Posts Tagged Robotic

[BOOK Chapter] Application of a Robotic Rehabilitation Training System for Recovery of Severe Plegie Hand Motor Function after a Stroke – Full Text PDF

Abstract

We have developed a rehabilitation training system (UR-System-PARKO: Useful
and Ultimate Rehabilitation System-PARKO) for patients after a stroke to promote
recovery of motor function of the severe plegic hand with hemiplegia. A clinical
test with six patients for the therapeutic effect of the UR-System-PARKO for severe
plegic hand was performed. For all patients, the active ranges of motion (total
active motion) of finger extension improved after training with the UR-SystemPARKO. Moreover, the modified Ashworth scale (MAS) scores of finger extension
increased. Thus, the training reduced the spastic paralysis. These results suggest the
effectiveness of training with the UR-System-PARKO for recovery of motor function as defined by finger extension in the severe plegic hand.

1. Introduction

Stroke is the leading cause of disability in Japan, with more than 1 million people
in Japan living with a disability as a result of stroke. Therefore, interventions that
address the sensorimotor impairments resulting from stroke are important. Motor
function may be restored more than 6 months after a stroke [1, 2], but these studies
included patients with only moderate poststroke hemiplegia, whereas most stroke
survivors have a severely plegic hand with difficulty extending the fingers [3]. This
suggests that a method is needed for treatment of these severely affected cases.
However, although a few studies on rehabilitation therapy for severe plegic hands
have been reported, no marked recovery of ability in extension of the fingers of
the plegic hands was achieved in any study [4, 5]. Proprioceptive neuromuscular
facilitation (PNF) is a therapeutic method that was reported to increase the muscle
strength of the plegic extremities in patients with stroke-induced hemiplegia [6].
However, since PNF is indicated for patients with a certain level of joint motion,
this method has not been used for severe plegic hands where the fingers cannot
extend. Thus, the first author developed a method to build up the extensor digitorum muscle strength using PNF [7, 8] for stroke patients with severe hemiplegia.

With this therapy, he has performed repeated facilitation training using his hands
on stroke patients with a severe plegic hand to help them recover their motor function, and a good treatment outcome was achieved [9, 10] (Figure 1).
Facilitation training uses extension of the elbow joint with resistance applied to
the tips of the fully extended hemiplegic fingers to increase the force of the extensor digitorum muscle. However, this approach is time-consuming for the therapist.
Therefore, development of a training system is required instead of repeated
facilitation training by a therapist. The objectives of this study were to develop
a training system to increase the output of the extensor digitorum muscle force
and to verify the effect of training with the developed system on a severe plegic
hand. The training system is called the UR-System-PARKO (a useful and ultimate
rehabilitation support system for PARKO). The UR-System-PARKO was developed
by remodeling the simplified training system, which developed previously for
resistance training of hemiplegic upper limbs [11]. A brace for securing the plegic
hand to the UR-System-PARKO was developed on the basis of repeated facilitation
training by a therapist.[…]

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[WEB SITE] Hong Kong researchers create robotic arm to help stroke patients

new robotic arm  A research team at Hong Kong Polytechnic University (PolyU) has developed a robotic arm to facilitate self-help and upper-limb mobile rehabilitation for stroke patients after discharge from hospital.

Referred to as a mobile exo-neuro-musculo-skeleton, the robotic arm enables intensive and effective self-help rehabilitation exercise.

The lightweight device is said to be the first of its kind to combine exo-skeleton, soft robot and exo-nerve stimulation technologies. It is intended to cater to the increasing need for outpatient rehabilitation service for stroke patients.

“Referred to as a mobile exo-neuro-musculo-skeleton, the robotic arm enables intensive and effective self-help rehabilitation exercise.”

PolyU Department of Biomedical Engineering researcher Hu Xiaoling said: “We are confident that with our mobile exo-neuro-musculo-skeleton, stroke patients can conduct rehabilitation training anytime and anywhere, turning the training into part of their daily activities.

“We hope such flexible self-help training can well supplement traditional outpatient rehabilitation services, helping stroke patients achieve a much better rehabilitation progress.”

Designed to be flexible and easy-to-use, the robotic arm is compact in size, has fast responses and requires a minimal power supply.

It comprises different components for the wrist/hand, elbow, and fingers that can be worn separately or together for various functional training needs. The device can also be connected to a mobile application, where users can manage their training.

The exo-skeleton and soft robot components of the device offer external mechanical forces guided by voluntary muscle signals in order to facilitate the desired joint movement for the patients.

PolyU improved the rehabilitation by adding its Neuro-muscular Electrical Stimulation (NMES) technology, which allows the robotic arm to contract user’s muscles when electromyography signals are detected.

When tested in a clinical trial involving ten stroke patients, the robotic arm is reported to have led to better muscle coordination, wrist and finger functions, and lower muscle spasticity following 20 two-hour training sessions.

The researchers plan to collaborate with hospitals and clinics for conducting additional trials.

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[WEB SITE] HOMEREHAB – Development of Robotic Technology for Post-Stroke Home Tele-Rehabilitation – The European Coordination Hub for Open Robotics Development

homerehab1

Rehabilitation can help hemiparetic patients to learn new ways of using and moving their weak arms and legs. With immediate therapy it is also possible that people who suffer from hemiparesis may eventually regain movement. However, reductions in healthcare reimbursement place constant demands on rehabilitation specialists to reduce the cost of care and improve productivity. Service providers have responded by shortening the length of patient hospitalisation.

The HOMEREHAB project will develop a new tele-rehabilitation robotic system for delivering therapy to stroke patients at home. It will research on the complex trade-off between robotic design requirements for in home systems and the performance required for optimal rehabilitation therapies, which current commercial systems designed for laboratories and hospitals do not take into account. Additionally, the new home scenario also demands for the smart monitoring of the patient’s physiological state, and the adaptation of the rehabilitation therapy for an optimal service.

 

Contact:

Universidad Miguel Hernández de Elche (UMH)
Nicolas M. Garcia-Aracil
Email: Nicolas.garcia@umh.es
Internet: www.umh.es

 

CEIT – Centro de Estudios e Investigaciones Técnicas
Iñaki Díaz
Email: idiaz@ceit.es
Internet: www.ceit.es

 

Instead Technologies
Alejandro García Moll
Email: Alejandro.garciam@gouhm.umh.es
Internet: www.gouhm.uhm.es

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[Abstract + References] Evaluation of an Upper-Limb Rehabilitation Robotic Device for Home Use from Patient Perspective

Abstract

This paper presents a user study to evaluate the system’s performance by measuring objective indicators and subjective perception between the two versions of a planar rehabilitation robotic device: (i) PupArm system, called RoboTherapist 2D system for commercial purpose, designed and developed for clinical settings; and (ii) Homerehab system, developed for home use. Homerehab system is a home rehabilitation robotic platform developed inside the EU HOMEREHAB-Echord++ project framework. Nine patients with different neurological disorders participate in the study. Based on the analysis of subjective assessments of usability and the data acquired objectively by the robotic devices, we can conclude that the performance and user experience with both systems are very similar. This finding will be the base of more extensively studies to demonstrate that home-therapy with HomeRehab could be as efficient as therapy in clinical settings assisted by PupArm robot.

This work has been supported by the European Commission through the project HOMEREHAB: “Development of Robotic Technology for Post-Stroke Home Tele-Rehabilitation – Echord++” (Grant agreement: 601116); by the AURORA project (DPI2015-70415-C2-2-R), which is funded by the Spanish Ministry of Economy and Competitiveness and by the European Union through the European Regional Development Fund (ERDF), “A way to build Europe” and by Conselleria d’Educació, Cultura i Esport of Generalitat Valenciana through the grant APOTIP/2017/001.

References

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Go, A.S., Mozaffarian, D., Roger, V.L.: Heart disease and stroke statistics–2014 update: a report from the American Heart Association. Circulation 129, e28–e292 (2014)
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Langhorne, P., Coupar, F., Pollock, A.: Motor recovery after stroke: a systematic review. Lancet Neurol. 8(8), 741–754 (2009)
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Richards, L., Hanson, C., Wellborn, M., Sethi, A.: Driving motor recovery after stroke. Top. Stroke Rehabil. 15(5), 397–411 (2008)
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Linder, S.M., Rosenfeldt, A.B., Reiss, A., Buchanan, S., Sahu, K., Bay, C.R., Wolf, S.L., Alberts, J.L.: The home stroke rehabilitation and monitoring system trial: a randomized con-trolled trial. Int. J. Stroke 8(1), 1747–4949 (2013)
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Diaz, I., Catalan, J.M., Badesa, F.J., Justo, X., Lledo, L.D., Ugartemendia, A., Gil, J.J., Díez, J., Garca-Aracil, N.: Development of a robotic device for post-stroke home tele-rehabilitation. Adv. Mech. Eng
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Badesa, F.J., Llinares, A., Morales, R., Garcia-Aracil, N., Sabater, J.M., Perez-Vidal, C.: Pneumatic planar rehabilitation robot for post-stroke patients. Biomed. Eng. Appl. Basis Commun. 26(2), 1450025 (2014)
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Brooke, J.: SUS: a quick and dirty usability scale. In: Jordan, P.W., Thomas, B., Weerdmeester, B.A., McClealland, I.L. (eds.) Usability Evaluation in Industry, pp. 189–194. Taylor and Francis, London (1996)
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LLinares, A., Badesa, F.J., Morales, R., Garcia-Aracil, N., Sabater, J., Fernandez, E.: Robotic assessment of the influence of age on upper-limb sensorimotor function. Clin. Interv. Aging 8, 879 (2013).  https://doi.org/10.2147/CIA.S45900
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via Evaluation of an Upper-Limb Rehabilitation Robotic Device for Home Use from Patient Perspective | SpringerLink

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[Abstract] Robotic and Sensor Technology for Upper Limb Rehabilitation

Abstract

Robotic and sensor-based neurologic rehabilitation for the upper limb is an established concept for motor learning and is recommended in many national guidelines. The complexity of the human hands and arms and the different activities of daily living are leading to an approach in which robotic and sensor-based devices are used in combination to fulfill the multiple requirements of this intervention.

A multidisciplinary team of the Fondazione Don Carlo Gnocchi (FDG), an Italian nonprofit foundation, which spans across the entire Italian territory with 28 rehabilitation centers, developed a strategy for the implementation of robotic rehabilitation within the FDG centers. Using an ad hoc form developed by the team, 4 robotic and sensor-based devices were identified among the robotic therapy devices commercially available to treat the upper limb in a more comprehensive way (from the shoulder to the hand). Encouraging results from a pilot study, which compared this robotic approach with a conventional treatment, led to the deployment of the same set of robotic devices in 8 other FDG centers to start a multicenter randomized controlled trial. Efficiency and economic factors are just as important as clinical outcome.

The comparison showed that robotic group therapy costs less than half per session in Germany than standard individual arm therapy with equivalent outcomes. To ensure access to high-quality therapy to the largest possible patient group and lower health care costs, robot-assisted group training is a likely option.

 

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[Abstract + References] Robotic and Sensor Technology for Upper Limb Rehabilitation – PM&R

Abstract

Robotic and sensor-based neurologic rehabilitation for the upper limb is an established concept for motor learning and is recommended in many national guidelines. The complexity of the human hands and arms and the different activities of daily living are leading to an approach in which robotic and sensor-based devices are used in combination to fulfill the multiple requirements of this intervention. A multidisciplinary team of the Fondazione Don Carlo Gnocchi (FDG), an Italian nonprofit foundation, which spans across the entire Italian territory with 28 rehabilitation centers, developed a strategy for the implementation of robotic rehabilitation within the FDG centers. Using an ad hoc form developed by the team, 4 robotic and sensor-based devices were identified among the robotic therapy devices commercially available to treat the upper limb in a more comprehensive way (from the shoulder to the hand). Encouraging results from a pilot study, which compared this robotic approach with a conventional treatment, led to the deployment of the same set of robotic devices in 8 other FDG centers to start a multicenter randomized controlled trial. Efficiency and economic factors are just as important as clinical outcome. The comparison showed that robotic group therapy costs less than half per session in Germany than standard individual arm therapy with equivalent outcomes. To ensure access to high-quality therapy to the largest possible patient group and lower health care costs, robot-assisted group training is a likely option.

 

References

  1. Lo, A.C., Guarino, P.D., Richards, L.G. et al, Robot-assisted therapy for long-term upper-limb impairment after stroke. N Engl J Med2011;365:1749.
  2. Hesse, S., Heß, A., Werner, C.C., Kabbert, N., Buschfort, R. Effect on arm function and cost of robot assisted group therapy in subacute patients with stroke and a moderately to severely affected arm: A randomized controlled trial. Clin Rehabil2014;28:637–647.
  3. Smith BM, Albus JS, Barbera AJ. A Glossary of Terms for Robotics. Prepared for U.S. Air Force Materials Laboratory Integrated Computer Aided Manufacturing Program. U.S. Department of Commerce. National Bureau of Standards. 1981. Available at: https://www.gpo.gov/fdsys/pkg/GOVPUB-C13-7a9025561f229e1f7fb504ace852d602/pdf/GOVPUB-C13-7a9025561f229e1f7fb504ace852d602.pdf. Accessed September 5, 2018..

  4. Feigin, V.L., Forouzanfar, M.H., Krishnamurthi, R. et al, Global and regional burden of stroke during 1990-2010: Findings from the Global Burden of Disease Study 2010. Lancet2014;383:245–255.
  5. Mehrholz, J., Pohl, M., Platz, T., Kugler, J., Elsner, B. Electromechanical and robot-assisted arm training for improving activities of daily living, arm function, and arm muscle strength after stroke.Cochrane Database Syst Rev2015;11:CD006876.
  6. Kolominsky-Rabas, P.L., Heuschmann, P.U., Marschall, D. et al, Lifetime cost of ischemic stroke in Germany: Results and national projections from a population-based stroke registry. The Erlangen Stroke Project. Stroke2006;37:1179–1183.
  7. Ringelstein, E.B., Nabavi, D.G. Der ischämische Schlaganfall: Eine praxisorientierte Darstellung von Pathophysiologie, Diagnostik und Therapie. 1st ed. KohlhammerGermany2007.
  8. Deutscher Verband für Physiotherapie. Aktuelle Arbeitsmarktdaten veröffentlicht—Fachkräftemangel in der Physiotherapie mehr als deutlich. Available at: https://www.physio-deutschland.de/fachkreise/news-bundesweit/einzelansicht/artikel/Aktuelle-Arbeitsmarktdaten-veroeffentlicht-Fachkraeftemangel-in-der-Physiotherapie-mehr-als-deutlich.html. Published March 2017. Accessed April 5, 2018..

  9. Wright, D.L., Shea, C.H. Cognition and motor skill acquisition: Contextual dependencies. in: C.R. Reynolds (Ed.) Cognitive assessment: A multidisciplinary perspectiveSpringer Verlag USBoston, MA1994:89–106.
  10. Masiero, S., Armani, M., Rosati, G. Upper-limb robot-assisted therapy in rehabilitation of acute stroke patients: Focused review and results of new randomized controlled trial. J Rehabil Res Dev2011;48:355–366.
  11. Mehrholz, J., Hädrich, A., Platz, T., Kugler, J., Pohl, M. Electromechanical and robot-assisted arm training for improving generic activities of daily living, arm function, and arm muscle strength after stroke. Cochrane Database Syst Rev2012;6:CD006876.
  12. Norouzi-Gheidari, N., Archambault, P.S., Fung, J. Effects of robot-assisted therapy on stroke rehabilitation in upper limbs: Systematic review and meta-analysis of the literature. J Rehabil Res Dev2012;49:479–496.
  13. Masiero, S., Carraro, E., Ferraro, C., Gallina, P., Rossi, A., Rosati, G. Upper limb rehabilitation robotics after stroke: A perspective from the University of Padua, Italy. J Rehabil Med2009;41:981–985.
  14. Wagner, T.H., Lo, A.C., Peduzzi, P. et al, An economic analysis of robot-assisted therapy for long-term upper-limb impairment after stroke. Stroke2011;42:2630–2632.
  15. Masiero, S., Poli, P., Armani, M., Ferlini, G., Rizzello, R., Rosati, G. Robotic upper limb rehabilitation after acute stroke by NeReBot: Evaluation of treatment costs. Biomed Res Int2014;2014:265634.
  16. Aprile I. Multi-segmental robotic and technological upper limb rehabilitation in stroke. Fondazione Don Carlo Gnocchi Onlus. Clinical trial registration number NCT02879279. Available at: https://clinicaltrials.gov..

  17. De Wit, L., Putman, K., Dejaeger, E. et al, Use of time by stroke patients: A comparison of four European rehabilitation centers. Stroke2005;36:1977–1983.
  18. Lee, K.B., Lim, S.H., Kim, K.H. et al, Six-month functional recovery of stroke patients: A multi-time-point study. Int J Rehabil Res2015;38:173–180.
  19. Veerbeek, J.M., van Wegen, E., van Peppen, R. et al, What is the evidence for physical therapy poststroke? A systematic review and meta-analysis. PLoS One2014;9 (e87987).

 

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[Abstract] EEG predicts upper limb motor improvement after robotic rehabilitation in chronic stroke patients

Introduction/Background

Robotic rehabilitation is known to be at least as effective as conventional training for upper limb motor recovery after stroke; nevertheless, which patients could benefit from this treatment is unknown and finding markers that could predict rehabilitation outcome is a challenge.

We aimed at understanding the neural mechanisms of motor function recovery after upper limb robotic rehabilitation in chronic stroke patients using neurophysiological markers obtained by electroencephalography recording (EEG).

Material and method

Fourteen chronic stroke patients (M/F: 11/3; 59.5 ± 13 yrs) with mild to moderate upper limb paresis were subjected to 10 sessions of upper limb rehabilitation with a planar mobile robotic device (MOTORE, Humanware). Fugl–Meyer Assessment Scale (FMAS) and Wolf Motor Function Test (WMFT) were administered before (t0), at the end (t1) and at 1 month follow-up (t2); at the same timing 64-channals EEG was recorded.

We analyzed power spectrum density in different frequency bands of the affected and unaffected hemispheres with 64-ch EEG and their correlation with motor impairment as measured by clinical scales. Correlation analyses were performed to identify the indicators of good rehabilitative outcome.

Results

Clinical assessment indicated a significant functional improvement in upper limb motor function at the end of rehabilitation as assessed with FMAS and WMFT score that is maintained at follow-up. We found a positive correlation between global Alpha activity at t0 and WMFT score variation (t0–t1) and between global Beta activity at t0 and WMFT time variation (t0–t1) and a positive correlation between Beta activity at t0 in the unaffected hemisphere and FMAS variation (t0–t1 and t0–t2).

Conclusion

Robotic rehabilitation improves upper limb motor performance in stroke patients even in the chronic phase. The amount of Alpha and Beta band power at t0 is suggestive of rehabilitation-related motor outcome. Our results suggest that EEG recording preliminarily to robotic rehabilitation could help identifying good responders to treatment thus optimizing results.

 

via EEG predicts upper limb motor improvement after robotic rehabilitation in chronic stroke patients – ScienceDirect

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[WEB SITE] Project3 – Flexo-glove

image

Project Description

Flexo-glove is a 3D printed soft exoskeleton robotic glove with compact and streamlined design for assistance in activities of daily livings and rehabilitation purposes of patients with hand function impairment.

Specifications:

  • Overall weight of 330g including battery
  • Providing 22N pinch force, 48N power grasp force and object grasp size of up to 81mm in diameter
  • Two control modes: intention-sensing via wireless surface EMG for assistive mode and externally-directed via an accompanying smartphone

Project Details: —> Visit site

My Role:

  • Initiated the project with the idea of using soft 3D printed materials in design of the Flexo-glove inspired by X-Limb
  • Performed feasibility study for using cable-driven mechanism in actuation of rehabilitation glove
  • Leading a group of four mechatronics engineering students to fabricate the prototype and characterise the grip forces

Awards

  • Received Dyason fellowship, $5000 travel fellowship awarded by Melbourne Robotic Lab. to visit Harvard BioRobotics Lab

Related Publications

 A. Mohammadi, J. Lavranos, R. D. Howe, P. Choong and D. Oetomo

  Flexo-glove: A 3D Printed Soft Exoskeleton Robotic Glove for Impaired Hand Rehabilitation and Assistance

  40th International Engineering in Medicine and Biology Conference (EMBC), 2018.

Full Text  PDF 

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[WEB SITE] Soft Robotic Glove

Soft Robotic Glove

A lightweight robotic glove to assist people suffering from loss of hand motor control to restore their ability to grasp objects independently

The majority of patients with partial or total loss of hand motor abilities, including those suffering from debilitating disorders like muscular dystrophy, amyotrophic lateral sclerosis (ALS), and incomplete spinal cord injury, experience greatly reduced quality of life due to their inability to perform many daily activities. Tasks often taken for granted by the able-bodied become frustrating and nearly impossible feats due to reduced gripping strength and motor control of the hand.

 

The soft robotic glove under development at the Wyss Institute could one day be an assistive device used for grasping objects, which could help patients suffering from muscular dystrophy, amyotrophic lateral sclerosis (ALS), incomplete spinal cord injury, or other hand impairments to regain some daily independence and control of their environment. Credit: Wyss Institute at Harvard University

Visit site for more —>  Soft Robotic Glove

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[ARTICLE] Quantification of upper limb position sense using an exoskeleton and a virtual reality display – Full Text

Abstract

Background

Proprioceptive sense plays a significant role in the generation and correction of skilled movements and, consequently, in most activities of daily living. We developed a new proprioception assessment protocol that enables the quantification of elbow position sense without using the opposite arm, involving active movement of the evaluated limb or relying on working memory. The aims of this descriptive study were to validate this assessment protocol by quantifying the elbow position sense of healthy adults, before using it in individuals who sustained a stroke, and to investigate its test-retest reliability.

Methods

Elbow joint position sense was quantified using a robotic device and a virtual reality system. Two assessments were performed, by the same evaluator, with a one-week interval. While the participant’s arms and hands were occluded from vision, the exoskeleton passively moved the dominant arm from an initial to a target position. Then, a virtual arm representation was projected on a screen placed over the participant’s arm. This virtual representation and the real arm were not perfectly superimposed, however. Participants had to indicate verbally the relative position of their arm (more flexed or more extended; two-alternative forced choice paradigm) compared to the virtual representation. Each participant completed a total of 136 trials, distributed in three phases. The angular differences between the participant’s arm and the virtual representation ranged from 1° to 27° and changed pseudo-randomly across trials. No feedback about results was provided to the participants during the task. A discrimination threshold was statistically extracted from a sigmoid curve fit representing the relationship between the angular difference and the percentage of successful trials. Test-retest reliability was evaluated with 3 different complementary approaches, i.e. a Bland-Altman analysis, an intraclass correlation coefficient (ICC) and a standard error of measurement (SEm).

Results

Thirty participants (24.6 years old; 17 males, 25 right-handed) completed both assessments. The mean discrimination thresholds were 7.0 ± 2.4 (mean ± standard deviation) and 5.9 ± 2.1 degrees for the first and the second assessment session, respectively. This small difference between assessments was significant (− 1.1 ± 2.2 degrees), however. The assessment protocol was characterized by a fair to good test-retest reliability (ICC = 0.47).

Conclusion

This study demonstrated the potential of this assessment protocol to objectively quantify elbow position sense in healthy individuals. Futures studies will validate this protocol in older adults and in individuals who sustained a stroke.

 

Background

Proprioception is defined as the ability to perceive body segment positions and movements in space [1]. Sensory receptors involved in proprioception are mostly located in muscle [234], joint [56] and skin [37]. Proprioceptive sense is known to play a significant role in motor control [891011] and learning [812], particularly in the absence of vision. The importance of proprioceptive inputs has been demonstrated while studying individuals who presented lack of proprioception due to large-fiber sensory neuropathy [1112]. Despite an intact motor system, somatosensory deafferentation may lead to limitations in several activities involving motor skills, such as eating or dressing [12]. These disabilities may also be observed in individuals with proprioceptive impairments due to a stroke. Indeed, approximately half of the individuals who sustained a stroke present proprioceptive impairments in contralesional upper limb [13]. After a stroke, proprioception is known to be related to recovery of functional mobility and independence in activities of daily living (ADL; [14]). Fewer individuals with significant proprioceptive and motor losses (25%) were independent in ADL than individuals with motor deficits alone (78%). Moreover, fewer individuals with proprioceptive deficits (60%) after a stroke are discharged from the hospital directly to home compared to those without proprioceptive deficits (92%) [15].

Although the negative impact of proprioceptive impairments on motor and functional recovery is known, a large proportion of clinicians (70%) report not using standardised assessment to evaluate somatosensory deficits in patients with a stroke [16]. In clinical and research settings, proprioception is most frequently assessed with limb-matching tasks. Two types of matching tasks have commonly been used: the ipsilateral remembered matching task and the contralateral concurrent matching task [17]. In an ipsilateral remembered matching task, the evaluator or robotic device brings the patient’s limb to a target joint position, when the patient’s eyes are closed, keeps the limb in this position for several seconds, and then moves back the limb to the initial position. The patient needs to memorize the reference position and replicate it with the same (ipsilateral) limb. This task cannot, however, be used to evaluate proprioception in individuals with working memory issues, which represent around 25% of individuals who sustained a stroke [18]. In such cases, the matching error observed could reflect memory deficits, rather than proprioceptive impairments. Moreover, upper limb paresis affects 76% of individuals who sustained a stroke [19], making the task’s execution difficult or impossible. Assessing proprioception with the less affected arm as the indicator arm is therefore frequently considered in patients with hemiparesis. Indeed, in a contralateral concurrent matching task, the patient has to reproduce a mirror image of the evaluated limb position with the opposite (contralateral) limb [17]. However, considering that 20% of individuals who sustained a stroke also presents proprioceptive impairment on the ipsilateral side of the lesion [13], it would be difficult to ascertain whether the error is due to deficits in the evaluated arm, the opposite arm or both. In addition, interhemispheric communication is required in a contralateral concurrent matching task. Individuals with asymmetric stroke or with transcallosal degeneration would therefore be particularly disadvantaged while being assessed with a contralateral concurrent matching task [17].

In order to study proprioception in individuals who sustained a stroke, we developed an assessment protocol, that combines the use of an exoskeleton and a virtual reality system, enabling the quantification of position sense without using the opposite arm, involving active movement of the evaluated limb or relying on working memory. The primary objective of the present study was to validate the assessment protocol by quantifying the elbow joint position sense of healthy adults, before using this protocol with individuals who sustained a stroke. As a secondary objective, test-retest reliability of the assessment protocol was investigated.[…]

 

Continue —> Quantification of upper limb position sense using an exoskeleton and a virtual reality display | Journal of NeuroEngineering and Rehabilitation | Full Text

 

Fig. 1KINARM Exoskeleton Lab. a Modified wheelchair with each arm supported against gravity by exoskeletons; (b) Virtual reality display; (c) Virtual arm and real arm positions (blue line; non-visible for the participant) where ∆Θ represents the angular difference between the real and the virtual arm. The white circle corresponds to the center of rotation, i.e. the elbow joint

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