Posts Tagged Robotic

[ARTICLE] On neuromechanical approaches for the study of biological and robotic grasp and manipulation – Full Text

Abstract

Biological and robotic grasp and manipulation are undeniably similar at the level of mechanical task performance. However, their underlying fundamental biological vs. engineering mechanisms are, by definition, dramatically different and can even be antithetical. Even our approach to each is diametrically opposite: inductive science for the study of biological systems vs. engineering synthesis for the design and construction of robotic systems. The past 20 years have seen several conceptual advances in both fields and the quest to unify them. Chief among them is the reluctant recognition that their underlying fundamental mechanisms may actually share limited common ground, while exhibiting many fundamental differences. This recognition is particularly liberating because it allows us to resolve and move beyond multiple paradoxes and contradictions that arose from the initial reasonable assumption of a large common ground. Here, we begin by introducing the perspective of neuromechanics, which emphasizes that real-world behavior emerges from the intimate interactions among the physical structure of the system, the mechanical requirements of a task, the feasible neural control actions to produce it, and the ability of the neuromuscular system to adapt through interactions with the environment. This allows us to articulate a succinct overview of a few salient conceptual paradoxes and contradictions regarding under-determined vs. over-determined mechanics, under- vs. over-actuated control, prescribed vs. emergent function, learning vs. implementation vs. adaptation, prescriptive vs. descriptive synergies, and optimal vs. habitual performance. We conclude by presenting open questions and suggesting directions for future research. We hope this frank and open-minded assessment of the state-of-the-art will encourage and guide these communities to continue to interact and make progress in these important areas at the interface of neuromechanics, neuroscience, rehabilitation and robotics.

Introduction

Grasp and manipulation have captivated the imagination and interest of thinkers of all stripes over the millennia; and with enough reverence to even attribute the intellectual evolution of humans to the capabilities of the hand [123]. Simply put, manipulation function is one of the key elements of our identity as a species (for an overview, see [4]). This is a natural response to the fact that much of our physical and cognitive ability and well-being is intimately tied to the use of our hands. Importantly, we have shaped our tools and environment to match its capabilities (straightforward examples include lever handles, frets in string instruments, and touch-screens). This co-evolution between hand-and-world reinforces the notion that our hands are truly amazing and robust manipulators, as well as rich sensory, perceptual and even social information.

It then comes as no surprise that engineers and physicians have long sought to replicate and restore this functionality in machines—both as appendages to robots and prostheses attached to humans with missing upper limbs [5]. Robotic hands and prostheses have a long and illustrious history, with records of sophisticated articulated hands as early as Gottfried ‘Götz’ von Berlichingen’s iron hand in 1504 [6]. Other efforts [7891011] were often fueled by the injuries of war [12131415] and the Industrial Revolution [16]. The higher survival rate in soldiers who lose upper limbs [1718] and the continual emergence of artificial intelligence [1920] are but the latest impetus. Thus, the past 20 years have seen an explosion in designs, fueled by large scale governmental funding (e.g., DARPA’s Revolutionizing Prosthetics and HAPTIX projects, EU’s INPUT and SOFTPRO projects) and private efforts such as DeepMind. A new player in this space is the potentially revolutionary social network of high-quality amateur scientists as exemplified by the FABLAB movement [21]. They are enabled by ubiquitously accessible and inexpensive 3D printing and additive manufacturing tools [22], collaborative design databases (http://www.eng.yale.edu/grablab/openhand/ and others), and communities with formal journals (http://www.liebertpub.com/overview/3d-printing-and-additive-manufacturing/621/ and http://www.journals.elsevier.com/additive-manufacturing/). Grassroots communities have also emerged that can, for example, compare and contrast the functionality of prosthetic hands whose price differs by three orders of magnitude (http://www.3dprint.com/2438/50-prosthetic-3d-printed-hand).

For all the progress that we have seen, however, (i) robotic platforms remain best at pre-sorted, pick-and-place assembly tasks [23]; and (ii) many prosthetic users still prefer simple designs like the revered whole- or split-hook designs originally developed centuries ago [2425].

Why have robotic and prosthetic hands not come of age? This short review provides a current attempt to tackle this long-standing question in response to the current technological boom in robotic and prosthetic limbs. Similar booms occurred in response to upper limb injuries [25] after the Napoleonic [26], First [12] and Second World Wars [8], and—with the advent of powerful inexpensive computers—in response to industrial and space exploration needs in the 1960’s, 1970’s and 1980’s [272829303132]. We argue that a truly bio-inspired approach suffers, by definition, from both gaps in our understanding of the biology, and technical challenges in mimicking (what we understand of) biological sensors, motors and controllers. Although biomimicry is often not the ultimate goal in robotics in general, it is relevant for humanoids and prostheses. Thus, our approach is to clarify when and why a better understanding of the biology of grasp and manipulation would benefit robotic grasping and manipulation.

Similarly, why is our understanding of the nature, function and rehabilitation of biological arms and hands incomplete? Jacob Benignus Winsløw Jacques-Bénigne Winslow, (1669—1760) noted in his Exposition anatomique de la structure du corps humain (1732) that ‘The coordination of the muscles of the live hand will never be understood’ [33]. Interestingly, he is still mostly correct. As commented in detail before [4], there has been much work devoted to inferring the anatomical, physiological, neural and cognitive processes that produce the upper limb function we so dearly appreciate and passionately work to restore following trauma or pathology. We argue, as Galileo Galilei did, that mathematics and engineering have much to contribute to the understanding of biological systems. Without such a ‘mathematical language’ we run the risk, as Galileo put it, of ‘wandering in vain through a dark labyrinth’ [34]. Thus, this short review also attempts to point out important mathematical and engineering developments and advances that have helped our understanding of our hands.

This review first contrasts the fundamental differences between engineering and neuroscience approaches to biological robotic systems. Whereas the former applies engineering principles, the latter relies on scientific inference. We then discuss how the physics of the world provides a common ground between them because both types of systems have similar functional goals, and must abide by the same physical laws. We go on to evaluate how biological and robotic systems implement the necessary sensory and motor functions using the dramatically different anatomy, morphology and mechanisms available to each. This inevitably raises questions about differences in their sensorimotor control strategies. Whereas engineering system can be designed and manufactured to optimize well-defined functional features, biological systems evolve without such strict tautology. Biological systems likely evolve by implementing ecologically and temporally good-enough, sub-optimal or habitual control strategies in response to the current multi-dimensional functional constraints and goals in the presence of competition, variability, uncertainty, and noise. We conclude by exploring the notion that the functional versatility of biological systems that roboticists admire is, in fact, enabled by the very nonlinearities and complexities in anatomy, sensorimotor physiology, and neural function that engineering approaches often seek to avoid. […]

Continue —> On neuromechanical approaches for the study of biological and robotic grasp and manipulation | Journal of NeuroEngineering and Rehabilitation | Full Text

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[Abstract] A soft robotic supernumerary finger and a wearable cutaneous finger interface to compensate the missing grasping capabilities in chronic stroke patients

Abstract:

Stroke survivors who experience severe hemipare-sis often cannot completely recover the use of their hand and arm. Many of the rehabilitation devices currently available are designed to increase the functional recovery right after the stroke when, in some cases, biological restoring and plastic reorganization of the central nervous system can take place. However, this is not always the case. Even after extensive therapeutic interventions, the probability of regaining functional use of the impaired hand is low. In this respect, we present a novel robotic system composed of a supernumerary robotic finger and a wearable cutaneous finger interface. The supernumerary finger is used to help grasping objects while the wearable interface provides information about the forces exerted by the robotic finger on the object being held. We carried out two experiments, enrolling 16 healthy subjects and 2 chronic stroke patients. Results showed that using the supernumerary finger greatly improved the grasping capabilities of the subjects. Moreover, providing cutaneous feedback significantly improved the performance of the considered task and was preferred by all subjects.

Source: A soft robotic supernumerary finger and a wearable cutaneous finger interface to compensate the missing grasping capabilities in chronic stroke patients – IEEE Xplore Document

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[Conference paper] ERRSE: Elbow Robotic Rehabilitation System with an EMG-Based Force Control – Abstract+References

Abstract

Robotic devices for rehabilitation purposes have been increasingly studied in the past two decades and are becoming more and more diffused, due to their effective support to the traditional therapy. They allow to automate in a repeatable manner the rehabilitative exercises and to quantify outcomes, giving important feedback to the therapist. This paper deals with the design, development and preliminary characterization of a robotic system, with an exoskeleton device, for assisted upper-limb rehabilitation, in which surface EMG measurements are used to implement a force-based active and resistive control. A prototype of the system has been realized, measurements of important parameters of the motion permitted to optimize the design and preliminary tests on the control strategy were carried out. 

References

  1. 1.
    Lo AC et al (2010) Robot-assisted therapy for long-term upper-limb impairment after stroke. N Eng J Med 362:1772–1783CrossRefGoogle Scholar
  2. 2.
    Lum PS, Burgar CG, Shor PC, Majmundar M, Van der Loos M (2002) Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke. Arch Phys Med Rehabil 83(7):952–959CrossRefGoogle Scholar
  3. 3.
    Prange GB, Jannink MJA, Groothuis-Oudshoorn CGM, Hermens HJ, IJzerman MJ (2006) Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. J Rehabil Res Dev 43(2):171–184CrossRefGoogle Scholar
  4. 4.
    Huang VS, Krakauer JW (2009) Robotic neurorehabilitation: a computational motor learning perspective. J NeuroEng Rehabil 6Google Scholar
  5. 5.
    Krebs HI, Volpe BT, Williams D, Celestino J, Charles SK, Lynch D, Hogan N (2007) Robot-aided neurorehabilitation: a robot for wrist rehabilitation. Trans Neural Syst Rehabil Eng 15(3):327–335CrossRefGoogle Scholar
  6. 6.
    Colombo R, Pisano F, Micera S, Mazzone A, Delconte C, Carrozza MC, Dario P, Minuco G (2005) Robotic techniques for upper limb evaluation and rehabilitation of stroke patients. IEEE Trans Neural Syst Rehabil Eng 13(3):311–324CrossRefGoogle Scholar
  7. 7.
    Lo H, Quan Xie S (2012) Exoskeleton robots for upper-limb rehabilitation: state of the art and future prospects. Med Eng Phys 34(2012):261–268CrossRefGoogle Scholar
  8. 8.
    Li H, Zhao G, Zhou Y, Chen X, Ji Z, Wang L (2014) Relationship of EMG/SMG features and muscle strength level: an exploratory study on tibialis anterior muscles during plantar-flexion among hemiplegia patients. Biomed Eng Online 13(1):2–15CrossRefGoogle Scholar
  9. 9.
    Kiguchi K, Hayashi Y (2015) EMG-based control of a lower-lim power-assist robot. In: Intelligent assistive robots. Springer tracts in advanced robotics, vol 106, pp 371–383Google Scholar
  10. 10.
    Kawasaki H, Ito S, Ishigure Y, Nishimoto Y, Aoki T, Mouri T, Skaeda H, Abe M (2007) Development of a hand motion assist robot for rehabilitation therapy by patient self-motion control. In: Proceedings of the IEEE international conference on robotic rehabilitation (ICORR), June 2007, pp 234–240Google Scholar
  11. 11.
    Jeong EC (2013) Comparison of wrist motion classification methods using surface electromyogram. J Cent South Univ 20(4):960–968CrossRefGoogle Scholar
  12. 12.
    Celli A et al (2008) Treatment of elbow lesions: new aspects in diagnosis and surgical techniques. Springer, New York, p c2008CrossRefGoogle Scholar
  13. 13.
    Kyrylova A (2015) Development of a wearable mechatronic elbow brace for postoperative motion rehabilitation. Electronic thesis and dissertation repository. Paper 3019Google Scholar
  14. 14.
    Ferris DP, Lewis CL, (2009) Robotic lower limb exoskeletons using proportional myoelectric control. In: Proceedings of the 31st annual international conference of the IEEE EMBS, pp 2119–2124Google Scholar
  15. 15.
    DiCicco M, Lucas L, Matsuoka Y (2004) Comparison of control strategies for an EMG controlled orthotic exoskeleton for the hand. In: Proceedings of the 2004 IEEE international conference on robotics and automation, April 2004Google Scholar

Source: ERRSE: Elbow Robotic Rehabilitation System with an EMG-Based Force Control | SpringerLink

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[Conference paper] Hand Robotic Rehabilitation: From Hospital to Home – Abstract+References

Abstract

Stroke patients are often affected by hemiparesis. In the rehabilitation of these patients the function of the hand is often neglected. Thus in this work we propose a robotic approach to the rehabilitation of the hand of a stroke patient in hospital and also at home. Some experimental results can be presented here especially for inpatients. Further experimental results on home-patients must be acquired through a telemedicine platform, designed for this application. 

References

  1. 1.
    Song D, Lan N, Loeb GE, Gordon J (2008) Model-based sensorimotor integration for multi-joint control: development of a virtual arm model. Ann Biomed Eng 36(6):1033–1048. doi:10.1007/s10439-008-9461-8 CrossRefGoogle Scholar
  2. 2.
    Prange GB, Jannink MJA, Groothuis-Oudshoorn CGM, Hermens HJ, Ijzerman MJ (2006) Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke. J Rehabil Res Dev 43(2):171–183. doi:10.1682/Jrrd.2005.04.0076 CrossRefGoogle Scholar
  3. 3.
    Fulesdi B, Limburg M, Bereczki D, Kaplar M, Molnar C, Kappelmayer J, Neuwirth G, Csiba L (1999) Cerebrovascular reactivity and reserve capacity in type II diabetes mellitus. J Diabetes Complicat 13(4):191–199. doi:10.1016/S1056-8727(99)00044-6 CrossRefGoogle Scholar
  4. 4.
    Mukherjee M, Koutakis P, Siu KC, Fayad PB, Stergiou N (2013) Stroke survivors control the temporal structure of variability during reaching in dynamic environments. Ann Biomed Eng 41(2):366–376. doi:10.1007/s10439-012-0670-9 CrossRefGoogle Scholar
  5. 5.
    Nowak DA (2008) The impact of stroke on the performance of grasping: usefulness of kinetic and kinematic motion analysis. Neurosci Biobehav R 32(8):1439–1450. doi:10.1016/j.neubiorev.2008.05.021 CrossRefGoogle Scholar
  6. 6.
    Salter RB (2004) Continuous passive motion: from origination to research to clinical applications. J Rheumatol 31(11):2104–2105Google Scholar
  7. 7.
    Fu MJ, Knutson JS, Chae J (2015) Stroke rehabilitation using virtual environments. Phys Med Rehabil Clin N Am 26(4):747–757. doi:10.1016/j.pmr.2015.06.001 CrossRefGoogle Scholar
  8. 8.
    Jarrassé N, Proietti T, Crocher V, Robertson O, Sahbani A, Morel G, Roby-Brami A (2014) Robotic exoskeletons: a perspective for the rehabilitation of arm coordination in stroke patients. Front Hum Neurosc 8. doi:10.3389/fnhum.2014.00947
  9. 9.
    Pisotta I, Perruchoud D, Ionta S (2015) Hand-in-hand advances in biomedical engineering and sensorimotor restoration. J Neurosci Methods 246:22–29. doi:10.1016/j.jneumeth.2015.03.003 CrossRefGoogle Scholar
  10. 10.
    Saudabayev A, Varol HA (2015) Sensors for robotic hands: a survey of state of the art. IEEE Access 3:1765–1782. doi:10.1109/ACCESS.2015.2482543 CrossRefGoogle Scholar
  11. 11.
    Schott J, Rossor M (2003) The grasp and other primitive reflexes. J Neurol Neurosurg Psychiatry 74(5):558–560. doi:10.1136/jnnp.74.5.558 CrossRefGoogle Scholar
  12. 12.
    Cruz EG, Kamper DG (2010) Use of a novel robotic interface to study finger motor control. Ann Biomed Eng 38(2):259–268. doi:10.1007/s10439-009-9845-4 CrossRefGoogle Scholar
  13. 13.
    Legnani G, Casolo F, Righettini P, Zappa B (1996) A homogeneous matrix approach to 3D kinematics and dynamics – I. Theory. Mech Mach Theory 31(5):573–587. doi:10.1016/0094-114X(95)00100-D CrossRefGoogle Scholar
  14. 14.
    Borboni A, Mor M, Faglia R (2016) Gloreha-hand robotic rehabilitation: design, mechanical model, and experiments. J Dyn Syst Meas Control Trans ASME 138(11). doi:10.1115/1.4033831
  15. 15.
    Borboni A, Villafañe JH, Mullè C, Valdes K, Faglia R, Taveggia G, Negrini S (2017) Robot-assisted rehabilitation of hand paralysis after stroke reduces wrist edema and pain: a prospective clinical trial. J Manipulative Physiol Ther 40(1):21–30. doi:10.1016/j.jmpt.2016.10.003 CrossRefGoogle Scholar
  16. 16.
    Dobkin BH (2005) Rehabilitation after stroke. N Engl J Med 352(16):1677–1684. doi:10.1056/NEJMcp043511 CrossRefGoogle Scholar
  17. 17.
    Jorgensen HS, Nakayama H, Raaschou HO, Olsen TS (1999) Stroke: neurologic and functional recovery the Copenhagen Stroke Study. Phys Med Rehabil Clin N Am 10(4):887–906Google Scholar
  18. 18.
    Kuptniratsaikul V, Kovindha A, Suethanapornkul S, Massakulpan P, Permsirivanich W, Kuptniratsaikul PSA (2017) Motor recovery of stroke patients after rehabilitation: one-year follow-up study. Int J Neurosci 127(1):37–43. doi:10.3109/00207454.2016.1138474 CrossRefGoogle Scholar
  19. 19.
    Ferrucci L, Bandinelli S, Guralnik JM, Lamponi M, Bertini C, Falchini M, Baroni A (1993) Recovery of functional status after stroke a postrehabilitation follow-up study. Stroke 24(2):200–205CrossRefGoogle Scholar
  20. 20.
    Kelly-Hayes M, Wolf PA, Kase CS, Gresham GE, Kannel WB, D’Agostino RB (1989) Time course of functional recovery after stroke: the Framingham study. Neurorehabilitation Neural Repair 3(2):65–70. doi:10.1177/136140968900300202 CrossRefGoogle Scholar
  21. 21.
    Borghetti M, Sardini E, Serpelloni M (2013) Sensorized glove for measuring hand finger flexion for rehabilitation purposes. IEEE Trans Instrum Meas 62(12):3308–3314. doi:10.1109/TIM.2013.2272848 CrossRefGoogle Scholar

Source: Hand Robotic Rehabilitation: From Hospital to Home | SpringerLink

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[ARTICLE] A soft robotic exosuit improves walking in patients after stroke – Full Text

A softer recovery after stroke

Passive assistance devices such as canes and braces are often used by people after stroke, but mobility remains limited for some patients. Awad et al. studied the effects of active assistance (delivery of supportive force) during walking in nine patients in the chronic phase of stroke recovery. A soft robotic exosuit worn on the partially paralyzed lower limb reduced interlimb propulsion asymmetry, increased ankle dorsiflexion, and reduced the energy required to walk when powered on during treadmill and overground walking tests. The exosuit could be adjusted to deliver supportive force during the early or late phase of the gait cycle depending on the patient’s needs. Although long-term therapeutic studies are necessary, the immediate improvement in walking performance observed using the powered exosuit makes this a promising approach for neurorehabilitation.

Abstract

Stroke-induced hemiparetic gait is characteristically slow and metabolically expensive. Passive assistive devices such as ankle-foot orthoses are often prescribed to increase function and independence after stroke; however, walking remains highly impaired despite—and perhaps because of—their use. We sought to determine whether a soft wearable robot (exosuit) designed to supplement the paretic limb’s residual ability to generate both forward propulsion and ground clearance could facilitate more normal walking after stroke. Exosuits transmit mechanical power generated by actuators to a wearer through the interaction of garment-like, functional textile anchors and cable-based transmissions. We evaluated the immediate effects of an exosuit actively assisting the paretic limb of individuals in the chronic phase of stroke recovery during treadmill and overground walking. Using controlled, treadmill-based biomechanical investigation, we demonstrate that exosuits can function in synchrony with a wearer’s paretic limb to facilitate an immediate 5.33 ± 0.91° increase in the paretic ankle’s swing phase dorsiflexion and 11 ± 3% increase in the paretic limb’s generation of forward propulsion (P < 0.05). These improvements in paretic limb function contributed to a 20 ± 4% reduction in forward propulsion interlimb asymmetry and a 10 ± 3% reduction in the energy cost of walking, which is equivalent to a 32 ± 9% reduction in the metabolic burden associated with poststroke walking. Relatively low assistance (~12% of biological torques) delivered with a lightweight and nonrestrictive exosuit was sufficient to facilitate more normal walking in ambulatory individuals after stroke. Future work will focus on understanding how exosuit-induced improvements in walking performance may be leveraged to improve mobility after stroke.

INTRODUCTION

Bipedal locomotion is a defining trait of the human lineage, with a key evolutionary advantage being a low energetic cost of transport (1). However, the economy of bipedal gait may be lost because of neurological injury with disabling consequences. Hemiparetic walking (27) is characterized by a slow and highly inefficient gait that is a major contributor to disability after stroke (8, 9), which is a leading cause of disability among Americans (10). Despite rehabilitation, the vast majority of stroke survivors retain neuromotor deficits that prevent walking at speeds suitable for normal, economical, and safe community ambulation (11). Impaired motor coordination (12), muscle weakness and spasticity (13), and reduced ankle dorsiflexion (DF; drop foot) and knee flexion during walking are examples of typical deficits after stroke that limit walking speed and contribute to gait compensations such as hip circumduction and hiking (1418), increase the risk of falls, and reduce fitness reserve and endurance (3, 4, 9, 12, 1921). Even those able to achieve near-normal walking speeds present with gait deficits (22, 23) that hinder community reintegration and limit participation to well below what is observed in even the most sedentary older adults (24, 25), ultimately contributing to reduced health and quality of life (10, 26, 27).

Walking independence is an important short-term goal for survivors of a stroke; however, independence can be achieved via compensatory mechanisms. The persistence of neuromotor deficits after rehabilitation often necessitates the prescription of passive assistive devices such as canes, walkers, and orthoses to enable walking at home and in the community (2830). Unfortunately, commonly prescribed devices compensate for poststroke neuromotor impairments in a manner that prevents normal gait function. For example, ankle-foot orthoses (AFOs) inhibit normal push-off during walking (31) and reduce gait adaptability (32). The stigma associated with the use of these devices is also important to consider, especially for the growing population of young adult survivors of stroke (33, 34). The major personal and societal costs of stroke-induced walking difficulty and the limitations of the existing intervention paradigm motivate the development of rehabilitation interventions and technologies that enable the rapid attainment of more normal walking behavior.

Recent years have seen the development of powered exoskeletal devices designed to enable walking in individuals who are unable to walk (35, 36). Central to this remarkable engineering achievement is a rigid structure that can support its own weight and provide high amounts of assistance; however, these powerful machines may not always be necessary to restore more normal gait function in individuals who retain the ability to walk after neurological injury, such as the majority of those after stroke. To address this opportunity, our team developed a lightweight, soft wearable robot (exosuit) that interfaces to the paretic limb of persons after stroke via garment-like, functional textile anchors. Exosuits produce gait-restorative joint torques by transmitting mechanical power from waist-mounted body-worn (37) or off-board (38, 39) actuators to the wearer through the interaction of the textile anchors and a cable-based transmission.

Several factors, such as the compliance of the exosuit-human system (40), prevent exosuits from providing the assistance necessary to enable nonambulatory individuals to walk again (41); however, for ambulatory individuals, the lightweight and nonrestrictive nature of this technology has the potential to facilitate a more natural interaction with the wearer and minimize disruption of the natural dynamics of walking (42). Our first efforts developing exosuits led to the creation of systems that could comfortably deliver assistive forces to healthy users during walking (39, 40, 4347). Recently, we demonstrated that assistive forces delivered through the exosuit interface produce marked reductions in the energy cost of healthy walking (37, 48). Thus, although exosuits can only augment, not replace, a wearer’s existing gait functions, we posit that they have the potential to work synergistically with the residual abilities of individuals with impaired gait to improve walking function.

The primary objective of this foundational study was to evaluate the potential of using the exosuit technology to restore healthy walking behavior in individuals after stroke. Toward this end, we evaluated the effects on hemiparetic gait of actively assisting the paretic limb during treadmill walking using a tethered, unilateral (worn on only one side of the body) exosuit designed to supplement the wearer’s generation of paretic ankle plantarflexion (PF) during stance phase and DF during swing phase. We posited that this targeted assistance of the paretic ankle’s gait functions would facilitate more symmetrical propulsive force generation by the paretic and nonparetic limbs and reduce the energetic burden associated with poststroke walking, which previous work has shown can be more than 60% more costly (49). Previous work on wearable assistive robots for persons after stroke has suggested that the timing of PF force delivery during walking could be an important contributor to positive outcomes in this heterogeneous population (50). Hence, we also evaluated different onset timings of PF force delivery for each individual, hypothesizing that this timing would need to be individualized to optimize outcomes.

Designed to be unobtrusive to the wearer when not powered, the exosuit’s mass of ~0.9 kg is distributed along the length of the paretic limb similar to a pair of pants. Nonetheless, to understand the net effect of walking with an exosuit powered and assisting the paretic limb, it is necessary to evaluate whether there are effects because of simply wearing the exosuit passively (worn but unpowered). A secondary objective was thus to evaluate the effects of walking with the passive exosuit relative to walking with the exosuit not worn. Moreover, because one of the compelling aspects of soft wearable robots, such as exosuits, is their potential to provide gait assistance and, potentially, rehabilitation benefit during community-based walking activities, in addition to treadmill-based biomechanical investigation into the effects of a tethered exosuit, our final objective was to evaluate the effects of exosuit assistance delivered from a first-generation, body-worn (untethered) exosuit during overground walking. Ultimately, by investigating how individuals with poststroke hemiparesis respond to exosuit-generated active assistance of ankle PF and DF during treadmill and overground walking, this study serves to define the technology’s potential for improving mobility and enabling more effective neurorehabilitation after stroke. […]

Continue —> A soft robotic exosuit improves walking in patients after stroke | Science Translational Medicine

 

Fig. 1. Overview of a soft wearable robot (exosuit) designed to augment paretic limb function during hemiparetic walking. Exosuits (A) use garment-like functional textile anchors worn around the waist and calf (B) and Bowden cable-based mechanical power transmissions to generate assistive joint torques as a function of the paretic gait cycle (C). Integrated sensors (load cells and gyroscopes) are used to detect gait events and in a cable position–based force controller that modulates force delivery. The contractile elements of the exosuit are the Bowden cables located posterior and anterior to the ankle joint. Exosuit-generated PF and DF forces are designed to restore the paretic limb’s contribution to forward propulsion (GRF) and ground clearance (ankle DF angle during swing phase)—subtasks of walking that are impaired after stroke. Poststroke deficits in these variables are demonstrated through a comparison of paretic (black) and nonparetic (gray) limbs. Means across participants are presented (n = 7).

 

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[ARTICLE] An Evaluation of the Design and Usability of a Novel Robotic Bilateral Arm Rehabilitation Device for Patients with Stroke – Full Text

Introduction: Robot-assisted therapy for upper limb rehabilitation is an emerging research topic and its design process must integrate engineering, neurological pathophysiology, and clinical needs.

Purpose of the study: This study developed/evaluated the usefulness of a novel rehabilitation device, the MirrorPath, designed for the upper limb rehabilitation of patients with hemiplegic stroke.

Methods: The process follows Tseng’s methodology for innovative product design and development, namely two stages, device development and usability assessment. During the development process, the design was guided by patients’ rehabilitation needs as defined by patients and their therapists. The design applied synchronic movement of the bilateral upper limbs, an approach that is compatible with the bilateral movement therapy and proprioceptive neuromuscular facilitation theories. MirrorPath consists of a robotic device that guides upper limb movement linked to a control module containing software controlling the robotic movement.

Results: Five healthy subjects were recruited in the pretest, and 4 patients, 4 caregivers, and 4 therapists were recruited in the formal test for usability. All recruited subjects were allocated to the test group, completed the evaluation, and their data were all analyzed. The total system usability scale score obtained from the patients, caregivers, and therapists was 71.8 ± 11.9, indicating a high level of usability and product acceptance.

Discussion and conclusion: Following a standard development process, we could yield a design that meets clinical needs. This low-cost device provides a feasible platform for carrying out robot-assisted bilateral movement therapy of patients with hemiplegic stroke.

Clinical Trial Registration: identifier NCT02698605.

Introduction

The World Health Organization (WHO) has reported that stroke is the third leading cause of death in developed countries and involves approximately 15 million stoke events annually. One-third of stroke patients die and a further one-third of events results in permanent disability. Depending on the location of the brain insult, stroke can lead to a wide range of functional impairments (Mackay et al., 2004); these include language, cognition, sensation, and motor functions. Motor impairment impacts the patient’s ability to perform activities of daily living. For the majority of patients, recovery of motor function involving an upper limb is slower than that of lower limb (Feys et al., 1998). Indeed, most activities of daily living rely the functioning of the upper limb, thus emphasizing the need for effective upper limb rehabilitation.

With an attempt to enhance the effectiveness of upper limb rehabilitation among stroke patients, a series of rehabilitation techniques have been developed and refined in recent decades; these include task-oriented motor training, constraint-induced movement therapy, mirror therapy, and bilateral movement training. Each of these methods has a number of theoretical advocates and each has been shown to be effective clinically. For instance, bilateral movement therapy, which involves coordinated movement of the bilateral upper limbs, has been shown to enhance upper limb recovery and coordination between the hands. Stoykov et al. (2009) found that bilateral arm training is more effective than unilateral training when restoring proximal upper limb function because it seems to improve the functional linkages between the bilateral hemispheres.

Even after receiving a full course of conventional rehabilitation, 60% of stroke patients still have difficulties when using their affected upper limb (Kwakkel et al., 1999). As a result, it has become the upmost importance to develop novel rehabilitation strategies that are able to help patients reach a higher level of recovery. One such approach is robot-assisted rehabilitation, which incorporates robotic technologies into the rehabilitation processes. Several well-known robot-assisted movement therapies for the upper limb has been implemented clinically, including MIT-Manus (Krebs et al., 1998), Bi-Manu-Track (Hesse et al., 2003), BATRAC (Cauraugh et al., 2010), and MIME (Burgar et al., 2000), each of which follows different movement therapy theories. Regarding the body parts that are mainly involved in therapy, Bi-Manu-Track focuses on the bilateral forearms and wrists, while BATRAC and MIME focus on the shoulder and elbow of the affected limb. Regarding the movement dimension, BATRAC involves movement in one-dimension, while MIME allows three-dimensional movement. In fact, the higher the degrees of freedom adopted during the movement therapy, the more complex is the design of the robotic device. As a result, it has become important to come up with a feasible design that fulfills the patient’s rehabilitation needs while avoiding the high costs that can be associated with instrument acquirement and maintenance. Furthermore, the effectiveness of the system needs to be comparable to that provided by conventional therapies so that a motivation to pursue this therapeutic option can be established (Kwakkel et al., 2008; Lo et al., 2010).

As an approach to the development of mechanical rehabilitation devices for hemiplegic upper limbs, Timmermans et al. (2009) proposed that three design domains are required; these were the therapy techniques used, the motivation of the patient, and resulting performance rewards. An online survey of physical therapists, 233 in total, indicated that a preferred upper limb robotic device needs to accommodate different hand movements, to be able to be used while in a seated position, to be able to provide the user with feedback, to focus on the restoration of activities of daily living, to able to be used at home, to have adjustable resistance levels and to cost less than US$6,000 (Lu et al., 2011).

In terms of usability, the interaction between the user and the machine tends to be overlooked during the development stage. Although a variety of upper limb rehabilitation machines have been proposed, only a few have been commercialized. This low market acceptance can be attributed to the high cost of these devices, safety concerns, and poor usability (Lee et al., 2005). To this end, the aim of this study was to design a bilateral upper limb rehabilitation device called MirrorPath for the rehabilitation of stroke patients that follows the theories of bilateral movement therapy and proprioceptive neuromuscular facilitation (PNF). These two theories were initially developed by Knott and Kabat and have been shown to have a positive effect on the range of active and passive motions needed by stroke patients (Sharman et al., 2006). Our device will guide the patient’s upper limbs, each of which moves along a diagonal motion path on the horizontal plane. The position and velocity of motion of the bilateral limbs are perfectly mirrored across the midline on the table. Finally, usability testing was conducted on the completed prototype.

Continue —>  Frontiers | An Evaluation of the Design and Usability of a Novel Robotic Bilateral Arm Rehabilitation Device for Patients with Stroke | Frontiers in Neurorobotics

Figure 2. (A) A patient performed bilateral diagonal movements using the device; (B) due to weakness of right upper limb, the patient’s grip was assisted with an elastic bandage, and the patient’s elbow was support by a sling; (C) the application scenario.

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[Abstract+References] Toward wearable supernumerary robotic fingers to compensate missing grasping abilities in hemiparetic upper limb 

This paper presents the design, analysis, fabrication, experimental characterization, and evaluation of two prototypes of robotic extra fingers that can be used as grasp compensatory devices for a hemiparetic upper limb. The devices are the results of experimental sessions with chronic stroke patients and consultations with clinical experts. Both devices share a common principle of work, which consists in opposing the device to the paretic hand or wrist so to restrain the motion of an object. They can be used by chronic stroke patients to compensate for grasping in several activities of daily living (ADLs) with a particular focus on bimanual tasks. The robotic extra fingers are designed to be extremely portable and wearable. They can be wrapped as bracelets when not being used, to further reduce the encumbrance. Both devices are intrinsically compliant and driven by a single actuator through a tendon system. The motion of the robotic devices can be controlled using an electromyography-based interface embedded in a cap. The interface allows the user to control the device motion by contracting the frontalis muscle. The performance characteristics of the devices have been measured experimentally and the shape adaptability has been confirmed by grasping various objects with different shapes. We tested the devices through qualitative experiments based on ADLs involving five chronic stroke patients. The prototypes successfully enabled the patients to complete various bimanual tasks. Results show that the proposed robotic devices improve the autonomy of patients in ADLs and allow them to complete tasks that were previously impossible to perform.

Alley R, Williams TI, Albuquerque M, . (2011) Prosthetic sockets stabilized by alternating areas of tissue compression and release. Journal of Rehabilitation Research and Development 48(6): 679. Google Scholar CrossRef, Medline
ArbotiX© (2012) ArbotiX-M Robocontroller. Available at: http://www.trossenrobotics.com/p/arbotix-robot-controller.aspx. (accessed 20 May 2017)
Aszmann OC, Roche AD, Salminger S, . (2015) Bionic reconstruction to restore hand function after brachial plexus injury: A case series of three patients. Lancet 385(9983): 21832189. Google Scholar CrossRef, Medline
Balasubramanian S, Klein J, Burdet E (2010) Robot-assisted rehabilitation of hand function. Current Opinion in Neurology 23(6): 661670. Google Scholar CrossRef, Medline
Belter JT, Segil JL (2013) Mechanical design and performance specifications of anthropomorphic prosthetic hands: A review. Journal of Rehabilitation Research and Development 50(5): 599. Google Scholar CrossRef, Medline
Bicchi A, Bavaro M, Boccadamo G, . (2008) Physical human–robot interaction: Dependability, safety, and performance. In: 10th IEEE international workshop on advanced motion control, Trento, Italy, 26–28 March 2008, pp. 914. Piscataway, NJ: IEEE. Google Scholar CrossRef
Birglen L, Lalibertè T, Gosselin C (2008) Underactuated Robotic Hands. Berlin: Springer. Google Scholar CrossRef
Brott T, Adams H, Olinger CP, . (1989) Measurements of acute cerebral infarction: A clinical examination scale. Stroke 20(7): 864870. Google Scholar CrossRef, Medline
Çalli B, Walsman A, Singh A, . (2015) Benchmarking in manipulation research: The YCB object and model set and benchmarking protocols. arXiv abs/1502.03143. Google Scholar
Casson AJ, Logesparan L, Rodriguez-Villegas E (2010) An introduction to future truly wearable medical devices—from application to ASIC. In: 2010 annual international conference of the IEEE Engineering in Medicine and Biology Society, Buenos Aires, Argentina, 31 August–4 September 2010, pp. 34303431. Piscataway, NJ: IEEE. Google Scholar CrossRef
Catalano MG, Grioli G, Farnioli E, . (2014) Adaptive synergies for the design and control of the Pisa/IIT SoftHand. International Journal of Robotics Research 33(5): 768782. Google Scholar Link
Davis WE, Burton AW (1991) Ecological task analysis: Translating movement behavior theory into practice. Adapted Physical Activity Quarterly 8(2): 154177. Google Scholar CrossRef
Demers L, Weiss-Lambrou R, Ska B (2002) The Quebec User Evaluation of Satisfaction with Assistive Technology (QUEST 2.0): An overview and recent progress. Technology and Disability 14(3): 101105. Google Scholar
Dollar AM, Howe RD (2010) The highly adaptive SDM hand: Design and performance evaluation. International Journal of Robotics Research 29(5): 585597. Google Scholar Link
Dollar AM, Howe RD (2011) Joint coupling design of underactuated hands for unstructured environments. International Journal of Robotics Research 30(9): 11571169. Google Scholar Link
Eppner C, Brock O (2013) Grasping unknown objects by exploiting shape adaptability and environmental constraints. In: 2013 IEEE/RSJ international conference on intelligent robots and systems, Tokyo, Japan, 3–7 November 2013, pp. 40004006. Piscataway, NJ: IEEE. Google Scholar CrossRef
Falco J, Van Wyk K, Liu S, . (2015) Grasping the performance: Facilitating replicable performance measures via benchmarking and standardized methodologies. IEEE Robotics & Automation Magazine 22(4): 125136. Google Scholar CrossRef
Farina D, Merletti R (2000) Comparison of algorithms for estimation of EMG variables during voluntary isometric contractions. Journal of Electromyography and Kinesiology 10(5): 337349. Google Scholar CrossRef, Medline
Felzer T, Freisleben B (2002) HaWCoS: the hands-free wheelchair control system. In: Proceedings of the fifth international ACM conference on assistive technologies, Edinburgh, Scotland, 8–10 July 2002, pp. 127134. New York: ACM. Google Scholar CrossRef
Gafford J, Ding Y, Harris A, . (2014) Shape deposition manufacturing of a soft, atraumatic, deployable surgical grasper. Journal of Medical Devices 8(3): 030927. Google Scholar CrossRef
Gillen G (2015) Stroke Rehabilitation: A Function-Based Approach. St. Louis, MO: Elsevier Health Sciences. Google Scholar
Go AS, Mozaffarian D, Roger VL, . (2014) Heart disease and stroke statistics—2014 update: A report from the American Heart Association. Circulation 129(3): e28. Google Scholar CrossRef, Medline
Graf C (2008) The Lawton instrumental activities of daily living scale. American Journal of Nursing 108(4): 5262. Google Scholar CrossRef, Medline
Heo P, Gu GM, Lee SJ, . (2012) Current hand exoskeleton technologies for rehabilitation and assistive engineering. International Journal of Precision Engineering and Manufacturing 13(5): 807824. Google Scholar CrossRef
Holland DP, Park EJ, Polygerinos P, . (2014) The soft robotics toolkit: Shared resources for research and design. Soft Robotics 1(3): 224230. Google Scholar CrossRef
Hussain I, Meli L, Pacchierotti C, . (2015a) Vibrotactile haptic feedback for intuitive control of robotic extra fingers. In: Proceedings of the IEEE world haptics conference, Evanston, IL, 22–26 June 2015, 394399. Piscataway, NJ: IEEE. Google Scholar CrossRef
Hussain I, Salvietti G, Malvezzi M, . (2017a) On the role of stiffness design for fingertip trajectories of underactuated modular soft hands. In: Proceedings of the IEEE international conference on robotics and automation. Singapore, 29 May–3 June 2017. Piscataway, NJ: IEEE. Google Scholar CrossRef
Hussain I, Salvietti G, Meli L, . (2015b) Using the robotic sixth finger and vibrotactile feedback for grasp compensation in chronic stroke patients. In: Proceedings of the IEEE/RAS-EMBS international conference on rehabilitation robotics, Singapore, 11–14 August 2015, pp. 6772. Piscataway, NJ: IEEE. Google Scholar CrossRef
Hussain I, Salvietti G, Spagnoletti G, . (2016) The Soft-SixthFinger: A wearable EMG controlled robotic extra-finger for grasp compensation in chronic stroke patients. IEEE Robotics and Automation Letters 1(2): 10001006. Google Scholar CrossRef
Hussain I, Salvietti G, Spagnoletti G, . (2017b) A soft supernumerary robotic finger and mobile arm support for grasping compensation and hemiparetic upper limb rehabilitation. Robotics and Autonomous Systems 93: 112. Google Scholar CrossRef
Kiguchi K, Tanaka T, Fukuda T (2004) Neuro-fuzzy control of a robotic exoskeleton with EMG signals. IEEE Transactions on Fuzzy Systems 12(4): 481490. Google Scholar CrossRef
Konrad P (2005) The ABC of EMG: A Practical Introduction to Kinesiological Electromyography. Scottsdale, AZ: Noraxon, Inc. Google Scholar
Laschi C, Cianchetti M (2014) Soft robotics: New perspectives for robot bodyware and control. Frontiers in Bioengineering and Biotechnology 2: 3. Google Scholar CrossRef, Medline
Lum PS, Godfrey SB, Brokaw EB, . (2012) Robotic approaches for rehabilitation of hand function after stroke. American Journal of Physical Medicine & Rehabilitation 91(11): S242S254. Google Scholar CrossRef, Medline
Lund AM (2001) Measuring usability with the USE questionnaire. Usability Interface 8(2): 36. Google Scholar
Ma RR, Belter JT, Dollar AM (2015) Hybrid deposition manufacturing: Design strategies for multimaterial mechanisms via three-dimensional printing and material deposition. Journal of Mechanisms and Robotics 7(2): 021002. Google Scholar CrossRef
Meng Q, Lee MH (2006) Design issues for assistive robotics for the elderly. Advanced Engineering Informatics 20(2): 171186. Google Scholar CrossRef
Merletti R, Botter A, Troiano A, . (2009) Technology and instrumentation for detection and conditioning of the surface electromyographic signal: State of the art. Clinical Biomechanics 24(2): 122134. Google Scholar CrossRef, Medline
Merlo A, Campanini I (2010) Technical aspects of surface electromyography for clinicians. The Open Rehabilitation Journal 3(1): 98109. Google Scholar CrossRef
Michaelsen SM, Jacobs S, Roby-Brami A, . (2004) Compensation for distal impairments of grasping in adults with hemiparesis. Experimental Brain Research 157(2): 162173. Google Scholar CrossRef, Medline
Miguelez J, Miguelez M, Alley R (2004) Amputations about the shoulder: Prosthetic management. Atlas of Amputations and Limb Deficiencies—Surgical, Prosthetic, and Rehabilitation Principles. Rosemont, IL: American Academy of Orthopaedic Surgeons, 263273. Google Scholar
Nakayama H, Jorgensen HS, Raaschou HO, . (1994) Compensation in recovery of upper extremity function after stroke: The Copenhagen Stroke Study. Archives of Physical Medicine and Rehabilitation 75(8): 852857. Google Scholar CrossRef, Medline
Nowak DA (2008) The impact of stroke on the performance of grasping: Usefulness of kinetic and kinematic motion analysis. Neuroscience & Biobehavioral Reviews 32(8): 14391450. Google Scholar CrossRef, Medline
Odhner LU, Jentoft LP, Claffee MR, . (2014) A compliant, underactuated hand for robust manipulation. International Journal of Robotics Research 33(5): 736752. Google Scholar Link
Ort T, Wu F, Hensel NC, . (2015) Supernumerary robotic fingers as a therapeutic device for hemiparetic patients. In: ASME 2015 dynamic systems and control conference, Columbus, OH, 28–30 October 2015, p. V002T27A010. New York: American Society of Mechanical Engineers. Google Scholar CrossRef
Oskoei MA, Hu H (2007) Myoelectric control systems—a survey. Biomedical Signal Processing and Control 2(4): 275294. Google Scholar CrossRef
Pons J, Rocon E, Ceres R, . (2004) The MANUS-HAND dextrous robotics upper limb prosthesis: Mechanical and manipulation aspects. Autonomous Robots 16(2): 143163. Google Scholar CrossRef
Pons JL (2008) Wearable Robots: Biomechatronic Exoskeletons. Chichester: Wiley. Google Scholar CrossRef
Pons JL (2010) Rehabilitation exoskeletal robotics. IEEE Engineering in Medicine and Biology Magazine 29(3): 5763. Google Scholar CrossRef, Medline
Prattichizzo D, Malvezzi M, Hussain I, . (2014a) The Sixth-Finger: A modular extra-finger to enhance human hand capabilities. In: Proceedings of the IEEE international symposium in robot | human interactive communication. Edinburgh, UK, 25–29 August 2014. Piscataway, NJ: IEEE. Google Scholar CrossRef
Prattichizzo D, Salvietti G, Chinello F, . (2014b) An object-based mapping algorithm to control wearable robotic extra-fingers. In: Proceedings of the IEEE/ASME international conference on advanced intelligent mechatronics. Besanon, France, 8–11 July 2014. Piscataway, NJ: IEEE. Google Scholar CrossRef
Raghavan P, Krakauer JW, Gordon AM (2006) Impaired anticipatory control of fingertip forces in patients with a pure motor or sensorimotor lacunar syndrome. Brain 129(6): 14151425. Google Scholar CrossRef, Medline
Robotis© (2012) Dynamixel MX-28T robot actuator. Available at: http://www.trossenrobotics.com/dynamixel-mx-28-robot-actuator.aspx . (accessed 20 May 2017)
Salvietti G, Hussain I, Cioncoloni D, . (2016) Compensating hand function in chronic stroke patients through the robotic sixth finger. IEEE Transactions on Neural System and Rehabilitation Engineering 25(2): 142150. Google Scholar CrossRef, Medline
Saponas TS, Tan DS, Morris D, . (2008) Demonstrating the feasibility of using forearm electromyography for muscle–computer interfaces. In: Proceedings of the SIGCHI conference on human factors in computing systems, Florence, Italy, 5–10 April 2008, pp. 515524. New York: ACM. Google Scholar CrossRef
Schwarz RJ (1955) The anatomy and mechanics of the human hand. Artificial Limbs 2(2): 2235. Google Scholar Medline
Stanger CA, Anglin C, Harwin WS, . (1994) Devices for assisting manipulation: A summary of user task priorities. IEEE Transactions on Rehabilitation Engineering, 2(4): 256265. Google Scholar CrossRef
Timoshenko S, Gere J (1972) Mechanics of Materials. New York: Van Nostrand Reinhold Co. Google Scholar
Vanderborght B, Albu-Schäffer A, Bicchi A, . (2013) Variable impedance actuators: A review. Robotics and Autonomous Systems 61(12): 16011614. Google Scholar CrossRef
Van der Loos HM, Reinkensmeyer DJ (2008) Rehabilitation and health care robotics. In: Siciliano B, Khatib O (eds.) Springer Handbook of Robotics. Berlin: Springer, pp. 12231251. Google Scholar CrossRef
Webb J, Xiao ZG, Aschenbrenner KP, . (2012) Towards a portable assistive arm exoskeleton for stroke patient rehabilitation controlled through a brain computer interface. In: 4th IEEE RAS & EMBS international conference on biomedical robotics and biomechatronics, Rome, Italy, 24–27 June 2012 pp. 12991304. Piscataway, NJ: IEEE. Google Scholar CrossRef
Wu F, Asada H (2014) Bio-artificial synergies for grasp posture control of supernumerary robotic fingers. In: Robotics: science and systems, Berkeley, CA, 12–16 July 2014. Cambridge, MA: MIT Press Google Scholar CrossRef
Wu FY, Asada HH (2016) Implicit and intuitive grasp posture control for wearable robotic fingers: A data-driven method using partial least squares. IEEE Transactions on Robotics 32(1): 176186. Google Scholar CrossRef
Zecca M, Micera S, Carrozza M, . (2002) Control of multifunctional prosthetic hands by processing the electromyographic signal. Critical Reviews in Biomedical Engineering 30(4–6): 459485. Google Scholar CrossRef, Medline

Source: Toward wearable supernumerary robotic fingers to compensate missing grasping abilities in hemiparetic upper limbThe International Journal of Robotics Research – Irfan Hussain, Giovanni Spagnoletti, Gionata Salvietti, Domenico Prattichizzo, 2017

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[WEB SITE] Integration of FES Into G-EO System Gait Trainer Receives FDA Nod

Reha Technology USA Inc announces it now offers FDA-approved integrated Functional Electronic Stimulation (FES) for its G-EO System Evolution robotic gait trainer.

“The FES in conjunction with the G-EO System will allow clinicians to generate contractions in paralyzed or weakened muscles in lower extremities at the appropriate time in the walking cycle to maximize patient outcomes,” says Matthew Brooks, clinical director of Reha Technology USA Inc, in a media release.

The G-EO System robotic gait trainer provides passive and active, assistive and resistive training and the simulation of stairs walking up and down.

“We look forward to add this integrated FES feature to all of our current and future customers and we are confident that this extended offering will create added value for their therapy environment,” adds executive VP Paul Abrams, in the release.

[Source(s): Reha Technology USA Inc, PR Newswire]

Source: Integration of FES Into G-EO System Gait Trainer Receives FDA Nod – Rehab Managment

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[ARTICLE] Efficacy of Short-Term Robot-Assisted Rehabilitation in Patients With Hand Paralysis After Stroke – Full Text

Background: We evaluated the effectiveness of robot-assisted motion and activity in additional to physiotherapy (PT) and occupational therapy (OT) on stroke patients with hand paralysis. Methods: A randomized controlled trial was conducted. Thirty-two patients, 34.4% female (mean ± SD age: 68.9 ± 11.6 years), with hand paralysis after stroke participated. The experimental group received 30 minutes of passive mobilization of the hand through the robotic device Gloreha (Brescia, Italy), and the control group received an additional 30 minutes of PT and OT for 3 consecutive weeks (3 d/wk) in addition to traditional rehabilitation. Outcomes included the National Institutes of Health Stroke Scale (NIHSS), Modified Ashworth Scale (MAS), Barthel Index (BI), Motricity Index (MI), short version of the Disabilities of the Arm, Shoulder and Hand (QuickDASH), and the visual analog scale (VAS) measurements. All measures were collected at baseline and end of the intervention (3 weeks). Results: A significant effect of time interaction existed for NIHSS, BI, MI, and QuickDASH, after stroke immediately after the interventions (all, P < .001). The experimental group had a greater reduction in pain compared with the control group at the end of the intervention, a reduction of 11.3 mm compared with 3.7 mm, using the 100-mm VAS scale. Conclusions: In the treatment of pain and spasticity in hand paralysis after stroke, robot-assisted mobilization performed in conjunction with traditional PT and OT is as effective as traditional rehabilitation.

Stroke (or cerebrovascular accident) is a sudden ischemic or hemorrhagic episode which causes a disturbed generation and integration of neural commands from the sensorimotor31 areas of the cortex. As a consequence, the ability to selectively activate muscle tissues for performing movement is reduced.26 Sixty percent of those individuals who survive a stroke exhibit a sensorimotor deficit of one or both hands and may benefit from rehabilitation to maximize recovery of the upper extremity.23,25 Restoration of arm and hand motility is essential for the independent performance of daily activities.23,26 A prompt and effective rehabilitation approach is essential28 to obtain recovery of an impaired limb to prevent tendon shortening, spasticity, and pain.2

Recent technologies have facilitated the use of robots as tools to assist patients in the rehabilitation process, thus maximizing patient outcomes.4 Several groups have developed robotic tools for upper limb rehabilitation of the shoulder and elbow.27 These robotic tools assist the patient with carrying out exercise protocols and may help restore upper limb mobility.22,26 The complexity of wrist and finger articulations had delayed the development of dedicated rehabilitation robots until 2003 when the first tool based on continuous passive motion (CPM) was presented followed by several other solutions, with various levels of complexity and functionality.3

A recent review on the mechanisms for motor relearning reported factors such as attention and stimuli (reinforcement) are crucial during learning which indicates that motor relearning can take place with patients with neurological disorders even when only the sensorial passive stimulation is applied.30 In addition, another review reported the benefits of CPM for stretching and upper limb passive mobilization for patients with stroke but that CPM treatment requires further research.40

Among robotic devices, Gloreha (Figure 1),5,10 with its compliant mechanical transmission, may represent an easily applied innovative solution to rehabilitation, because the hand can perform grasp and release activities wearing the device by mean of a flexible and light orthosis. Our objective of this study was to determine the efficacy of robot-assisted motion in addition to traditional physiotherapy (PT) and occupational therapy (OT) compared with additional time spent in PT and OT on stroke patients with hand paralysis on function, motor strength, spasticity, and pain.

Figure 1. Wearable glove/orthosis.

Continue —> Efficacy of Short-Term Robot-Assisted Rehabilitation in Patients With Hand Paralysis After Stroke – Feb 16, 2017

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[Abstract] Robotic and Mechanotherapeutic Technology to Restore the Functions of the Upper Limbs: Prospects for Development (Review).

Abstract

We have analyzed the advantages and disadvantages of the robotic and mechanotherapeutic technologies used for rehabilitation of the upper limbs. Robotic and mechanotherapeutic devices started as simple controllers and upper limb weight support systems in kinesitherapy, but have subsequently shown their potential as systems for providing task oriented movement training, by efforts to maximize the correspondence between the features of anatomical and biomechanical arms. Integration of functional neuromuscular electrostimulation with robotic and mechanotherapeutic technology considerably widens the possibilities of using robots for rehabilitation and for providing mechanical assistance, while the appearance of portable and fixed exoskeletons is leading to completely new devices based on both rehabilitation and assistive technologies. Currently prototypes of robotic assistive and rehabilitation devices controlled by brain-computer interfaces are being developed.

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Source: EBSCOhost | 120466983 | Robotic and Mechanotherapeutic Technology to Restore the Functions of the Upper Limbs: Prospects for Development (Review).

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