Robotic Devices as Therapeutic and Diagnostic Tools for Stroke RecoveryRobotic Devices as Tools for Stroke Recovery FREE

Attempts to improve recovery after stroke have quickened because the prevalence and incidence of those with stroke disability has increased, driven by an aging population and improved survival after the initial injury. The phenotype of the poststroke condition is based on the neurological deficit that comprises a complex interaction of cognitive and sensorimotor impairments and that depends on the size and location of the brain injury. Standard treatments for the sensorimotor impairment focus, in part, on teaching patients to use the so-called unaffected limbs to adapt, compensate, and, especially, improve motor abilities with respect to feeding, grooming, and toileting. Lower extremity function, in particular walking a few steps, even with a prosthesis or assistance, shows more reliable improvement than upper extremity function. Treatment of the affected upper limb is a tedious and difficult process that occurs in short episodes and concentrates on passive movement, especially for patients with moderate to severe stroke. As it turns out, passive movement is best suited for maintaining joint integrity.¹ Although there is compelling evidence that specialized stroke recovery units provide care that leads to decreased mortality and morbidity, there is less agreement on which particular treatment program is superior, or even that significant recovery beyond standard treatment is possible. Most change from the acute state occurs within weeks, but smaller incremental improvement may occur later and has been shown to result from additional intervention with a variety of protocols and devices.

The extent of the functional recovery occurring after training with robotic devices needs to stand the test of multicenter randomized control trials.² Nevertheless, the available literature on robotic studies demonstrates clear incremental reductions of motor impairment that offer the opportunity to build a better outcome.^{3 - 4} Furthermore, when looking at the ensemble of robotic studies, several issues come to the fore. Intensive treatment protocols for sensorimotor impairment have demonstrated benefit compared with standard care.^{5 - 7} Motor learning principles suggest that to optimize the outcome of motor training, distributed training (timing and session scheduling given over longer intervals) will be more effective than massed training (scheduled within narrow time windows). Incidentally, distributed training also guarantees that an adequate challenge occurs throughout treatment, thus maintaining task interest.^{8 - 9} Robots are tireless agents that produce reliable, highly reproducible control of movement sequences and, thus, act as tools to lighten the workload of intensive training protocols. Another issue is whether clinical scales adequately capture the variability that occurs among patients with similar brain lesions. Robotic devices can measure the kinematics and dynamics of movement performance objectively and, coupled with neuroimaging methods that capture brain blood flow or metabolic activity,^{10 - 15} will provide a richer description of stroke phenotype. A more objective phenotype will become more important as we understand the genotypic differences that influence recovery.¹⁶ A final issue is whether standard rehabilitation techniques should include robotic training in combination with electromyographic triggering, transcranial direct current stimulation (tDCS), transcranial magnetic stimulation, and pharmacological sensitization.^{17 - 22}

Some time ago, a group of engineers and clinicians met to discuss the range of robotic devices that could potentially be used in stroke recovery (Figure 1). This led to the first robotic treatments of patients; some occurred within weeks, others occurred within months of stroke, in a rehabilitation setting, to measure whether robotic devices would improve recovery.^{24 ,26 ,28 - 32} The trials were encouraging and, although the numbers of patients were relatively small, a recent meta-analysis demonstrated significant improvement for those trained with devices that targeted movement of the shoulders and elbows.³ Training with devices that targeted the wrists and hands, and the translation into “functional abilities,” was less convincing.³

In our restorative neurology center at the Burke-Cornell Medical Research Institute, the treatment with robotic devices has included patients within days of their stroke, together with patients with chronic stroke (greater than 6 months from injury). Our ongoing results suggest that robotic training influences motor learning, a notion that is strengthened by the very rapid improvement that occurs at the treatment's onset and, also, by the sustained improvement the patients exhibit even months after the training has ended. For example, we analyzed the performance of 248 patients (mean age, 62.3 years; range, 17-89 years; 5 days-11.3 years after stroke) who participated in robotic training for at least 18 sessions. The impairment level of the upper extremity was estimated by the Fugl-Meyer Motor (FM) scale (a reliable and standard clinical scale of movement performance; maximum score, 66; lower scores indicate more severe impairment) and the scores were used to generate cumulative probability distributions (CPDs) for the different points of the training (Figure 2A). On admission to the treatment, the patients' degree of motor impairments spanned nearly the complete range of the upper extremity FM scale (range, 0-54). Inspection of the CPDs at the 0.5 level (Figure 2A) revealed an improvement of 5 points between the admission and midpoint evaluation. Crucially, a 3-point improvement in the FM scale score has been shown to have a significant impact on disability.³⁴ Still at the 0.5 level, the CPDs showed little change from midpoint to treatment discharge, but the gain was maintained in the 3-month follow-up. Examining the CPDs at the 0.75 level (Figure 2A) revealed a 3-point improvement from admission to midpoint evaluation and yet a further improvement by discharge and again at follow-up. Remarkably, the total improvement from admission to follow-up was 9 points. The timing and degree of motor changes in response to robotic training may thus be instructive. Shorter robotic training periods might be productive for patients with lower admission FM scale scores and greater upper extremity impairment. Conversely, longer training periods resulted in stepwise improvements that continued after the training had ended for those with less severe impairment (higher admission scores). For some patients with chronic stroke, these overall results suggest that a plateau performance during standard therapy may belie a reserve brain recovery potential. Whether bursts of improvement, as demonstrated by some of the patients who improved after the robotic training ended, depend on some critical level of performance that effectively incorporates the affected limb more often in natural circumstances remains to be determined.

Many investigators worry about the subjective nature of the FM scale, and the field of restorative neurology desperately needs objective outcome data. Among the objective parameters recorded by robotic devices, we offer submovements as a reasonable candidate that captures velocity and position information and represents an essential building block of motor performance. The robotic devices record speed and position constantly, and in this manner, they provide a longitudinal kinematic performance history. In a robotic study of stroke patients (N = 47, all greater than 6 months after stroke), the submovement profiles were extracted (Figure 2B) from unassisted movements performed by each patient treated with interactive robotic devices. By discharge, the submovements grew taller and longer and became less numerous (Figure 2B). This submovement analysis was commensurate with improved task performance and suggested that the form of the movement contributed importantly to the function¹⁰ (Figure 2B, right panels). Improving smoothness of movement may begin to define objective criteria with which to track recovery or effectiveness of new treatment. The outliers who responded poorly, or not at all, suggest that a specific treatment approach, whether robotic treatment or standard therapy, might be better tailored to detailed movement failures. It would also be very useful to include correlative neuroimaging information.

In a recent study, we treated patients with chronic stroke (N = 6; on average 4.7 years from stroke) with tDCS (anode over the affected hemisphere, 1 mA) for 20 minutes prior to robotic training. A motor evoked potential (by transcranial magnetic stimulation) in the flexor carpi radialis of the affected limb remained facilitated for the entire treatment³⁴ (Figure 2C). Although a recent pilot experiment that exposed stroke patients (4-8 weeks of stroke) to robotic training (20-minute sessions for 30 trials) and tDCS (1.5 mA for the initial 7 minutes of training) was not successful,²² further studies are needed to determine whether prior tDCS, or simultaneous tCDS of longer duration, may have an effect on outcome.

This selected glimpse into the confluence of bioengineering and restorative neurology suggests that the opportunity to reduce impairment lasts longer than formerly thought. It is also obvious that therapists should consider arming themselves with some new tools. Robotic devices create the possibility for objective kinematic and dynamic metrics in a view of stroke recovery that includes the measurement of form and structure of movement. These measurements, in turn, can render a richer and more complete phenotype for stroke recovery.

Correspondence:Bruce T. Volpe, MD, Department of Neurology and Neuroscience, Weill Medical College of Cornell University, 785 Mamaroneck Ave, White Plains, NY 10605 (btv3@cornell.edu).

Accepted for Publication:March 20, 2009.

Author Contributions:Study concept and design: Volpe, Huerta, Edwards, Hogan, and Krebs. Acquisition of data: Volpe, Zipse, Rykman, Hogan, and Krebs. Analysis and interpretation of data: Volpe, Huerta, Rykman, Dipietro, Hogan, and Krebs. Drafting of the manuscript: Volpe, Huerta, Zipse, and Edwards. Critical revision of the manuscript for important intellectual content: Volpe, Huerta, Rykman, Dipietro, Hogan, and Krebs. Statistical analysis: Volpe, Huerta, and Dipietro. Obtained funding: Volpe, Huerta, and Hogan. Administrative, technical, and material support: Huerta, Zipse, Rykman, Edwards, Dipietro, and Hogan. Study supervision: Volpe, Hogan, and Krebs.

Financial Disclosure:Drs Hogan and Krebs are coinventors of the Massachusetts Institute of Technology–held patent for the robotic devices used to treat patients in this work. They hold equity positions in Interactive Motion Technologies, Inc, a company that manufactures this type of technology under license to the Massachusetts Institute of Technology.

Funding/Support:This work was supported by National Institutes of Health grant HD043343, Skirball Foundation, and Burke Medical Research Institute.