• We are sorry, but NCBI web applications do not support your browser and may not function properly. More information
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Neurorehabil Neural Repair. Author manuscript; available in PMC Aug 23, 2009.
Published in final edited form as:
PMCID: PMC2730506
NIHMSID: NIHMS107217

Effects of Robot-assisted therapy on upper limb recovery after stroke: A Systematic Review

Gert Kwakkel, PhD,1,2 Boudewijn J. Kollen, PhD,3 and Hermano I. Krebs, PhD4,5,6

Abstract

Background and Purpose

To present a systematic review of studies that investigates the effects of robot-assisted therapy on motor and functional recovery in patients with stroke.

Summary of Review

A database of articles published up to October 2006 was compiled using the following MEDLINE key words: cerebral vascular accident, cerebral vascular disorders, stroke, paresis, hemiplegia, upper extremity, arm and robot. References listed in relevant publications were also screened. Studies that satisfied the following selection criteria were included: (1) patients were diagnosed with cerebral vascular accident; (2) effects of robot-assisted therapy for the upper limb were investigated; (3) the outcome was measured in terms of motor and/or functional recovery of the upper paretic limb; (4) The study was a randomised clinical trial (RCT). For each outcome measure, the estimated effect size (ES) and the summary effect size (SES) expressed in standard deviation units (SDU) were calculated for motor recovery and functional ability (ADL) using fixed and random effect models. Ten studies, involving 218 patients, were included in the synthesis. Their methodological quality ranged from 4 to 8 on a (maximum) 10 point scale. Meta-analysis showed a non-significant heterogeneous SES in terms of upper limb motor recovery. Sensitivity analysis of studies involving only shoulder-elbow robotics subsequently demonstrated a significant homogeneous SES for motor recovery of the upper paretic limb. No significant SES was observed for functional ability (ADL).

Conclusion

As a result of marked heterogeneity in studies between distal and proximal arm robotics, no overall significant effect in favour of robot-assisted therapy was found in the present meta-analysis. However, subsequent sensitivity analysis showed a significant improvement in upper limb motor function after stroke for upper arm robotics. No significant improvement was found in ADL function. However, the administered ADL scales in the reviewed studies fail to adequately reflect recovery of the paretic upper limb and valid instruments that measure outcome of dexterity of the paretic arm and hand are mostly absent in selected studies. Future research on the effects of robot-assisted therapy should therefore distinguish between upper and lower robotics arm training and concentrate on kinematical analysis to differentiate between genuine upper limb motor recovery and functional recovery due to compensation strategies by proximal control of the trunk and upper limb.

Keywords: robotics, cerebrovascular accident, Activities of daily living, Upper limb, Rebiew, systematic

Introduction

Stroke is the leading cause of disability in the United States. 750.000 individuals are affected each year and the prevalence rate is among 200–300 patients per 100.000 inhabitants.1 Although prospective epidemiological studies are lacking, findings of a number of longitudinal studies indicate that in 30% to 66% of hemiplegic stroke patients, the paretic arm remains without function when measured 6 months after stroke, whereas only 5% to 20% demonstrate complete functional recovery.2

The results of a systematic review involving 123 randomized clinical trials (RCTs) by van Peppen and colleagues demonstrated that there is strong evidence that intensity as well as task specificity are the main drivers in an effective treatment program after stroke. In addition, this training should be repetitive, functional, meaningful and challenging for a patient.3,4

However, the question as to how the effects of exercise therapy can be further enhanced in a clinical environment presents a challenge to answer. Therefore, there is a need to develop better ways to augment exercise training in a functional way. Using therapeutic adjuncts to facilitate clinical practice, such as robotics511, is a new promising development. Robotics allows patients to train independently of a therapist and to improve upon their own functional level (i.e., robot-assisted therapy). In particular, there is strong evidence for robot-assisted therapy to increase treatment compliance by way of introducing incentives to the patient, such as games. In addition, by using computer assisted devices for regaining upper limb function, the robot can easily apply new constraints, in order to optimize the required movement pattern. Therefore, the complexity of a motor task to be learned can be controlled for more precisely with robotics than in conventional treatment approaches.

Although many devices have been designed to deliver arm therapy in individuals with stroke, five of these devices, the MIT-MANUS5,6 (designed and built at the Massachusetts Institute of Technology), the ARM-GUIDE7 (Assisted Rehabilitation and Measurement guide), the MIME8,9 (Mirror-Image Motion Enabler), the InMotion2 Shoulder-Elbow Robot10 and the Bi-Manu-Track11, were tested in at least one Randomized Clinical Trial (RCT). The MIT-MANUS is a robot that allows subjects to execute reaching movements in the horizontal plane. This two degrees of freedom (DoF) robot enables unrestricted movements of the shoulder and elbow joints5. The ARM-GUIDE is a trombone-like device and has four controlled DoF. A DC servo motor can assist in the movement of a subject’s arm in the reaching direction along a linear track. Optical encoders record the position in the reach, elevation and yaw axes7. The MIME robot consists of a six DoF robot arm. The robot enables the bilateral practice of a three DoF shoulder-elbow movement, whereby the non-paretic arm guides the paretic arm12. The InMotion2 Shoulder-Elbow Robot, which is the commercial version of MIT-MANUS (Interactive Motion Technologies, Inc, Cambridge, MA, USA), has two DoF and provides shoulder/elbow training in the horizontal plane with a supported forearm10. The Bi-Manu-Track is designed to specifically train distal arm movements by practicing bilateral elbow pro- and supination as well as wrist flexion and extension in a mirror or parallel fashion11.

In the past, several studies were unable to prove superiority of one type of conventional stroke regimen over another1316, but there is strong evidence that highly repetitive movement training can result in improved recovery4,16. Applying robot-assisted therapy enables patients to practice intensively with their upper paretic limb. The objective of the present systematic review is to determine the additional effects of robot-assisted therapy on motor recovery and functional outcome in comparison with conventional treatment forms.

Material and methods

Literature search

A computerized literature search was conducted in MEDLINE, CINAHL, EMBASE, Cochrane Controlled Trial Register, DARE, SciSearch, Doc-on-line and PEDro. Studies were collected up to October 2006. Used MeSH and keywords were: cerebral vascular accident, cerebral vascular disorders, stroke, paresis, hemiplegia, upper extremity, arm and robot* (i.e. all words that start with the term ‘robot’). Literature lists of narrative reviews were also evaluated for relevant publications. Only articles written in the English, German or Dutch language were included. Studies were included when: (1) patients were diagnosed with cerebral vascular accident; (2) effects of robot-assisted therapy for the upper limb were investigated; (3) the outcome was measured in terms of motor and/or functional recovery of the upper paretic limb; (4) The study was a randomised clinical trial (RCT). Excluded were studies that compared the effects of two different types of robot-assisted therapy and studies of persons with chronic impairment due to stroke that compared discharge outcomes with pre-intervention stable scores (time since stroke onset > 6 months).

Definitions

Cerebral vascular Accident has been defined as ‘A sudden, non-convulsive loss of neurologic function due to an ischemic or hemorrhagic intracranial vascular event’ (PubMed [Medline], MeSH database, 2005). Robotics has been defined as: ‘The application of electronic, computerized control systems to mechanical devices designed to perform human functions’. Although formerly restricted to the industry, nowadays also certain human functions can be controlled by bionic (bioelectronic) devices, such as automated insulin pumps and other prostheses (PubMed [Medline], MeSH database, 2005). One independent reviewer (JH) selected articles based on title and abstract.

Methodological quality

The methodological quality of the studies was rated with the PEDro scale17 and scored by two independent reviewers (JH and GK). Inter-rater reliability of individual items was tested with Cohen’s Kappa statistics. When no consensus between the 2 reviewers was reached, a third reviewer made the final decision. Reviewers were not blinded to author(s), institution(s), or journal. PEDro scores of 4 points or higher were classified as ‘high quality’, whereas studies with 3 points or lower were classified as ‘low quality’.

Quantitative analysis

The abstracted data (mean age, numbers of patients in the experimental and control group, mean difference in change scores on the measure scales of motor recovery and functional level and their standard deviations in experimental and control groups at baseline) were entered in Excel for Windows. The effect size gi (Hedges’s g) for individual studies was established by calculating the difference between the means of the experimental and control groups divided by the average population SDi. If necessary, means and SDi were requested from the respective authors. Alternatively, Hedges’s g estimates were obtained from t values. The impact of sample size was determined by assigning a weighting factor (wi) to each study, in such a way that larger effect-weights were given to studies with larger samples. Finally, gu values of individual studies were averaged, resulting in a weighted SES, while the individual weights were combined to estimate the variance of the SES. The effect size gu for individual studies was computed for the degree of motor recovery and functional performance. If a significant between-study variation (Q-statistic) was found (representing statistical heterogeneity) a random effects model was applied. Based on the classification of Cohen, effect sizes < 0.2 were classified as small, between 0.2 and 0.5 as medium, and > 0.5 as large. For all outcome variables, the critical value for rejection of H0 hypothesis was set two-tailed at p<0.05.

Results

Appendix 2 shows the flow chart for the selection of studies. After searching the electronic databases 87 from 173 hits were considered to be relevant for further screening. However, from these 87 publications, after screening their abstracts forty-four relevant studies were selected. Ten of these articles were critical or narrative reviews6,7,1825, and 24 studies were non-controlled trials. Following the exclusion of (1) pre-experimental studies and (2) controlled studies that did not measure motor and/or functional recovery of the upper paretic limb, in total 22 studies were excluded4,9,2647. Finally, ten studies were identified as being relevant8,10,12,4854. Two studies referred to the same patient sample49,54. The study of Volpe et al54 presented outcomes on motor recovery of the upper paretic limb (Fugl-Meyer Arm motor score; FMA), whereas the study of Fasoli et al.49 reported on outcome of functional ability (Functional Independence Measure; FIM) in the same stroke population.

Table 1 shows the main characteristics of the ten eligible studies included in the present meta-analysis. The start of the therapy ranged from one week after stroke4850,53,54 to more than six months after stroke6,8,10,51,52.

Table 1
Study characteristics

Methodological Quality

The results of the methodological quality score of the ten RCTs are presented in Appendix 1. Cohen’s kappa for agreement was 0.82. The methodological quality varied from 4 points51,52 to 8 points49.

The groups of two studies were not comparable at baseline. In the study of Hesse et al50, the Barthel Index was statistically significant higher in favour of the experimental group. The study of Kahn et al.51 showed a statistical significant higher value for the Chedoke McMaster test in favour of the control group.

Quantitative analysis

Motor recovery

Seven studies8,10,12,48,50,53,54 used the F-M as outcome parameter, whereas the studies of Kahn et al.51,52 evaluated outcome with the Chedoke McMaster. A total of 218 patients with stroke were involved. Five RCTs reported statistically significant effects for motor recovery in favour of the experimental group, whereas four RCTs did not find significant differences. On average, the experimental group received daily 48.3 minutes of Robot-assisted therapy (RT) and the control group 29.0 minutes of Control Therapy (CT). An overall statistically non-significant (0.65, 95%CI: −0.02 to 1.33; Z = 1.90, P=0.06) heterogeneous SES (χ2 = 40.82, P<0.001) was found in favour of the robot-assisted therapy. (Figure 1 and Table 2a). As shown in Table 2a and Figure 1, observed heterogeneity between studies was mainly due to study of Hesse and colleagues who reported a larger individual effect size for bilateral distal arm training when compared to the other studies. Subsequent sensitivity analysis without the study of Hesse et al showed a homogeneous SES (χ2 = 4.35, P=0.60) in favour of shoulder-elbow arm robotics (0.36 SDU [fixed]; 95%CI: 0.05 to 0.65, Z = 2.32, P<0.026).

Figure 1
Meta-analysis of robot-assisted therapy trails on motor recovery

ADL

Five studies8,12,48,49,53 evaluated outcome with the FIM. A total of 139 patients with stroke were involved. None of the studies reported significant effects for ADL in favour of the experimental group. The study of Burgar et al, 2000 is not shown due to the inability to calculate an individual effect size. On average, the experimental group received daily 50.3 minutes of RT and the control group 23.4 minutes of CT. A homogeneous non-significant SES was found (χ2 =0.50, P> 0.05) for robot-assisted therapy (SES [fixed] 0.13 SDU; CI, −0.23 to 0.50, Z = 0.86, P>0.05) (Figure 2 and Table 2b).

Figure 2
Meta-analysis of robot-assisted therapy trails on ADL.

Discussion

The present review, involving 218 patients, shows a positive trend toward robot-assisted therapy for the proximal upper limb when compared to conventional treatment modalities with regard to motor recovery when measured with the FM assessment scale (FMA) or the arm and hand impairment part of the Chedoke McMaster Stroke Assessment Scale (CMSA). The lack of overall significance for all included studies on motor recovery was the result of the application of a random effects model (REM). This REM calculates a summary effect size (SES) that is based on the within and between study variance. As a consequence, larger 95% confidence intervals of the SES are obtained than a fixed effects model would generate. However, a sensitivity analysis showed that observed heterogeneity was mainly caused by the study of Hesse and colleagues19, who investigated the effects of Bi-Manu-Track robotic on distal elbow pro-supination and wrist flexion and extension activities in patients with stroke. By contrast, the other robots, such as MIT-manus and MIME robots, are designed to train proximal shoulder and elbow movements. This finding suggests that the observed effect size is dependent on proximal or distal arm robot training. Unfortunately, only three of the seven studies that measured FMA or CMSA score provided additional subscores for the proximal and distal arm components at baseline and at the end of the therapy. Two studies reported only the subscores of both components at the end of the therapy, whereas the other studies did not differentiate between these scores at all. Therefore, it remains uncertain whether the observed improved motor score is due to an improvement at the proximal shoulder-elbow level or at the distal hand-wrist level. In order to better understand what exactly patients learn by robotics when they improve in upper limb motor function, future studies should address this issue.1 The significant, moderate SES of upper arm robotics on motor control based on FMA and CMSA scales denotes a mean overall change of 7% to 8% in motor control of the upper limb in favour of the robot-assisted therapy. These effects were based on studies of high methodological quality (PEDro score = 6 ± 1.25 (mean ± SD)).

No significant improvements were observed on outcome of ADL measured by the FIM scale. It is common knowledge that the FIM (like the Barthel Index; BI) does not measure dexterity of the upper paretic limb properly.4 Therefore, scales that measure dexterity specifically, such as the Action Research Arm test, the Wolf Motor Function Test and Jebsen test or Nine-hole-peg test, are preferred. In addition, as was shown recently by Rohrer et al,44, and Kahn and colleagues51, future studies should also investigate kinematic changes to better understand the observed improvements in motor co-ordination.4 For this later purpose, longitudinal conducted studies with repeated measurements are needed to understand how synergistic-dependent movement patterns improve in patients during robotic assisted therapies. In particular, changes that occur in adaptive compensatory movements of the trunk should be also investigated.57 Moreover, it should be noted that measurements at a functional level can not differentiate between improvements at motor recovery level and improvements due to the use of alternative compensating strategies. Therefore, a better understanding of the relationship between the mechanisms of cortical reorganization and motor recovery is needed. In particular, understanding the bi-hemispheric plasticity after stroke will allow the development of new (robot-assisted) therapies to enhance learning-dependent neuroplasticity.17 While an initial small study by Lum and colleagues suggested that unilateral training is superior to bilateral training53, larger studies investigating whether bilateral robotic assisted training, with or without auditory or visual cueing and feedback that promotes interhemispheric activation of the limbs should be subject to investigation. It may have an added value to only unilateral training, particularly to the more impaired patients. New developments in robotic innovations and capabilities are still to be expected and most likely will expand its applicability to other or more specific motor functions.

Another important issue to elucidate is the impact of high intensity robot-assisted therapy on motor recovery after stroke. In studies with the MIT-MANUS robot, in addition to conventional therapy, patients received five hours per week of robot-assisted therapy, while the control group received only one hour of robot exposure.48,49,54 The number of movements generated in robot-assisted therapy is far higher than in other forms of therapy, such as Electric Stimulation (ES)50, free reaching51 and NeuroDevelopmental Therapy (NDT).8,12 It can be concluded that high intensity repetitive movements constitute an important contributor to the effectiveness of robot-assisted therapy. In fact studies that tried to match the intensity of robotic therapy to the number of movements generated by other forms of therapy failed to show a differential effect.51,52 In other words, robotic therapy had no particular advantage at low utilization, but it also did not hinder or halt recovery.

To examine whether the effectiveness of robot-assisted therapy is due to the treatment modality or to the high intensity of training, dose-response trials are required. It is important to understand that robotic therapy simply utilizes robots as vehicles to deliver highly repetitive therapy. There is no reason to assume that robots will lead to better results than human delivered movement therapy if there is a match of all variables. In practice the high intensity of training in patients receiving robotics in either the clinic in a classroom format and also in their own home environment will likely justify cost-effectiveness studies in which the purchase of a robotic device and support with a single therapist supervising multiple patients as demonstrated by Daly and colleagues10 is compared with the costs of intermittent, patient-tailored training by a therapist in a clinical setting. For the home environment, patients’ performances can be monitored and data collected remotely using robotic therapy in conjunction with broadband telematics. This development has - among other benefits - the potential of saving travelling expenses to and from a health care facility and increasing of independent training time. To date, high quality studies investigating the efficiency of robotics in relation to usual care are lacking in the literature. Such trials should incorporate a critical and comprehensive economic and effectiveness evaluation of hospital and community care, patient and family resources and other resources e.g. home help visits, and involve a cost-effectiveness analysis rather than a cost-minimization, cost-benefit or cost-utility analysis. To our knowledge, the only such a study presently in progress is being supported and run by the Veterans Affairs in the US involving four of its rehabilitation hospitals (PI: Albert Lo, Yale University and VA West Haven; CSP 558; West Haven, CT; Baltimore, MD; Gainesville, FL; Seattle, WA).

Finally, most studies were based on a small sample size and were heavily underpowered to reject H0 hypothesis. In particular, because the stroke population is heterogeneous at impairment level and treatment effects are relatively small when compared to observed differences in patterns of recovery due to spontaneous recovery55, stratification on the basis of prognostic baseline characteristics becomes necessary.2 For example, assuming that a 6 points difference constitutes the minimal clinically important difference (MCID) on outcome of ARAT56, at least 238 patients per arm are needed in a two arm RCT to maintain 80% statistical power in order to reject the H0 hypothesis. The large standard deviation of ARAT measurements, i.e. about 32 points at 26 weeks post stroke2, is mainly responsible for this large sample size, In contrast, when selecting only stroke subjects displaying some dexterity on the ARAT (i.e., ARAT>9 points), a sample standard deviation of 12 points is observed and only 31 patients in each arm will be needed to show a MCID of 6 points. As is the case in CIMT57, demonstrating a therapeutic effect is also a matter of appropriate selection of patients.4 To that effect it is important to note that patients with some probability for return of upper limb function in the first 5 weeks post stroke2 or those with some return of voluntary extension of wrist and fingers beyond the subacute stage after stroke are likely to be the most favorable candidates for robotic therapy because of the presence of some dexterity.4 By the same token, we expect that patients in the middle range of the FM–arm score (i.e., about 22 to 44 of maximal 66 points) may benefit most from robotic therapy.

The present review has a number of limitations. First, in this review we assumed that all studies used different patients, but because some studies were conducted at the same time and at the same place, we can not be certain that only unrelated study populations were used. Second, in the present study we pooled Fugl-Meyer arm motor scores58 with the scores from the physical impairment inventory of the arm of the Chedoke-McMaster Stroke Assessment scale59 in order to calculate one overall summary effect size for motor outcome of the upper paretic limb. The FMA includes 42 items to measure shoulder-elbow impairment and coordination and 24 items to measure the hand-wrist impairment.58 On the other hand, the CSMA contains 2 dimensions related with motor control of arm and hand each measured on a 7 point scale.59 In our opinion, pooling outcomes of both scales to generate one dimensionless SES is justified because both assessments are based on the six recovery stages of Brunnstrom. This suggests that both scales measure the same underlying construct (i.e., synergy-dependency of motor control). Third, we also pooled the robot-assisted interventions in order to obtain one overall effect size, even though different robots focussed on different parts of the upper limb function, different robot control strategies were employed (robots can be programmed to deliver different behaviours and as stated earlier we excluded comparison of different types of robot-assisted therapy), and some protocols focused on sub-acute phase of stroke recovery while other studies focused on the chronic phase. For the same reason we were unable to differentiate between the control interventions. Different types of control interventions, such as NDT and ES which have proven their effectiveness only at impairment level, made it difficult to interpret the effectiveness of robot-assisted therapy. These shortcomings emphasize the need for more high-quality RCTs. In the present review we did pool all motor outcomes of the upper limb, irrespective whether they were measured with the FMA or the Chedoke Assesment scale. We assumed that this was appropriate as both scales are based on the same motor stages of Brunnstrom.59 Finally, only studies written in the English, German or Dutch language were included. We may have inadvertently missed relevant studies that were not published in scientific journals or in other languages.

In summary, the present systematic review confirms the potential for robotic assisted devices to elicit improvements in proximal upper limb function. However, improvements in terms of ADL could not be substantiated. Unfortunately, administered ADL scales such as FIM and BI do not reflect recovery of the paretic upper limb properly. Future research on the effects of robot-assisted therapy should focus on kinematic analysis60 in order to differentiate between recovery by neural repair and recovery based on compensation strategies.4,60 In addition, trials should use valid instruments that measure upper limb skills specifically, such as Action Research Arm Test (ARAT) or Wolf Motor Function Test (WFMT). Finally, the cost-effectiveness of robotics needs to be investigated. This latter is particularly important to be clarified because of the increasing pressure experienced by health care professionals in most countries to reduce health care costs. In addition, robotics may offer stroke patients an opportunity to train independently in an intensive functional fashion and at home. This becomes increasingly important at a time when a lack of time is reported to be the major barrier for therapists not to comply with evidenced based guidelines for stroke rehabilitation.61, 62

Acknowledgments

We would like to thank students Jan Hoogendoorn, Vincent Groen and Mario Kramer of the Faculty of Human Movement Science of VU University of Amsterdam for their participation in this research synthesis in collecting and reviewing papers. Dr. Krebs is supported by the NYS DOH Center for Research Excellence in Spinal Cord Injury, and the US Public Health Service (NIH R01-HD045343, Veterans Affairs Baltimore B3688R and B3607R). Dr. H.I. Krebs is a co-inventor in the MIT-held patents for the robotic devices. He holds equity positions in Interactive Motion Technologies, Inc., the company that manufactures this type of technology under license to MIT.

Appendix 1

Application of Methodological Criteria Investigating the Effects of Robot Therapy

Reference1234567891011Total
Score
Co-
intervention
Aisen, 1997010110011117No
Burgar, 2000010100110116No
Fasoli, 2004110110111118Yes
Hesse, 2005110000111116No
Kahn, 2000010000001114No
Lum, 2002010100110116No
Volpe, 2000110110110117Yes
Daly, 2005110100110116No
Kahn, 2006110000100114No
Lum, 2006110100110116No

Appendix 2

An external file that holds a picture, illustration, etc.
Object name is nihms107217f3.jpg

Flow diagram

Footnotes

1Of notice, one of us (Krebs) is running an NIH/NCMRR sponsored robot trial at the Burke Rehabilitation Hospital, White Plains, NY enrolling 200 persons with chronic impairment due to stroke to determine the influence of proximal and distal components of training and the preferred order of training (160 robot trained subjects and 40 controls).

References

1. Muntner P, Garrett E, Klag MJ, Coresh J. Trends in Stroke Prevalence between 1973 and 1991 in the US Population 25 to 74 years of age. Stroke. 2002;33:1209–1213. [PubMed]
2. Kwakkel G, Kollen BJ, van der Grond J, Prevo AJ. Probability of regaining dexterity in the flaccid upper limb: impact of severity of paresis and time since onset in acute stroke. Stroke. 2003;34:2181–6. [PubMed]
3. Van Peppen RP, Kwakkel G, Wood-Dauphinee S, Hendriks HJ, Van der Wees PJ, Dekker J. The impact of physical therapy on functional outcomes after stroke: what’s the evidence? Clin Rehabil. 2004;18:833–62. [PubMed]
4. Kwakkel G, Kollen BJ, Lindeman E. Understanding the pattern of functional recovery after stroke: Facts and theories. Restorative Neurology and Neuroscience. 2004;22:281–299. [PubMed]
5. Krebs HI, Hogan N, Aisen ML, Volpe BT. Robot-aided neurorehabilitation. IEEE Trans Rehabil Eng. 1998;6:75–87. [PMC free article] [PubMed]
6. Krebs HI, Volpe BT, Aisen ML, Hogan N. Increasing productivity and quality of care: robotic-aided neurorehabilitation. J Rehabil Res Dev. 2000;37:639–652. [PubMed]
7. Reinkensmeyer DJ, Kahn LE, Averbuch M, McKenna-Cole AN, Schmit BD, Rymer WZ. Understanding and treating arm movement impairment after chronic brain injury: progress with the ARM Guide. J Rehabil Res Dev. 2000;37:653–662. [PubMed]
8. Burgar CG, Lum PS, Shor PC, Van der Loos HFM. Development of robots for rehabilitation therapy: the Palo Alto VA/Stanford experience. J Rehabil Res Dev. 2000;37:663–673. [PubMed]
9. Lum PS, Burgar CG, Shor PC. Evidence for improved muscle activation patterns after retraining of reaching movements with the MIME robotic system in subjects with post-stroke hemiparesis. IEEE Trans Neural Syst Rehabil En. 2004;12:186–194. [PubMed]
10. Daly JJ, Hogan N, Perepezko EM, Krebs HI, Rogers JM, Goyal KS, Dohring ME, Fredrickson E, Nethery J, Ruff RL. Response to upper-limb robotics and functional neuromuscular stimulation following stroke. Journal of rehabilitation research & development. 2005;42(6):723–736. [PubMed]
11. Basmajian JV, Gowland CA, Finlayson AJ, Hall AL, Swanson LR, Stratford PW. Stroke treatment: comparison of integrated behavioural physical therapy vs. traditional physical therapy programs. Arch Phys Med Rehabil. 1987;68:276–272. [PubMed]
12. Lum PS, Burgar CG, Shor PC, Majmundar M, Van der Loos M. Robot-assisted movement training compared with conventional therapy techniques for the rehabilitation of upper-limb motor function after stroke. Arch Phys Med Rehabil. 2002;83(7):952–959. [PubMed]
13. Logigian MK, Samuels MA, Falconer J, Zagar R. Clinical exercise trial for stroke subjects. Arch Phys Med Rehabil. 1983;64:364–367. [PubMed]
14. Wagenaar RC, Meijer OG, van Wieringen P, Kuik DJ, Hazenberg GL, Lindeboom J. The functional recovery of stroke: a comparison between neuro-developmental treatment and the Brunnstrom method. Scand J Rehabil Med. 1990;22:1–8. [PubMed]
15. Wolf SL, LeCraw DE, Barton LA. Comparison of motor copy and targeted biofeedback training techniques for restitution of upper extremity function among subjects with neurologic disorders. Phys Ther. 1989;69(9):719–735. [PubMed]
16. Butefisch C, Humelsheim H, Denzler P, Mauritz KH. Repetitive training of isolated movements improves the outcome of motor rehabilitation of the centrally paretic hand. J Neurol Sci. 1995;130:56–68. [PubMed]
17. Foley NC, Bhogal SK, Teasell RW, Bureau Y, Speechley MR. Estimates of quality and reliability with the physiotherapy evidence-based database scale to assess the methodology of randomized controlled trials of pharmacological and nonpharmacological interventions. Phys Ther. 2006;86(6):817–24. [PubMed]
18. Dobkin BH. Strategies for stroke rehabilitation. Lancet Neurol. 2004;3:528–526. [PubMed]
19. Hesse S, Schmidt H, Werner C, Bardeleben A. Upper and lower extremity robotic devices for rehabilitation and for studying motor control. Curr Opin Neurol. 2003;16:705–710. [PubMed]
20. Hidler J, Nichols D, Pelliccio M, Brady K. Advances in the understanding and treatment of stroke impairment using robotic devices. Top Stroke Rehabil. 2005;12:22–33. [PubMed]
21. Hogan N, Krebs HI. Interactive robots for neuro-rehabilitation. Restor Neurol Neuorsci. 2004;22:349–358. [PubMed]
22. Lum P, Reinkensmeyer D, Mahoney R, Rymer WZ, Burgar C. Robot devices for movement therapy after stroke: current status and challenge to clinical acceptance. Top Stroke Rehabil. 2002;8:40–53. [PubMed]
23. Platz T. Evidence-based arm rehabilitation. Nervenarzt. 2003;74:841–849. [PubMed]
24. Riener R, Nef T, Colombo G. Robot-aided neurorehabilitation of the upper extremities. Med Biol Eng Comput. 2005;43:2–10. [PubMed]
25. Volpe BT, Krebs HI, Hogan N. Is robot-aided sensorimotor training in stroke rehabilitation a realistic option? . Curr Opin Neurol. 2001;14:745–752. [PubMed]
26. Brewer BR, Fagan M, Klatzky RL, Matsuoka Y. Perceptual limits for a robotic rehabilitation environment using visual feedback distortion. IEEE Trans Neural Syst Rehabil Eng. 2005;13(1):1–11. [PubMed]
27. Colombo R, Pisano M, Micere S, Mazzone A, Delconte C, Carrozza C, Dario P, Minuco G. Robotic techniques for upper limb evaluation and rehabilitation of stroke patients. IEEE Trans Neural Syst Rehabil Eng. 2005;13(3):311–324. [PubMed]
28. Cozens JA. Robotic Assistance of an active upper limb exercise in neurologically impaired patiens. IEEE Trans Rehabil Eng. 1999;7:254–256. [PubMed]
29. Dipietro L, Ferraro M, Palazzolo JJ, Krebs HI, Volpe BT, Hogan N. Customized interactive robotic treatment for stroke: EMG-triggered therapy. IEEE Trans Neural Syst Rehabil Eng. 2005;13:325–334. [PMC free article] [PubMed]
30. Fasoli SE, Krebs HI, Stein J, Frontera WR, Hogan N. Effects of robotic therapy on motor impairment and recovery in chronic stroke. Arch Phys Med Rehabil. 2003;84:477–482. [PubMed]
31. Fasoli SE, Krebs HI, Stein J, Frontera WR, Hughes R, Hogan N. Robotic therapy for chronic motor impairments after stroke: follow-up results. Arch Phys Med Rehabil. 2004;85:1106–1111. [PubMed]
32. Ferraro M, Palazzolo JJ, Krol J, Krebs HI, Hogan N, Volpe BT. Robot-aided sensorimotor arm training improves outcome in patients with chronic stroke. Neurology. 2003;61:1604–1607. [PubMed]
33. Finley MA, Fasoli SE, Dipietro L, Ohlhoff J, MacClellan L, Meister C, Whitall J, Macko R, Bever CT, Krebs HI, Hogan N. Short-duration robotic therapy in stroke patients with severe upper-limb motor impairment. Journal of rehabilitation research and development. 2005;42:683–692. [PubMed]
34. Johnson MJ, Van der Loos HF, Burgar CG, Shor P, Leifer LJ. Experimental results using force-feedback cueing in robot-assisted stroke therapy. IEEE Trans Neural Syst Rehabil Eng. 2005;13:335–348. [PubMed]
35. Krebs HI, Ferraro M, Buerger SP, Newbery MJ, Makiyama A, Sandmann M, Lynch D, Volpe BT, Hogan N. Rehabilitation robotics: pilot trial of a spatial extension for MIT-Manus. J Neuroengineering Rehabil. 2004;1:5. [PMC free article] [PubMed]
36. Krebs HI, Aisen ML, Volpe BT, Hogan N. Quantization of continuous arm movements in humans with brain injury. Proc Natl Acad Sci USA. 1999;99:4645–4649. [PMC free article] [PubMed]
37. Krebs HI, Palazzolo JJ, Dipietro L, Ferraro M, Krol J, Rannekleiv K, Volpe BT, Hogan N. Rehabilitation robotic: performance-based progressive robot-assisted therapy. Auton Robot. 2003;15:7–20.
38. MacClellan LR, Bradham DD, Whitall J, Volpe B, Wilson PD, Ohlhoff J, Meister C, Hogan N, Krebs HI, Bever CT., Jr Robotic upper-limb neurorehabilitation in chronic stroke patients. Journal of rehabilitation research & development. 2005;42:717–722. [PubMed]
39. Matsuoka Y, Allin SJ, Klatzky RL. The tolerance for visual feedback distortions in a virtal environment. Physiol Behav. 2002;77:651–655. [PubMed]
40. Micera S, Carozza MC, Guglielmelli E, Cappiello G, Zaccone F, Freschi C, Colombo R, Mazzone A, delconte C, Pisano F, Minuco G, Dario P. A simple robotic system for neurorehabilitation. Auton Robots. 2005;19:271–284.
41. Patton JL, Stoykov M, Kovic M. Evaluation of robotic training forces that either enhance or reduce error in chronic hemiparetic stroke survivors. Exp Brain Res. 2005;168:368–383. [PubMed]
42. Reikensmeyer DJ, Schmit BD, Rymer WZ. Assessment of active and passive restraint during guided reaching after chronic brain injury. Ann Biomed Eng. 1999;27:805–814. [PubMed]
43. Reinkensmeyer DJ, Pang CT, Nessler JA, Painter CC. Web-based telerehabilitation for the upper extremity after stroke. IEEE Trans Neural Syst Rehabil Eng. 2002;10:102–108. [PubMed]
44. Rohrer B, Fasoli S, Krebs HI, Hughes R, Volpe B, Frontera WR, Stein J, Hogan N. Movement smoothness changes during stroke recovery. J Neurosci. 2002;22:8297–8304. [PubMed]
45. Stein J, Krebs HI, Frontera WR, Fasoli SE, Hughes R, Hogan N. Comparison of two techniques of robot-aided upper limb exercise training after stroke. Am J Phys Med Rehabil. 2004;85:1106–1111. [PubMed]
46. Takahashi CD, Reinkensmeyer DJ. Hemiparetic stroke impairs anticipatory control of arm movement. Exp Brain Res. 2003;149:131–140. [PubMed]
47. Volpe BT, Krebs HI, Hogan N, Edelsteinn L, Diels CM, Aisen ML. Robot training enhanced motor outcome in patients with stroke maintained over 3 years. Neurology. 1999;53:1874–1876. [PubMed]
48. Aisen ML, Krebs HI, Hogan N, McDowel F, Volpe BT. The effect of robot-assisted therapy and rehabilitative training on motor recovery following stroke. Arch Neurol. 1997;54:443–446. [PubMed]
49. Fasoli SE, Krebs HI, Ferraro M, Hogan N, Volpe BT. Does shorter rehabilitation limit potential recovery poststroke? . Neurorehabil Neural Repair. 2004;18:88–94. [PubMed]
50. Hesse S, Werner C, Pohl M, Rueckriem S, Mehrhoz J, Lingnau ML. Computerized arm training improves the motor control of the severely affected arm after stoke. Stroke. 2005;36:1960–1966. [PubMed]
51. Kahn LE, Averbuch M, Rymer WZ, Reinkensmeyer J. Integration of Assistive Technology in the Information Age. Amsterdam: IOS Press; 2001. Comparison of robot-assisted reaching to free reaching in promoting recovery from chronic stroke; pp. 39–44.
52. Kahn LE, Zygman ML, Rymer WZ, Reinkensmeyer DJ. Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: a randomized controlled pilot study. Journal of NeuroEngineering and Rehabilitation. 2006;3:1–13. [PMC free article] [PubMed]
53. Lum PS, Burgar CG, Van der Loos M, Shor PC, Majmumdar M, Yap R. MIME robotic device for upper-limb neurorehabilitation in subacute stroke subjects: A follow-up study. Journal of rehabilitation research & development. 2006;43:631–642. [PubMed]
54. Volpe BT, Krebs HI, Hogan N, Edelstein L, Diels C, Aisen ML. A novel approach to stroke rehabilitation: robot-aided sensorimotor stimulation. Neurology. 2000;54:1938–1944. [PubMed]
55. Kwakkel G, Kollen B, Twisk J. Impact of time on improvement of outcome after stroke. Stroke. 2006;37:2348–53. [PubMed]
56. van der Lee JH, Beckerman H, Lankhorst GJ, Bouter LM. The responsiveness of the Action Research Arm test and the Fugl-Meyer Assessment scale in chronic stroke patients. J Rehab Med. 2001;33:110–113. [PubMed]
57. Wolf SL, Winstein CJ, Miller JP, Taub E, Uswatte G, Morris D, Giuliani C, Light KE, Nichols-Larsen D. EXCITE Investigators. Effect of constraint-induced movement therapy on upper extremity function 3 to 9 months after stroke: the EXCITE randomized clinical trial. JAMA. 2006;296:2095–104. [PubMed]
58. Sanford J, Moreland J, Swanson LR, Stratford PW, Gowland C. Reliability of Fugl-Meyer assessment for testing motor performance in patients following stroke. Phys Ther. 1993;73:447–454. [PubMed]
59. Gowland C, Stratford P, Ward M, Moreland J, Torresin W, Van Hullenaar S, Sanford J, Barreca S, Vanspall B, Plews N. Measuring physical impairment and disability with the Chedoke-McMaster Stroke Assessment. Stroke. 1993;24:58–63. [PubMed]
60. Cirstea MC, Levin MF. Compensatory strategies for reaching in stroke. Brain. 2000;123:940–53. [PubMed]
61. Pollock AS, Legg L, Langhorne P, Sellars C. Barriers to achieving evidence-based stroke rehabilitation. Clin Rehabil. 2000;14:611–7. [PubMed]
62. Kwakkel G. Impact of intensity of practice after stroke: issues for consideration. Disabil Rehabil. 2006;28:823–30. [PubMed]
PubReader format: click here to try

Formats:

Related citations in PubMed

See reviews...See all...

Cited by other articles in PMC

See all...

Links

  • MedGen
    MedGen
    Related information in MedGen
  • PubMed
    PubMed
    PubMed citations for these articles

Recent Activity

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

See more...