Posts Tagged Arm

[Abstract + References] Preliminary Design of Soft Exo-Suit for Arm Rehabilitation – Conference paper

Abstract

Every year, millions of people experience a stroke but only a few of them fully recover. Recovery requires a working staff, which is time consuming and inefficient. Therefore, over the past few years rehabilitation robots like Exoskeletons have been used in the recuperation process for patients. In this paper we have designed an Exosuit which takes into considerations of the rigid Exo-Skeleton and its limitations for patients suffering from loss of function of the arm. This paper concentrates on enabling a stroke affected person to perform flexion-extension at elbow joint. Validation of the developed model on general population is still needed.

References

  1. 1.
    Mathers, C., Fat, D.M., Boerma, J.T., World Health Organization: The global burden of disease: 2004 update. World Health Organization (2008)Google Scholar
  2. 2.
    McPhee, S.J., Hammer, G.D.: Nervous system disorders. Pathophysiol. Dis. Introd. Clin. Med. 59, 177–180 (2010)Google Scholar
  3. 3.
    Committee on Nervous System Disorders in Developing Countries the Board on Global Health and the Institute of Medicine. Neurological, Psychiatric, and Develop-Mental Disorders. National Academies Press, Washington, DC (2001)Google Scholar
  4. 4.
    Zhang, Y., Arakalian, V.: Design of a passive robotic ExoSuit for carrying heavy loads. In: Proceedings of the IEEE-RAS, 18th Annual International Conference on Humanoid Robots, Lyon, France (2018)Google Scholar
  5. 5.
    Gross, R., et al.: Modulation of lower limb muscle activity induced by curved walking in typically developing children. Gait Posture 50, 34–41 (2016)CrossRefGoogle Scholar
  6. 6.
    Viteckova, S., Kutilek, P., Jirina, M.: Wearable lower limb robotics: a review. Biocybern. Biomed. Eng. 33(2), 96–105 (2013)CrossRefGoogle Scholar
  7. 7.
    Rupala, B.S., Singla, A., Virk, G.S.: Lower limb exoskeletons: a brief review. In: Proceedings of the Conference on Mechanical Engineering and Technology COMET, Varanasi, Utter Pradesh, pp. 18–24 (2016)Google Scholar
  8. 8.
    Collo, A., Bonnet, V., Venture, G.: A quasi-passive lower limb exoskeleton for partial body weight support. In: Proceedings of the 6th IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob), UTown, Singapore, pp. 643–648 (2016)Google Scholar
  9. 9.
    Stewart, A.M., Pretty, C.G., Adams, M., Chen, X.: Review of upper limb hybrid exoskeletons. IFAC 50(1), 15169–15178 (2017)Google Scholar
  10. 10.
    Serea, F., Poboroniuc, M., Hartopanu, S., Olaru, R.: Exoskeleton for upper arm rehabilitation for disabled patients. In: International Conference and Exposition on Electrical and Power Engineering, (EPE 2014), pp. 153–157 (2014)Google Scholar
  11. 11.
    Perry, J.C., Rosen, J., Burns, S.: Upper-limb powered exoskeleton design. IEEE/ASM Trans. Mechatron. 12(4), 408–417 (2007)CrossRefGoogle Scholar
  12. 12.
    Li, B., Yuan, B., Chen, J., Zuo, Y., Yang, Y.: Mechanical design and human-machine coupling dynamic analysis of a lower extremity exoskeleton. In: Huang, Y., Wu, H., Liu, H., Yin, Z. (eds.) ICIRA 2017. LNCS (LNAI), vol. 10462, pp. 593–604. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-65289-4_56CrossRefGoogle Scholar
  13. 13.
    Jarrasé, N.: Contributions à l’explotation d’exosquelettes actifs pour la rééducation neuromotrice. Ph.D. thesis of Pierre et Marie Curie University (UPMC) (2010)Google Scholar
  14. 14.
    Gunn, M., Shank, T.M., Epps, M., Hossain, J., Rahman, T.: User evaluation of a dynamic arm orthosis for people with neuromuscular disorders. IEEE Trans. Neural Syst. Rehabil. Eng. 24(12), 1277–1283 (2016)CrossRefGoogle Scholar
  15. 15.
    Seth, D., Chablat, D., Bennis, F., Sakka, S., Jubeau, M., Nordez, A.: New dynamic muscle fatigue model to limit musculo-skeletal disorder. In: Virtual Reality International Conference 2016, Article no. 26 (2016)Google Scholar
  16. 16.
    Seth, D., Chablat, D., Sakka, S., Bennis, F.: Experimental validation of a new dynamic muscle fatigue model. In: Duffy, V.G.G. (ed.) DHM 2016. LNCS, vol. 9745, pp. 54–65. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-40247-5_6CrossRefGoogle Scholar
  17. 17.
    Seth, D., Chablat, D., Bennis, F., Sakka, S., Jubeau, M., Nordez, A.: Validation of a new dynamic muscle fatigue model and DMET analysis. Int. J. Virtual Real. 2016(16), 2016 (2016)Google Scholar
  18. 18.
    Talaty, M., Esquenazi, A., Briceno, J.E.: Differentiating ability in users of the ReWalk(TM) powered exoskeleton: an analysis of walking kinematics. In: Proceedings of the IEEE International Conference on Rehabilitation Robotics (ICORR), Seattle, USA, pp. 1–5 (2013).  https://doi.org/10.1109/icorr.2013.6650469
  19. 19.
    Aoustin, Y.: Walking gait of a biped with a wearable walking assist device. Int. J. of Humanoid Robotics 12(2), 1 550 018-1–11 550 018-20 (2015).  https://doi.org/10.1142/s0219843615500188CrossRefGoogle Scholar
  20. 20.
    Ktistakis, I.P., Bourbakis, N.G.: A survey on robotic wheelchairs mounted with robotic arms. In: National Aerospace and Electronics Conference (NAECON), pp. 258–262 (2015)Google Scholar
  21. 21.
    Aoustin, Y., Formalskii, A.: Walking of biped with passive exoskeleton: evaluation of energy consumption. Multibody Syst. Dyn. 43, 71–96 (2017).  https://doi.org/10.1007/s11044-017-9602-7MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Park, W., Jeong, W., Kwon, G., Kim, Y.H., Kim, L.: A rehabilitation device to improve the hand grasp function of stroke patients using a patient-driven approach. In: IEEE International Conference on Rehabilitation Robotics, Seattle Washington, USA (2013)Google Scholar
  23. 23.
    Akhmadeev, K., Rampone, E., Yu, T., Aoustin, Y., Le Carpentier, E.: A testing system for a real-time gesture classification using surface EMG. In: Proceedings of the 20th IFAC World Congress, Toulouse France (2017)Google Scholar
  24. 24.
    Schwartz, C., Lempereur, M., Burdin, V., Jacq, J.J., Rémy-Néris, O.: Shoulder motion analysis using simultaneous skin shape registration. In: Proceedings of the 29th Annual International Conference of the IEEE EMBS, Lyon, France (2007)Google Scholar
  25. 25.
    National Stroke Association Brochure (2017)Google Scholar
  26. 26.
    Nef, T., Guidali, M., Riener, R.: ARMin III – arm therapy exoskeleton with an ergonomic shoulder actuation. Appl. Bionics Biomech. 6(2), 127–142 (2009)CrossRefGoogle Scholar
  27. 27.
    Krebs, H.I., Hogan, N., Volpe, B.T., Aisen, M.L., Edelstein, L., Diels, C.: Overview of clinical trials with MITMANUS: a robot-aided neuro-rehabilitation facility. Technol. Health Care 7(6), 419–423 (1999)Google Scholar
  28. 28.
    Ali, H.: Bionic exoskeleton: history, development and the future. IOSR J. Mechan. Civ. Eng. 58–62 (2014)Google Scholar
  29. 29.
    Banala, S.K., Agrawal, S.K., Scholz, J.P.: Active leg exoskeleton (ALEX) for gait rehabilitation of motor-impaired patients. In: IEEE 2007 Rehabilitation Robotics, pp. 401–407 (2007)Google Scholar
  30. 30.
    Fitle, K.D., Pehlivan, A.U., O’Malley, M.K.: A robotic exoskeleton for re-habilitation and assessment of the upper limb following incomplete spinal cord in-jury. In: 2015 IEEE International Conference on Robotics and Automation (ICRA), pp. 4960–4966 (2015)Google Scholar
  31. 31.
  32. 32.
  33. 33.
    Plagenhoef, S., et al.: Anatomical data for analyzing human motion (1983)Google Scholar

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[Abstract + References] Arm Games for Virtual Reality Based Post-stroke Rehabilitation – Conference paper

Abstract

Stroke is a leading cause of serious long-term disability. World Health Organization (WHO) published that the second leading of death is stroke accident and every year, 15 million people worldwide suffer from stroke attack, two-thirds of them have a permanent disability. Muscle impairment can be treated by intensive movements involving repetitive task, task-oriented and task-variegated. Conventional stroke rehabilitation is expensive, less engaging and at the same time need more time for the rehabilitation process and need more energy and time for the therapist to guide the stroke-survivor. Modern stroke rehabilitation is more promising and more effective with modern rehabilitation aids allowing the rehabilitation process to be faster, however, this therapist method can be obtained in the big cities. To cover the lack of rehabilitation process in this research will develop and improve post-stroke rehabilitation using games. This research using electromyography (EMG) device to analyze the muscle contraction during the rehabilitation process and using Kinect XBOX to record trajectory hands movements. Five games from movements sequence have designed and will be examined in this research. This games obtained two results, the first is the EMG signal and the second is trajectory data. EMG signal can recognize muscle contractions during playing game and the trajectory data can save the pattern of movements and showed the pattern to the monitor. EMG signal processing using time or frequency feature extractions is a good idea to obtain more information from muscle contractions, also velocity, similarities and error movements can be obtained by study the possible approaches.

References

  1. 1.
    Leading Cause of Death Malaysia: Stroke.: Retrieved from http://www.worldlifeexpectancy.com/malaysia-stroke (2017)
  2. 2.
    Mayo ayo Clinic Staff.: Stroke rehabilitation: what to expect as you recover. Retrieved from http://www.mayoclinic.org/stroke-rehabilitation/art-20045172 (2017)
  3. 3.
    Dobkin, B.H.: Strategies for stroke rehabilitation. Lancet Neurol. 3(9), 528–536 (2004)CrossRefGoogle Scholar
  4. 4.
    Riener, R., Frey, M., Bernhardt, M., Nef, T., Colombo, G.: Human-centered rehabilitation robotics. In: 9th International Conference on Rehabilitation Robotics, pp. 319–322 (2005)Google Scholar
  5. 5.
    Yeh, S., Lee, S., Wang, J., Chen, S., Chen, Y., Yang, Y., Hung, Y.: Virtual reality for post-stroke shoulder-arm motor rehabilitation : training system & assessment method. In: Paper Presented at 14th International Conference on e-Health Networking, Applications and Services, pp. 190–195. Beijing, China: IEEE (2012)Google Scholar
  6. 6.
    Yeh, S., Stewart, J., McLaughlin, M., Parsons, T., Winstein, C. J., Rizzo, A.: VR aided motor training for post-stroke rehabilitation: system design, clinical test, methodology for evaluation. In: Proceedings of the IEEE Virtual Reality Conference, pp. 299–300. Charlotte, USA (2007)Google Scholar
  7. 7.
    Prashun, P., Hadley, G., Gatzidis, C., Swain, I.: Investigating the trend of virtual reality-based stroke rehabilitation systems. In: Proceedings of the 14th International Conference Information Visualisation, pp. 641–647. London, UK (2010)Google Scholar
  8. 8.
    Trombetta, M., Henrique, M., Rogofski, B.: Motion Rehab AVE 3D: VR-based exergame for post stroke rehabilitation. J. Comput. Methods Progr. Biomed. 151, 15–20 (2017).  https://doi.org/10.1016/j.cmpb.2017.08.008CrossRefGoogle Scholar
  9. 9.
    Esfahlani, S., Bogdan, M., Alireza, S., George, W.: Validity of the Kinect and Myo armband in serious game for assessing upper-limb movement. J. Entertain. Comput. 27, 150–156 (2018).  https://doi.org/10.1016/j.entcom.2018.05.003CrossRefGoogle Scholar
  10. 10.
    Kutlu M., Freeman C., Ann-Marie H.: A home based FES system for upper-limb stroke rehabilitation with iterative learning control. J. Int. Fed. Autom. Control. Papers on-line 50(1), 12089–12094 (2016)CrossRefGoogle Scholar
  11. 11.
    Khairunizam, W., K., Suhaimi, R., Aswad, A.R.: Design of arm movement sequence for upper limb management after stroke. In: Proceedings of the International workshop on Nonlinier Circuits, Communications and Signal Processing. George Town, Malaysia (2015)Google Scholar
  12. 12.
    Sevgi, A., Ilkin, M., Oya, Umit Y., Sacide, S.: Virtual reality in upper extremity rehabilitation of stroke patients: a randomized controlled trial. J. Stroke cerebrovasc. Dis. 27(2), 3473–3478 (2018).  https://doi.org/10.1016/j.jstrokecerebrovasdis.2018.08.007CrossRefGoogle Scholar
  13. 13.
    Rash, G.S., EdD.: Electromyography fundamentals. https://www.researchgate.net/publication/265248133_Electromyography_Fundamentals (2002)
  14. 14.
    Kaewboon, W., Limsakul, C., Phukpattaranont, P.: Upper limbs rehabilitation system for stroke patient with biofeedback and force. In: Proceedings of the Biomedical Engineering International Conference. Amphur Muang, Thailand (2013)Google Scholar
  15. 15.
    Ritchie, H., Roser, M.: Causes of death. https://ourworldindata.org/causes-of-death (2017)
  16. 16.
  17. 17.
    Riener, R., Frey, M., Bernhardt, M., Nef, T., Colombo, G.: Human centered rehabilitation robotics. In: Proceedings of the 9th International Conference Rehabilitation Robotics, pp. 319–322. Chicago, USA (2005)Google Scholar
  18. 18.
    Ivey, F.M., Hafer-Macko, C.E., Macko, R.F.: Exercise rehabilitation after stroke. J. NeuroRx 3(4), 439–450 (2006).  https://doi.org/10.1016/j.nurx.2006.07.011CrossRefGoogle Scholar
  19. 19.
    Htoon, Z.L., Na’im, Sidek, S., Fatai, S.: Assessment of upper limb MUSCLE tone level based on estimated impedance parameters. In: Proceedings of the Conference on Biomedical Engineering and Sciences. Kuala Lumpur, Malaysia (2016)Google Scholar
  20. 20.
    Kleim, J.A., Jones, T.A.: Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage. J. Speech Lang Hear Res. 51(1), 225–239 (2008).  https://doi.org/10.1044/1092-4388(2008/018)CrossRefGoogle Scholar
  21. 21.
    Takeuchi, N., Izumi, S.I.: Rehabilitation with post-stroke motor recovery: a review with a focus on neural plasticity. J. Stroke Res. Treat. (2013).  https://doi.org/10.1155/2013/128641CrossRefGoogle Scholar
  22. 22.
    Sveistrup, H.: Motor rehabilitation using virtual reality. J. NeuroEng. Rehabil. (2004).  https://doi.org/10.1186/1743-0003-1-10CrossRefGoogle Scholar
  23. 23.
    Langhorne, P., Bernhardt, J., Kwakkel, G.: Stroke rehabilitation. Lancet. J. Stroke Care 377(9778), 1693–1702 (2011).  https://doi.org/10.1016/S0140-6736(11)60325-5CrossRefGoogle Scholar
  24. 24.
    Suhaimi, R., Khairunizam, W., Ariffin, M.A.: Design of movement sequence for arm rehabilitation of post-stroke. In: Proceedings of the International Conference on Control System, Computing and Engineering. George Town, Malaysia (2015)Google Scholar
  25. 25.
    Suhaimi, R., Aswad, A.R., Adnan, N.H., Asyraf, F., Khairunizam, W., Hazry, D., Shahriman, A.B., Bakar, A., Razlan, Z.M.: Analysis of EMG-based muscles activity for stroke rehabilitation. In: Proceedings of the 2nd International Conference on Electronic Design (ICED), pp. 167–170. Penang, Malaysia (2014)Google Scholar
  26. 26.
    Basri, N.C., Khairunizam, W., Zunaidi, I., Bakar, S.A., Razlan, Z.M.: Investigation of upper limb movements for VR based post-stroke rehabilitation device. In: Proceedings of the 14th International Colloquium on signal processing and It’s Aplications (CSPA). Batu Feringghi, Malaysia (2018)Google Scholar
  27. 27.
    Majid, M.S.H., Khairunizam, W., Shahriman, A.B., Zunaidi, I.: EMG feature extraction for upper-limb functional movement during rehabilitation. In: Proceedings of the International Conference on Intelegent Informatics and Biomedical Science (ICIIMBS). Bangkok, Thailand (2018)Google Scholar
  28. 28.
    Majid, M.S., Khairunizam, W., Shahriman, A.B., Bakar, A.S., Zunaidi, I.: Performance evaluation of a VR-based arm rehabilitation using movements sequence patttern. In: Proceedings of the 14th International Colloquium on Signal Processing and It’s Aplications (CSPA). Batu Feringghi, Malaysia (2018)Google Scholar
  29. 29.
    Recommendation for sesor locations on individual muscles. Retrieved from http://seniam.org/sensor_location.htm

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[WEB SITE] Telerehab Program Works as Well as Clinic-Based Program for Improved Arm Function Poststroke – JAMA Neurology

It’s probably not news to physical therapists (PTs) when research backs up the idea that patients who experience arm impairments poststroke will tend to make greater functional improvements with larger and longer doses of rehabilitation. Unfortunately, PTs are also familiar with the fact that what’s optimal isn’t necessarily what’s typical, with challenges such as payment systems, logistics, and clinic access making it difficult to achieve the best possible results. That’s where telerehabilitation could make a big difference, say authors of a new study that found an entirely remotely delivered rehab program to be as effective as an equal amount of clinic-based sessions.

The findings lend further support to the ideas behind APTA’s efforts to increase telehealth opportunities for PTs and their patients—a significant component of the association’s current public policy priorities. In addition, APTA provides multiple telehealth resources on a webpage devoted to the topic, and has created the Frontiers in Research, Science, and Technology Council that provides interested members and other stakeholders with an online community to discuss technology’s role in physical therapy.

The study, published in JAMA Neurology (abstract only available for free), involved 124 participants who experienced arm motor deficits poststroke. All participants were enrolled in a rehabilitation therapy program that included 36 70-minute treatment sessions, half of which were supervised, over a 6- to 8-week period. The only major difference: one group’s supervised sessions were face-to-face with a physical therapist (PT) or occupational therapist (OT), while the other group received telerehab from a PT or OT via a computer with video capabilities, accompanied by the use of a gaming system.

Researchers were interested in finding out how patients fared in each approach, using scores from the Fugl Meyer (FM) assessment of motor recovery poststroke as their primary measure. Authors of the study also measured patient adherence with therapy as well as levels of patient motivation related to how well they liked the therapy they were receiving and their degree of dedication to treatment goals.

Using a treatment approach “based on an upper-extremity task-specific training manual and Accelerated Skill Acquisition Program,” researchers set up matched programs that included at least 15 minutes per session of arm exercises from a common set of 88 possible exercises, at least 15 minutes of functional training, and 5 minutes of stroke education. The clinic-based participants received in-person instruction on the exercises and used “standard exercise hardware”; the telerehab patients received instructions via video link and engaged in functional exercise via a videogame interface. Here’s what the researchers found:

  • Both groups improved at about the same rate, with the telerehab participants averaging a 7.86 FM gain, compared with an average gain of 8.36 points for the clinic-based group.
  • Improvements were also about the same for the subgroup of participants who entered rehabilitation more than 90 days poststroke, with these “late” participants averaging a 6.6-point gain for the telerehab group and a 7.4-point increase for the clinic-based group.
  • While both groups reported high levels of dedication to treatment goals, the clinic-based group tended to report better levels of motivation and satisfaction. Adherence was also high for both groups, with a 93.4% adherence rate for the clinic-based group and a rate of 98.3% for the telerehab group.
  • Both groups increased their knowledge of stroke at similar rates.

As for the technical details of the telerehab sessions, the system included a computer linked to the internet, a table, a chair, and 12 “gaming input devices.” Keyboards were not necessary. The supervised sessions began with a 30-minute videoconference between the patient and therapist, and the functional training games used were designed to match the functional task work being done with the clinic-based participants. Unsupervised sessions adhered to the same content but didn’t include contact with the therapist.

“In an era when prescribed doses of poststroke rehabilitation therapy are declining, adversely affecting patient outcomes, these and prior findings suggest that outcomes could be improved for many patients…if larger doses of rehabilitation therapy were prescribed,” authors write. “Our study found that a 6-week course of daily home-based [telerehab] is safe, is rated favorably by patients, is associated with excellent treatment adherence, and produces substantial gains in arm function that were not inferior to dose-matched interventions delivered in the clinic.”

Authors acknowledged that patient satisfaction with telerehab might be improved by increasing the amount of time spent with the therapist—providing that therapist is properly trained. “Current results underscore the importance of maintaining a licensed therapist’s involvement during [telerehab],” they write.

Ultimately, it’s still too early to determine just how generalizable the findings are to other populations and conditions, the researchers say, but all indicators seem to point to the need for increasing the availability of telerehab and its inclusion in health plans.

“The US Bipartisan Budget Act of 2018 expanded telehealth benefits,” authors write. “Eventually, home-based [telerehab] may plan an ascendant role for improving patient outcomes.”

Research-related stories featured in PT in Motion News are intended to highlight a topic of interest only and do not constitute an endorsement by APTA. For synthesized research and evidence-based practice information, visit the association’s PTNow website.

via JAMA Neurology: Telerehab Program Works as Well as Clinic-Based Program for Improved Arm Function Poststroke

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[ARTICLE] Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke – Full Text

Abstract

Upper limb motor deficits in severe stroke survivors often remain unresolved over extended time periods. Novel neurotechnologies have the potential to significantly support upper limb motor restoration in severely impaired stroke individuals. Here, we review recent controlled clinical studies and reviews focusing on the mechanisms of action and effectiveness of single and combined technology-aided interventions for upper limb motor rehabilitation after stroke, including robotics, muscular electrical stimulation, brain stimulation and brain computer/machine interfaces. We aim at identifying possible guidance for the optimal use of these new technologies to enhance upper limb motor recovery especially in severe chronic stroke patients. We found that the current literature does not provide enough evidence to support strict guidelines, because of the variability of the procedures for each intervention and of the heterogeneity of the stroke population. The present results confirm that neurotechnology-aided upper limb rehabilitation is promising for severe chronic stroke patients, but the combination of interventions often lacks understanding of single intervention mechanisms of action, which may not reflect the summation of single intervention’s effectiveness. Stroke rehabilitation is a long and complex process, and one single intervention administrated in a short time interval cannot have a large impact for motor recovery, especially in severely impaired patients. To design personalized interventions combining or proposing different interventions in sequence, it is necessary to have an excellent understanding of the mechanisms determining the effectiveness of a single treatment in this heterogeneous population of stroke patients. We encourage the identification of objective biomarkers for stroke recovery for patients’ stratification and to tailor treatments. Furthermore, the advantage of longitudinal personalized trial designs compared to classical double-blind placebo-controlled clinical trials as the basis for precise personalized stroke rehabilitation medicine is discussed. Finally, we also promote the necessary conceptual change from ‘one-suits-all’ treatments within in-patient clinical rehabilitation set-ups towards personalized home-based treatment strategies, by adopting novel technologies merging rehabilitation and motor assistance, including implantable ones.

Introduction

Stroke constitutes a major public health problem affecting millions of people worldwide with considerable impacts on socio-economics and health-related costs. It is the second cause of death (Langhorne et al., 2011), and the third cause of disability-adjusted life-years worldwide (Feigin et al., 2014): ∼8.2 million people were affected by stroke in Europe in 2010, with a total cost of ∼€64 billion per year (Olesen et al., 2012). Due to ageing societies, these numbers might still rise, estimated to increase 1.5–2-fold from 2010 to 2030 (Feigin et al., 2014).

Improving upper limb functioning is a major therapeutic target in stroke rehabilitation (Pollock et al., 2014Veerbeek et al., 2017) to maximize patients’ functional recovery and reduce long-term disability (Nichols-Larsen et al., 2005Veerbeek et al., 2011Pollock et al., 2014). Motor impairment of the upper limb occurs in 73–88% first time stroke survivors and in 55–75% of chronic stroke patients (Lawrence et al., 2001). Constraint-induced movement therapy (CIMT), but also standard occupational practice, virtual reality and brain stimulation-based interventions for sensory and motor impairments show positive rehabilitative effects in mildly and moderately impaired stroke victims (Pollock et al., 2014Raffin and Hummel, 2018). However, stroke survivors with severe motor deficits are often excluded from these therapeutic approaches as their deficit does not allow easily rehabilitative motor training (e.g. CIMT), treatment effects are negligible and recovery unpredictable (Byblow et al., 2015Wuwei et al., 2015Buch et al., 2016Guggisberg et al., 2017).

Recent neurotechnology-supported interventions offer the opportunity to deliver high-intensity motor training to stroke victims with severe motor impairments (Sivan et al., 2011). Robotics, muscular electrical stimulation, brain stimulation, brain computer/machine interfaces (BCI/BMI) can support upper limb motor restoration including hand and arm movements and induce neuro-plastic changes within the motor network (Mrachacz-Kersting et al., 2016Biasiucci et al., 2018).

The main hurdle for an improvement of the status quo of stroke rehabilitation is the fragmentary knowledge about the physiological, psychological and social mechanisms, their interplay and how they impact on functional brain reorganization and stroke recovery. Positive stimulating and negatively blocking adaptive brain reorganization factors are insufficiently characterized except from some more or less trivial determinants, such as number and time of treatment sessions, pointing towards the more the better (Kwakkel et al., 1997). Even the long accepted model of detrimental interhemispheric inhibition of the overactive contralesional brain hemisphere on the ipsilesional hemisphere is based on an oversimplification and lack of differential knowledge and is thus called into question (Hummel et al., 2008Krakauer and Carmichael, 2017Morishita and Hummel, 2017).

Here, we take a pragmatic approach of comparing effectiveness data, keeping this lack of knowledge of mechanisms in mind and providing novel ideas towards precision medicine-based approaches to individually tailor treatments to the characteristics and needs of the individual patient with severe chronic stroke to maximize rehabilitative outcome.[…]

Continue —>   Neurotechnology-aided interventions for upper limb motor rehabilitation in severe chronic stroke | Brain | Oxford Academic

Conceptualization of longitudinal personalized rehabilitation-treatment designs for patients with severe chronic stroke. Ideally, each patient with severe chronic stroke with a stable motor recovery could be stratified based on objective biomarkers of stroke recovery in order to select the most appropriate/promising neurotechnology-aided interventions and/or their combination for the specific case. Then, these interventions can be administered in the clinic and/or at home in sequence, moving from one to another only when patient’s motor recovery plateaus. In this way, comparisons of the efficacy of each intervention (grey arrows) are still possible, and if the selected interventions and/or their combination are suitable, motor recovery could increase.

Conceptualization of longitudinal personalized rehabilitation-treatment designs for patients with severe chronic stroke. Ideally, each patient with severe chronic stroke with a stable motor recovery could be stratified based on objective biomarkers of stroke recovery in order to select the most appropriate/promising neurotechnology-aided interventions and/or their combination for the specific case. Then, these interventions can be administered in the clinic and/or at home in sequence, moving from one to another only when patient’s motor recovery plateaus. In this way, comparisons of the efficacy of each intervention (grey arrows) are still possible, and if the selected interventions and/or their combination are suitable, motor recovery could increase.

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[NEWS] NEOFECT Wins Design Week VirtualTech Award for Second Year In a Row

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SmartBoardforHome

NEOFECT was once again honored at the San Francisco Design Week (SFDW) Awards, winning the VirtualTech award for its new Smart Board for Home NextGen, a gamified rehabilitation device for stroke survivors to use at home.

This marks the second consecutive year that the company has received the VirtualTech award, according to a company announcement.

“The Smart Board for Home NextGen is the epitome of the 2019 SFDW Awards theme, and we’re humbled to have won this year after receiving Honorable Mention in the VirtualTech category last year for our Smart Glove for Home,” says Scott Kim, co-founder and CEO of NEOFECT USA, in the release.

“We took every aspect of the patient experience into account when redesigning the Smart Board for Home NextGen,” Kim adds.

“For example, stroke patients’ grip is often weak, so we re-engineered the handle to be more secure. We developed more interactive virtual reality games, like tennis, so patients can have more variety, and also created a dual-player option.”

SFDW is an international design competition that honors projects encouraging thought leadership in design, focusing on “Where Innovation Meets Social Responsibility.”

The awards celebrate and recognize exemplary work in all fields of design, including architecture, interior design, industrial design, communication design, and user experience design.

Twenty-four winning projects and 11 honorable mentions were selected by a jury comprised of professionals—including executives from Lyft, Google, Microsoft, and Fitbit—who reviewed submissions from a pool of applicants from the USA and Europe. Each winning project was judged based on impact, singularity, inclusiveness, social responsibility, ease of use, visual appeal, and feasibility.

Award winners from leading design firms, in-house teams, and creative individuals were honored recently during a ceremony that took place at Pier 27 in San Francisco, the release explains.

“We are extremely excited the San Francisco Design Week Awards returned this year,” states SFDW Executive Director Dawn Zidonis.

“As with last year, the quality of the many entries exceeded our expectations. Congratulations to this year’s outstanding and diverse winners, including NEOFECT.”

[Source(s): NEOFECT, Business Wire]

 

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[Abstract] Differential Poststroke Motor Recovery in an Arm Versus Hand Muscle in the Absence of Motor Evoked Potentials

Background. After stroke, recovery of movement in proximal and distal upper extremity (UE) muscles appears to follow different time courses, suggesting differences in their neural substrates.

Objective. We sought to determine if presence or absence of motor evoked potentials (MEPs) differentially influences recovery of volitional contraction and strength in an arm muscle versus an intrinsic hand muscle. We also related MEP status to recovery of proximal and distal interjoint coordination and movement fractionation, as measured by the Fugl-Meyer Assessment (FMA).

Methods. In 45 subjects in the year following ischemic stroke, we tracked the relationship between corticospinal tract (CST) integrity and behavioral recovery in the biceps (BIC) and first dorsal interosseous (FDI) muscle. We used transcranial magnetic stimulation to probe CST integrity, indicated by MEPs, in BIC and FDI. We used electromyography, dynamometry, and UE FMA subscores to assess muscle-specific contraction, strength, and inter-joint coordination, respectively.

Results. Presence of MEPs resulted in higher likelihood of muscle contraction, greater strength, and higher FMA scores. Without MEPs, BICs could more often volitionally contract, were less weak, and had steeper strength recovery curves than FDIs; in contrast, FMA recovery curves plateaued below normal levels for both the arm and hand.

Conclusions. There are shared and separate substrates for paretic UE recovery. CST integrity is necessary for interjoint coordination in both segments and for overall recovery. In its absence, alternative pathways may assist recovery of volitional contraction and strength, particularly in BIC. These findings suggest that more targeted approaches might be needed to optimize UE recovery.

 

via Differential Poststroke Motor Recovery in an Arm Versus Hand Muscle in the Absence of Motor Evoked Potentials – Heidi M. Schambra, Jing Xu, Meret Branscheidt, Martin Lindquist, Jasim Uddin, Levke Steiner, Benjamin Hertler, Nathan Kim, Jessica Berard, Michelle D. Harran, Juan C. Cortes, Tomoko Kitago, Andreas Luft, John W. Krakauer, Pablo A. Celnik, 2019

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[NEWS] NEOFECT Redesigns Smart Board for Home

Published on May 8, 2019

SmartBoardforHome

NEOFECT has redesigned its Smart Board for Home in reply to feedback from patients recovering from stroke and other musculoskeletal conditions and neurological disorders.

The new Smart Board for Home NextGen includes a smaller surface to help patients use it at home more easily, a redesigned handle to better stabilize the user’s hand and arm, and updated gamified software.

The board size has been reduced from 42 inches to 32 inches so it can fit on most tables. To accommodate the weakened grip of many stroke patients, the redesigned handle includes more straps to better stabilize the user’s arm, ensure appropriate measurement for the post-game metrics, and provide a more secure, comfortable experience, according to the company in a media release.

“We took patient feedback and completely revamped the Smart Board for Home NextGen,” says Scott Kim, co-founder and CEO of San Francisco-based NEOFECT USA.

“This new model still has all the fun, measurable qualities patients can use at home, but now we’ve reduced even more barriers so that people of all abilities can gain back function in their hands and upper arms.”

Patients play games on the Smart Board for Home NextGen by placing their forearm in a cradle and moving their arm across the board. All movements are virtually mimicked on a Bluetooth-connected screen in real time. The gamified software also features an updated AI-powered algorithm to curate a more customized experience for each patient.

The Smart Board for Home NextGen games mimic real-world motions to rehabilitate users’ upper arms and shoulders, including new games like “Air Hawk” and “Tennis.”

Additionally, NEOFECT is developing a dual-player game for patients to use at home, which will be available in summer 2019.

[Source(s): NEOFECT, Business Wire]

Source:
http://www.rehabpub.com/2019/05/neofect-redesigns-smart-board-home/

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[Abstract] Brain-machine interface of upper limb recovery in stroke patients rehabilitation: A systematic review

Abstract

BACKGROUND:

Technologies such as brain-computer interfaces are able to guide mental practice, in particular motor imagery performance, to promote recovery in stroke patients, as a combined approach to conventional therapy.

OBJECTIVE:

The aim of this systematic review was to provide a status report regarding advances in brain-computer interface, focusing in particular in upper limb motor recovery.

METHODS:

The databases PubMed, Scopus, and PEDro were systematically searched for articles published between January 2010 and December 2017. The selected studies were randomized controlled trials involving brain-computer interface interventions in stroke patients, with upper limb assessment as primary outcome measures. Reviewers independently extracted data and assessed the methodological quality of the trials, using the PEDro methodologic rating scale.

RESULTS:

From 309 titles, we included nine studies with high quality (PEDro ≥ 6). We found that the most common interface used was non-invasive electroencephalography, and the main neurofeedback, in stroke rehabilitation, was usually visual abstract or a combination with the control of an orthosis/robotic limb. Moreover, the Fugl-Meyer Assessment Scale was a major outcome measure in eight out of nine studies. In addition, the benefits of functional electric stimulation associated to an interface were found in three studies.

CONCLUSIONS:

Neurofeedback training with brain-computer interface systems seem to promote clinical and neurophysiologic changes in stroke patients, in particular those with long-term efficacy.

via: https://www.ncbi.nlm.nih.gov/pubmed/30609208

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[BLOG POST] Stroke rehabilitation: maximizing arm and hand function after stroke – Evidently Cochrane

Potters hands

Stroke is the leading cause of disability in developed countries. The effects of stroke on the upper extremities are a major cause of functional impairment. This impairment of the upper extremity often leads to loss of independence with activities of daily living and of important occupations. There has been much research along different schools of thought that are intended to help people regain function and range of motion in their hand and arm after stroke. A quick search through the Cochrane Library would lead you to over a dozen Systematic Reviews of different interventions for the upper limb for people with stroke: these include: Constraint-induced movement therapyMental practiceMirror therapyVirtual realityRepetitive task practiceElectrical stimulation, and Occupational therapy for stroke … the list goes on.

So while it’s great that we are accumulating more and more evidence all the time, the challenge for therapists is that we just don’t have the time to spend scouring through the research, trying to find which one of these interventions is most effective for regaining upper limb function. Thankfully, Pollock and colleagues did the work for us and published a Cochrane Corner Overview paper titled “Interventions for Improving Upper Limb Function after Stroke.”

What was different about this study?

First, this study was called an “Overview” because it is basically a systematic review of systematic reviews of stroke on the upper extremity. In total, it included 40 systematic reviews (19 Cochrane Reviews and 21 non-Cochrane reviews with 18,078 participants) looking at improving arm function after stroke. That is a lot of research by any means. Their intent was to summarize the best evidence and, whenever possible, provide a side by-side comparison of interventions to give healthcare providers a succinct overview of the typical interventions for stroke to rehabilitate the upper limb.

So what did they find?

Good news and bad news. The bad news is they found that:

  • “There is no high quality evidence for any interventions that are currently routine practice, and evidence is insufficient to enable comparison of the relative effectiveness of interventions.” In other words, the evidence is insufficient to show which of the interventions are the most effective for improving upper limb function.

The good news is that they did find:

  • “Moderate quality evidence suggests that each of the following interventions may be effective: Constraint-Induced Movement Therapy (CIMT), Mental Practice, Mirror Therapy, interventions for sensory impairment, Virtual Reality and a relatively high dose of Repetitive Task Practice.”
  • Moderate quality evidence also indicates that unilateral arm training (exercise for the affected arm) may be more effective than bilateral arm training (doing the same exercise with both arms at the same time).
  • Some evidence shows that a greater dose of an intervention is better than a lesser dose.
  • “Effective collaboration is urgently needed to support definitive randomized controlled trials of interventions used routinely within clinical practice. Evidence related to dose is particularly needed because this has widespread clinical and research implications.”

What do we know about how intense therapy should be?

Team of health professionals

Until recently, the Scottish Intercollegiate Guidelines Network 2010 (SIGN) guideline on stroke management and rehabilitation recommended considering Constraint Induced Movement. However, Repetitive Task Training was not routinely recommended for improving upper limb function, and increased intensity of therapy for improving upper limb function in stroke patients was also not recommended.

The NICE Guidelines Stroke Rehabilitation in Adults 2013 in the UK recommended that therapists consider Constraint Induced Movement Therapy, and offer initially at least 45 minutes of each relevant stroke rehabilitation therapy for a minimum of 5 days per week to people who have the ability to participate.

A recent article in Advances in Clinical Neuroscience and Rehabilitation, called The Future of Stroke Rehabilitation: Upper Limb Recovery, points out that there is real concern that the dose and intensity of upper limb rehabilitation after stroke is just too low. The article brings some research results that at least two to three hours of arm training a day, for six weeks, reduced impairment and improved function by clinically meaningful amounts when started one to two months after stroke. However, anything less than this does not appear to provide much benefit overall.

The newly released AHA/ASA Guidelines for Stroke 2016 pulls all the updated evidence together, and states that when it comes to upper limb therapy following stroke, the research suggests that a higher dose is better. These new guidelines state that the patients who perform more than three hours of therapy daily made significantly more functional gains than those receiving less than three hours. The AHA/ASA Guidelines states that there is preliminary evidence suggesting the ideal setting appears to be the inpatient rehabilitation setting. Additionally, rehabilitation is best performed by an interprofessional team that can include a physician with expertise in rehabilitation, nurses, physical therapists, occupational therapists, speech/language therapists, psychologists, and orthotists.

What are the implications for therapists?

In order to truly have evidence based practice, we first need to identify the highest quality evidence. One of the main goals of the Cochrane Overview was to direct therapists to the highest quality evidence when making day-to-day clinical decisions. As we know, each person and each stroke is different. So for therapists, this overview suggests that that we can and should look closely into the evidence for and consider using Constraint-Induced Movement Therapy (CIMT), Mental Practice, Mirror Therapy, Interventions for Sensory Impairment, Virtual Reality and Repetitive Task Training in our practice. Preliminary evidence also suggests that we need to provide at least three hours of therapy a day in the post-acute setting.

While updated guidelines and reviews of the best available evidence are very helpful, we must always use our clinical reasoning and judgement to decide which intervention is most appropriate in our particular practice setting. The guidelines suggest that it benefits the patients when we work synergistically to facilitate an increased intensity of therapy by combining our efforts within the interprofessional team. Finally, to be truly effective we should strive to translate the evidence into functional interventions to ultimately make meaningful improvements in everyday lives of our patients.

Stroke rehabilitation: maximizing arm and hand function after stroke by Danny Minkow is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
Based on a work at http://onlinelibrary.wiley.com/doi/10.1002/14651858.CD010820.pub2/full. Images have been purchased for Evidently Cochrane from istock.comand may not be reproduced.

Links:

Pollock A, Farmer SE, Brady MC, Langhorne P, Mead GE, Mehrholz J, van Wijck F. Cochrane Overview: interventions for improving upper limb function after stroke. Stroke 2015;46:e57-8. doi:10.1161/STROKEAHA.114.008295. Available from: http://stroke.ahajournals.org/content/46/3/e57.full.pdf+html

Pollock A, Farmer SE, Brady MC, Langhorne P, Mead GE, Mehrholz J, van Wijck F. Interventions for improving upper limb function after stroke. Cochrane Database of Systematic Reviews 2014, Issue 11. Art. No.: CD010820. DOI: 10.1002/14651858.CD010820.pub2.

Scottish Intercollegiate Guidelines Network (SIGN). Management of patients with stroke: rehabilitation, prevention and management of complications, and discharge planning. Edinburgh: SIGN; 2010. (SIGN publication no. 118). [cited June 2010]. Available from: http://www.sign.ac.uk/pdf/sign118.pdf

The Stroke Association. “Web Upper Limb Video. UK Stroke Forum: Cochrane overview of interventions to improve upper limb function after stroke”. YouTube videocast, 24:28. YouTube, 18 December, 2015. Web. 9 May 2016. https://www.youtube.com/watch?v=7XuSLrB319Q.

National Clinical Guideline Centre; National Institute for Health and Care Excellence (commissioner). Stroke rehabilitation: long term rehabilitation after stroke. London: National Clinical Guideline Centre, Royal College of Physicians; 2013 (NICE CG162). [Issued June 2013]. Available from: https://www.nice.org.uk/guidance/cg162

Ward NS, Kelly K, Brander F. The future of stroke rehabilitation: upper limb recovery. Advances in Clinical Neuroscience & Rehabilitation2015; 15(4): 6-8. Available from: http://www.acnr.co.uk/2015/09/the-future-of-stroke-rehabilitation-upper-limb-recovery/.

Clarke D, Forster A, Drummond A, Tyson S, Rodgers H, Jones F, Harris R. Delivering optimum intensity of rehabilitation in hospital and at home: what do we know? [PowerPoint slides]. Oral presentation at Key Advances in Stroke Rehabilitation conference, London, 12thJune 2013. Available from: http://www.medineo.org

Winstein CJ, Stein J, Arena R, Bates B, Cherney LR, Cramer SC, Deruyter F, Eng JJ, Fisher B, Harvey RL, Lang CE, MacKay-Lyons M, Ottenbacher KJ, Pugh S, Reeves MJ, Richards LG, Stiers W, Zorowitz RD; American Heart Association Stroke Council, Council on Cardiovascular and Stroke Nursing, Council on Clinical Cardiology, and Council on Quality of Care and Outcomes Research. Guidelines for Adult Stroke Rehabilitation and Recovery: A Guideline for Healthcare Professionals From the American Heart Association/American Stroke Association. Stroke 2016;47(6):e98-e169. doi: 10.1161/STR.0000000000000098. Available from: http://stroke.ahajournals.org/content/47/6/e98.full.pdf+html

via Stroke rehabilitation: maximizing arm and hand function after stroke – Evidently Cochrane

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[Abstract] The effectiveness of somatosensory retraining for improving sensory function in the arm following stroke: a systematic review

The aim of this study was to evaluate if somatosensory retraining programmes assist people to improve somatosensory discrimination skills and arm functioning after stroke.

Nine databases were systematically searched: Medline, Cumulative Index to Nursing and Allied Health Literature, PsychInfo, Embase, Amed, Web of Science, Physiotherapy Evidence Database, OT seeker, and Cochrane Library.

Studies were included for review if they involved (1) adult participants who had somatosensory impairment in the arm after stroke, (2) a programme targeted at retraining somatosensation, (3) a primary measure of somatosensory discrimination skills in the arm, and (4) an intervention study design (e.g. randomized or non-randomized control designs).

A total of 6779 articles were screened. Five group trials and five single case experimental designs were included (N = 199 stroke survivors). Six studies focused exclusively on retraining somatosensation and four studies focused on somatosensation and motor retraining. Standardized somatosensory measures were typically used for tactile, proprioception, and haptic object recognition modalities. Sensory intervention effect sizes ranged from 0.3 to 2.2, with an average effect size of 0.85 across somatosensory modalities. A majority of effect sizes for proprioception and tactile somatosensory domains were greater than 0.5, and all but one of the intervention effect sizes were larger than the control effect sizes, at least as point estimates. Six studies measured motor and/or functional arm outcomes (n = 89 participants), with narrative analysis suggesting a trend towards improvement in arm use after somatosensory retraining.

Somatosensory retraining may assist people to regain somatosensory discrimination skills in the arm after stroke.

via The effectiveness of somatosensory retraining for improving sensory function in the arm following stroke: a systematic review – Megan L Turville, Liana S Cahill, Thomas A Matyas, Jannette M Blennerhassett, Leeanne M Carey, 2019

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