direct comparing different devices for gait rehabilitation in patients with a cerebral damage, with the exception of a single case report.45
Interestingly, these 2 robotic solutions train patients in ii dissimilar ways in terms of constriction/freedom of patients' ability. For this reason, they should not be seen equally alternative, but complementary: each one represents the best option for a specific kind of patient impairment.
It is important to empathise how different robotic approaches will respond to different rehabilitation problems and patients (Figure 3), as well as to all users' (patients, therapists, and clinicians) needs in general. As affirmed recently by Cochrane,35 it is imperative to define the characteristics of patients who may do good the well-nigh from robotic therapy. According to the principle that overground training is the virtually physiological i, although not always possible, the more is the severity of stroke, the more should be the assist and the constraint level provided by the device, equally shown in Figure ii.
Examples of robotic devices with different approaches.
Nigh studies aim at answering the question "are robotic devices constructive for all kinds of poststroke patients?". However, Morone et al46 have highlighted the need for irresolute this question into "for whom are robotic devices the most effective?" The goal should not be to test the efficacy for all patients only to dispose of all the possibilities, for improving efficacy. For example, the least-affected patients would rather do good from device-free conventional overground training than employ bogus interventions that may alter recovery of their physiological patterns.46
A key point for the improvidence and right use of new technologies is to know the group of patients for whom and the rehabilitation stage during which each type of technology is more beneficial. Following this principle, Morone et al46 , 47 establish that patients with more severe motor leg impairments are those who benefit the nearly from robot-assisted therapy in combination with conventional therapy. This finding probably results from the augmented intensity of robotic therapy, as compared to conventional therapy (especially for the virtually impaired patients). Conversely, patients with greater voluntary motor function in the affected limb tin perform intensive training during conventional therapy also. A large rehabilitation study (Locomotor Feel Applied Postal service-Stroke [LEAPS])48 showed that more than expensive high-tech therapy was not superior to intensive abode strength and residuum training (the so-called kitchen sink exercises), only both were amend than lower-intensity physical therapy. These results may support the idea that the bang-up advantage of robots designed for walking therapy is only related to the warranty of a more than intensive therapy. Consequently, later on xx years of investigation on robotic devices, including body weight back up systems, efficacy is withal uncertain, and nearly of the robotic utilize is still confined to research-controlled trial instead of in clinical practice.49 This skepticism has led to put into question the clinical usability of robots in neurorehabilitation: Hidler and Lumfifty questioned the possibility that these devices will go commonplace in every hospital and rehabilitation clinic or whether they will become things of the past like and then many other promising prototypes. In add-on, Iosa et al,15 after having asked "Where are the robots promised by scientific literature able to restore motor functions after stroke?", noted that despite surgical robots being introduced at effectually the same fourth dimension as rehabilitation robots, but the do good of the formers has been well established.38
On the other hand, from a theoretical point of view, many researchers agree that patients may do good from machines providing external support, until they recover the capacity of walking over ground, unsupported. Robots can favor this recovery, allowing a progressive decrease of external support matching the patients' level of gait dependency.46 Probably, the question needs to be changed from "Are robotic devices effective for rehabilitation?" to "Who may do good the near from robotic rehabilitation"?
Current perspective and open up problems
According to the current literature, information technology is non nevertheless clear how different rehabilitation approaches contribute to restorative processes of the central nervous systems afterwards stroke. In this scenario, the efficacy of robotic gait training seems to be strictly related to a good identification of the best candidates among patients of those who could benefit more from a robotic training. This choice is strictly related to both physical46 and psychological41 , 51 features with respect to the available devices.
A promising approach is the combination of different technologies, where each one facilitates the other. This is the instance of a brain-controlled neuromuscular stimulation coupled to an exoskeleton52 or of noninvasive brain stimulation (transcranial direct current stimulation [tDCS] and transcranial magnetic stimulation [TMS]) associated with robotic training.53 , 54 However, noninvasive brain stimulation associated with robots yielded limited results,55 , 56 unless the parameters were properly tuned according to the candidate patients.57
There are too some other points deserving attention. Despite most studies challenge that robots would increase rehabilitation intensity, repetition of tasks lonely is non sufficient to guide neural plasticity.58 Furthermore, most robots replicate physiological patterns, not e'er reachievable past patients. The approach is analogous to training footballers only to play many matches, without focusing the training on specific aspects and exercises that demand to be improved separately. In fact, optimal schemes of robot help to facilitate motor skill learning are debated.59 Thus, a robot is not a substitute for concrete therapists but should be considered a tool in the easily of therapists to train different determinants of a multisystem rehabilitation and for improving patients' skills.60 This leads to the need for a robot of active onboard control algorithms combined with functional motor learning tasks, to improve participation, required aid, and reinforcement learning.61
Conclusion
Finally, nearly of the robots commercialized nowadays are based on the a priori idea that walking is an automatic subcortical ability. All the same, this aspect was recently reconsidered from the post-obit perspectives: 1) from a biomechanical point of view, by reviewing the role of the trunk from a passive62 to an agile player;63 2) from a neurological point of view, in which the conventional bottom-upward approach has been integrated in a top-down arroyo;64 three) from a neuromechanical point of view, in which structures and functions are strictly connected effectually specific harmonic points of equilibrium that maximize the efficiency of walking.65 , 66
At this step, the part of the clinical researcher is to investigate whether the bachelor robot is constructive or not for the level of severity in patients with stroke admitted to his/her rehabilitation hospital. The office of bioengineers should be to match the most recent neurological findings with the specifics of the robots adult for gait training, not only simulating physiological patterns and emulating the therapist, but favoring and widening the determinants of gait recovery. Finally, both clinicians and bioengineers should interact for defining new paradigms and protocols for increasing robotic effectiveness and diffusion inside the rehabilitation teams.
Footnotes
Disclosure
The authors have no relevant affiliation or fiscal involvement with whatever organization or entity with a financial interest in or financial conflict with the subject, thing, or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock buying or options, expert testimony, grants, or patents, received or awaiting, or royalties. The authors written report no conflicts of interest in this work.
References
i. Rosamond Westward, Flegal K, Furie Grand, et al. Heart illness and stroke statistics-2007 update: a written report from the American heart association statistics committee and stroke statistics subcommittee. Circulation. 2007;115(5):e69–e171. [PubMed] [Google Scholar]
2. Feigin VL, Forouzanfar MH, Krishnamurthi R, et al. Global and regional burden of stroke during 1990–2010: findings from the Global Brunt of Disease Study 2010. Lancet. 2014;383(9913):245–254. [PMC costless commodity] [PubMed] [Google Scholar]
3. Appelros P, Nydevik I, Viitanen M. Poor outcome after first-ever stroke: predictors for decease, dependency, and recurrent stroke within the first year. Stroke. 2003;34(1):122–126. [PubMed] [Google Scholar]
4. Paolucci Southward, Bragoni Yard, Coiro P, et al. Quantification of the probability of reaching mobility independence at belch from a rehabilitation hospital in nonwalking early ischemic stroke patients: a multivariate written report. Cerebrovasc Dis. 2008;26(1):sixteen–22. [PubMed] [Google Scholar]
5. Lloyd-Jones D, Adams RJ, Brown TM, et al. Writing Grouping Members; American Centre Clan Statistics Committee and Stroke Statistics Subcommittee Heart disease and stroke statistics – 2010 update: a study from the American Middle Association. Circulation. 2010;121(7):46–215. [PubMed] [Google Scholar]
six. Belda-Lois JM, Mena-del Horno S, Bermejo-Bosch I, et al. Rehabilitation of gait after stroke: a review towards a acme-down arroyo. J Neuroeng Rehabil. 2011;eight:66. [PMC complimentary commodity] [PubMed] [Google Scholar]
vii. Masiero S, Poli P, Rosati G, et al. The value of robotic systems in stroke rehabilitation. Expert Rev Med Devices. 2014;11(2):187–198. [PubMed] [Google Scholar]
8. Wolpert DM, Diedrichsen J, Flanagan JR. Principles of sensorimotor learning. Nat Rev Neurosci. 2011;12(12):739–751. [PubMed] [Google Scholar]
ix. Huber D, Gutnisky DA, Peron S, et al. Multiple dynamic representations in the motor cortex during sensorimotor learning. Nature. 2012;484(7395):473–478. [PMC costless commodity] [PubMed] [Google Scholar]
10. Nudo RJ. Postinfarct cortical plasticity and behavioral recovery. Stroke. 2007;38(2):840–845. [PubMed] [Google Scholar]
11. Xie M. Fundamental of Robotics: Linking Perception to Action. Singapore: Globe Scientific; 2003. [Google Scholar]
12. Chen Thou, Chan CK, Guo Z, Yu H. A review of lower extremity assistive robotic exoskeletons in rehabilitation therapy. Crit Rev Biomed Eng. 2013;41(four–five):343–363. Review. [PubMed] [Google Scholar]
13. Morone 1000, Annicchiarico R, Iosa M, et al. Overground walking training with the i-Walker, a robotic servo-assistive device, enhances balance in patients with subacute stroke: a randomized controlled trial. J Neuroeng Rehabil. 2016;13(1):47. [PMC gratuitous article] [PubMed] [Google Scholar]
14. Wang Southward, Meijneke C, van der Kooij H. Modeling, design, and optimization of Mindwalker series elastic joint. IEEE Int Conf Rehabil Robot. 2013;2013:6650381. [PubMed] [Google Scholar]
15. Iosa G, Morone G, Fusco A, et al. 7 capital devices for the future of stroke rehabilitation. Stroke Res Treat. 2012;2012:187965. [PMC costless article] [PubMed] [Google Scholar]
16. Colombo G, Joerg Chiliad, Schreier R, Dietz V. Treadmill training of paraplegic patients using a robotic orthosis. J Rehabil Res Dev. 2000;37(6):693–700. [PubMed] [Google Scholar]
17. Schmidt H, Werner C, Bernhardt R, Hesse South, Krüger J. Gait rehabilitation machines based on programmable footplates. J Neuroeng Rehabil. 2007;ix(four):ii. [PMC complimentary article] [PubMed] [Google Scholar]
18. Hesse S, Waldner A, Tomelleri C. Research innovative gait robot for the repetitive do of floor walking and stair climbing upwardly and downward in stroke patients. J Neuroeng Rehabil. 2010;vii:thirty. [PMC costless article] [PubMed] [Google Scholar]
xix. Molteni F, Gasperini G, Gaffuri M, et al. Wearable robotic exoskeleton for over-ground gait grooming in sub-acute and chronic hemiparetic stroke patients: preliminary results. Eur J Phys Rehabil Med. 2017 Jan 24; Epub. [PubMed] [Google Scholar]
xx. Nelles Thousand. Cortical reorganization – furnishings of intensive therapy. Restor Neurol Neurosci. 2004;22(3–5):239–244. [PubMed] [Google Scholar]
21. Butefisch C, Hummelsheim H, Denzler P, Mauritz KH. Repetitive training of isolated movements improves the issue of motor rehabilitation of the centrally paretic paw. J Neurol Sci. 1995;130(ane):59–68. [PubMed] [Google Scholar]
22. Bayona NA, Bitensky J, Salter Grand, Teasell R. The function of chore-specific training in rehabilitation therapies. Top Stroke Rehabil. 2005;12(three):58–65. [PubMed] [Google Scholar]
23. Krakauer JW, Carmichael ST, Corbett D, Wittenberg GF. Getting neurorehabilitation right: what tin exist learned from animal models? Neurorehabil Neural Repair. 2012;26(viii):923–931. [PMC free article] [PubMed] [Google Scholar]
24. Johnson MJ, Feng Ten, Johnson LM, Winters JM. Potential of a suite of robot/computer-assisted motivating systems for personalized, home-based, stroke rehabilitation. J Neuroeng Rehabil. 2007;4:half-dozen. [PMC gratis article] [PubMed] [Google Scholar]
26. Iosa M, Morone Thou, Bragoni G, et al. Driving electromechanically assisted Gait Trainer for people with stroke. J Rehabil Res Dev. 2011;48(2):135–146. [PubMed] [Google Scholar]
27. French B, Thomas Fifty, Leathley 1000, et al. Does repetitive task training improve functional activity later stroke? A Cochrane systematic review and meta-analysis. J Rehabil Med. 2010;42(1):9–14. [PubMed] [Google Scholar]
28. Wevers L, van de Port I, Vermue K, Mead Yard, Kwakkel G. Furnishings of task-oriented circuit form training on walking competency after stroke: a systematic review. Stroke. 2009;40(7):2450–2459. [PubMed] [Google Scholar]
29. Van de Port IG, Wood-Dauphinee Southward, Lindeman E, Kwakkel Grand. Effects of practise training programs on walking competency after stroke: a systematic review. Am J Phys Med Rehabil. 2007;86(11):935–951. [PubMed] [Google Scholar]
thirty. Hesse Southward, Mehrholz J, Werner C. Robot-assisted upper and lower limb rehabilitation later stroke: walking and arm/hand part. Dtsch Arztebl Int. 2008;105(eighteen):330–336. [PMC free article] [PubMed] [Google Scholar]
31. Delussu AS, Morone G, Iosa M, Bragoni M, Traballesi Chiliad, Paolucci S. Physiological responses and energy cost of walking on the Gait Trainer with and without trunk weight back up in subacute stroke patients. J Neuroeng Rehabil. 2014;11:54. [PMC costless article] [PubMed] [Google Scholar]
32. Roth EJ. Center disease in patients with stroke: incidence, impact, and implications for rehabilitation. Office I: classification and prevalence. Arch Phys Med Rehabil. 1993;74(7):752–760. [PubMed] [Google Scholar]
33. Ryan Every bit, Dobrovolny Fifty, Silver K, Smith GV, Macko RF. Cardiovascular fettle afterward stroke: role of muscle mass and gait deficit severity. J Stroke Cerebrovasc Dis. 2000;9(4):185–191. [PubMed] [Google Scholar]
34. Lefeber N, Swinnen E, Kerckhofs E. The immediate furnishings of robot-assistance on free energy consumption and cardiorespiratory load during walking compared to walking without robot-assistance: a systematic review. Disabil Rehabil Help Technol. 2016;twenty:1–15. [PubMed] [Google Scholar]
35. Mehrholz J, Elsner B, Werner C, Kugler J, Pohl M. Electromechanical-assisted training for walking after stroke. Cochrane Database Syst Rev. 2013;7:CD006185. [PMC free article] [PubMed] [Google Scholar]
36. Morone One thousand, Masiero S, Werner C, Paolucci South. Advances in neuromotor stroke rehabilitation. Biomed Res Int. 2014;2014:236043. [PMC gratis article] [PubMed] [Google Scholar]
37. Kahn LE, Lum PS, Rymer WZ, Reinkensmeyer DJ. Robot-assisted movement preparation for the stroke-dumb arm: does it matter what the robot does? J Rehabil Res Dev. 2006;43(five):619–630. [PubMed] [Google Scholar]
38. Iosa M, Morone Grand, Cherubini A, Paolucci S. The three laws of neurorobotics: a review on what neurorehabilitation robots should do for patients and clinicians. J Med Biol Eng. 2016;36:1–11. Review. [PMC free article] [PubMed] [Google Scholar]
39. Sathian K, Buxbaum LJ, Cohen LG, et al. Neurological principles and rehabilitation of activity disorders: common clinical deficits. Neurorehabil Neural Repair. 2011;25(five suppl):21S–32S. [PMC free article] [PubMed] [Google Scholar]
40. Van Vliet PM, Wulf G. Extrinsic feedback for motor learning after stroke: what is the prove? Disabil Rehabil. 2006;28(13–14):831–840. [PubMed] [Google Scholar]
41. Bragoni Yard, Broccoli M, Iosa One thousand, et al. Influence of psychologic features on rehabilitation outcomes in patients with subacute stroke trained with robotic-aided walking therapy. Am J Phys Med Rehabil. 2013;92(10 suppl 2):e16–e25. [PubMed] [Google Scholar]
42. Mehrholz J, Pohl M. Electromechanical-assisted gait training afterward stroke: a systematic review comparing terminate-effector and exoskeleton devices. J Rehabil Med. 2012;44(iii):193–199. [PubMed] [Google Scholar]
43. Hornby TG, Campbell DD, Kahn JH, Demott T, Moore JL, Roth Hour. Enhanced gait-related improvements later therapist- versus robotic-assisted locomotor training in subjects with chronic stroke: a randomized controlled study. Stroke. 2008;39(vi):1786–1792. [PubMed] [Google Scholar]
44. Hidler J, Nichols D, Pelliccio Chiliad, et al. Multicenter randomized clinical trial evaluating the effectiveness of the Lokomat in subacute stroke. Neurorehabil Neural Repair. 2009;23(1):5–xiii. [PubMed] [Google Scholar]
45. Regnaux JP, Saremi 1000, Marehbian J, Bussel B, Dobkin BH. An accelerometry-based comparison of 2 robotic assistive devices for treadmill training of gait. Neurorehabil Neural Repair. 2008;22(4):348–354. [PubMed] [Google Scholar]
46. Morone G, Iosa M, Bragoni M, et al. Who may accept durable benefit from robotic gait training? A two-year follow-up randomized controlled trial in patients with subacute stroke. Stroke. 2012;43(four):1140–1142. [PubMed] [Google Scholar]
47. Morone G, Bragoni M, Iosa M, et al. Who may benefit from robotic-assisted gait training? A randomized clinical trial in patients with subacute stroke. Neurorehabil Neural Repair. 2011;25(7):636–644. [PubMed] [Google Scholar]
48. Duncan PW, Sullivan KJ, Behrman AL, et al. LEAPS Investigative Team. Torso-weight-supported treadmill rehabilitation later stroke. N Engl J Med. 2011;364(21):2026–2036. [PMC free commodity] [PubMed] [Google Scholar]
49. Dobkin BH, Duncan PW. Should body weight-supported treadmill training and robotic-assistive steppers for locomotor training trot dorsum to the starting gate? Neurorehabil Neural Repair. 2012;4:308–317. [PMC free article] [PubMed] [Google Scholar]
50. Hidler J, Lum PS. The road ahead for rehabilitation robotics. J Rehabil Res Dev. 2011;48(4):7–x. [PubMed] [Google Scholar]
51. Calabrò RS, De Cola MC, Leo A, et al. Robotic neurorehabilitation in patients with chronic stroke: psychological well-beingness beyond motor improvement. Int J Rehabil Res. 2015;38(3):219–225. [PubMed] [Google Scholar]
52. Grimm F, Walter A, Spüler Chiliad, Naros G, Rosenstiel Due west, Gharabaghi A. Hybrid neuroprosthesis for the upper limb: combining brain-controlled neuromuscular stimulation with a multi-articulation arm exoskeleton. Front Neurosci. 2016;10:367. [PMC free article] [PubMed] [Google Scholar]
53. Straudi Southward, Fregni F, Martinuzzi C, Pavarelli C, Salvioli S, Basaglia Northward. tDCS and robotics on upper limb stroke rehabilitation: upshot modification by stroke duration and type of stroke. Biomed Res Int. 2016;2016:5068127. [PMC free article] [PubMed] [Google Scholar]
54. Buetefisch C, Heger R, Schicks W, Seitz R, Netz J. Hebbian-blazon stimulation during robot-assisted grooming in patients with stroke. Neurorehabil Neural Repair. 2011;25(7):645–655. [PubMed] [Google Scholar]
55. Hesse S, Werner C, Schonhardt EM, Bardeleben A, Jenrich Westward, Kirker SG. Combined transcranial direct current stimulation and robot-assisted arm training in subacute stroke patients: a pilot written report. Restor Neurol Neurosci. 2007;25(1):9–15. [PubMed] [Google Scholar]
56. Edwards DJ, Krebs HI, Rykman A, et al. Raised corticomotor excitability of M1 forearm area following anodal tDCS is sustained during robotic wrist therapy in chronic stroke. Restor Neurol Neurosci. 2009;27(3):199–207. [PMC free article] [PubMed] [Google Scholar]
57. Ochi G, Saeki Due south, Oda T, Matsushima Y, Hachisuka K. Effects of anodal and cathodal transcranial direct current stimulation combined with robotic therapy on severely affected arms in chronic stroke patients. J Rehabil Med. 2013;45(2):137–140. [PubMed] [Google Scholar]
58. Plautz EJ, Milliken GW, Nudo RJ. Furnishings of repetitive motor training on movement representations in developed squirrel monkeys: role of employ versus learning. Neurobiol Learn Mem. 2000;74(one):27–55. [PubMed] [Google Scholar]
59. Basteris A, Sanguineti V. Toward 'optimal' schemes of robot assistance to facilitate motor skill learning. Conf Proc IEEE Eng Med Biol Soc. 2011;2011:2355–2358. [PubMed] [Google Scholar]
60. Morone G, Paolucci S, Mattia D, Pichiorri F, Tramontano M, Iosa M. The 3Ts of the new millennium neurorehabilitation gym: therapy, technology, translationality. Expert Rev Med Devices. 2016;13(nine):785–787. [PubMed] [Google Scholar]
61. Krishnan C, Ranganathan R, Dhaher YY, Rymer WZ. A pilot study on the feasibility of robot-aided leg motor grooming to facilitate active participation. PLoS 1. 2013;8(x):e77370. [PMC free article] [PubMed] [Google Scholar]
62. Perry J, Burnfield JM, Cabico LM. Gait Analysis: Normal and Pathological Function. Thorofare, NJ: SLACK; 2010. [Google Scholar]
63. Iosa M, Fusco A, Morone G, Paolucci S. Development and decline of upright gait stability. Front Crumbling Neurosci. 2014;six:14. [PMC free commodity] [PubMed] [Google Scholar]
64. Iosa Thousand, Zoccolillo L, Montesi M, Morelli D, Paolucci S, Fusco A. The brain's sense of walking: a report on the intertwine between locomotor imagery and internal locomotor models in healthy adults, typically developing children and children with cerebral palsy. Front Hum Neurosci. 2014;8:859. [PMC free commodity] [PubMed] [Google Scholar]
65. Iosa M, Morone Grand, Bini F, Fusco A, Paolucci Southward, Marinozzi F. The connexion between anthropometry and gait harmony unveiled through the lens of the golden ratio. Neurosci Lett. 2016;612:138–144. [PubMed] [Google Scholar]
66. Dzeladini F, van den Kieboom J, Ijspeert A. The contribution of a key pattern generator in a reflex-based neuromuscular model. Front Hum Neurosci. 2014;viii:371. [PMC gratis article] [PubMed] [Google Scholar]
Articles from Neuropsychiatric Disease and Handling are provided here courtesy of Dove Press
derricksoncamedid.blogspot.com
Source: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5440028/
0 Response to "State of the Art Robotic Foot Fully Controlled by the Brain"
إرسال تعليق