Laura Dipietro

Rehabilitation Robotics: Performance-Based Progressive Robot-Assisted Therapy

In this paper we describe the novel concept of performance-based progressive robot therapy that uses speed, time, or EMG thresholds to initiate robot assistance. We pioneered the clinical application of robot-assisted therapy focusing on stroke—the largest cause of disability in the US. We have completed several clinical studies involving well over 200 stroke patients. Research to date has shown that repetitive task-specific, goal-directed, robot-assisted therapy is effective in reducing motor impairments in the affected arm after stroke. One research goal is to determine the optimal therapy tailored to each stroke patient that will maximize his/her recovery. A proposed method to achieve this goal is a novel performance-based impedance control algorithm, which is triggered via speed, time, or EMG. While it is too early to determine the effectiveness of the algorithm, therapists have already noted one very strong benefit, a significant reduction in arm tone.

A Survey of Glove-Based Systems and Their Applications

Hand movement data acquisition is used in many engineering applications ranging from the analysis of gestures to the biomedical sciences. Glove-based systems represent one of the most important efforts aimed at acquiring hand movement data. While they have been around for over three decades, they keep attracting the interest of researchers from increasingly diverse fields. This paper surveys such glove systems and their applications. It also analyzes the characteristics of the devices, provides a road map of the evolution of the technology, and discusses limitations of current technology and trends at the frontiers of research. A foremost goal of this paper is to provide readers who are new to the area with a basis for understanding glove systems technology and how it can be applied, while offering specialists an updated picture of the breadth of applications in several engineering and biomedical sciences areas.

Motions or muscles? Some behavioral factors underlying robotic assistance of motor recovery

Robotics and related technologies have begun to realize their promise to improve the delivery of rehabilitation therapy. However, the mechanism by which they enhance recovery remains unclear. Ultimately, recovery depends on biology, yet the details of the recovery process remain largely unknown; a deeper understanding is important to accelerate refinements of robotic therapy or suggest new approaches. Fortunately, robots provide an excellent instrument platform from which to study recovery at the behavioral level. This article reviews some initial insights about the process of upper-limb behavioral recovery that have emerged from our work. Evidence to date suggests that the form of therapy may be more important than its intensity: muscle strengthening offers no advantage over movement training. Passive movement is insufficient; active participation is required. Progressive training based on measures of movement coordination yields substantially improved outcomes. Together these results indicate that movement coordination rather than muscle activation may be the most appropriate focus for robotic therapy.

Customized interactive robotic treatment for stroke: EMG-triggered therapy

A system for electromyographic (EMG) triggering of robot-assisted therapy (dubbed the EMG game) for stroke patients is presented. The onset of a patients attempt to move is detected by monitoring EMG in selected muscles, whereupon the robot assists her or him to perform point-to-point movements in a horizontal plane. Besides delivering customized robot-assisted therapy, the system can record signals that may be useful to better understand the process of recovery from stroke. Preliminary experiments aimed at testing the proposed system and gaining insight into the potential of EMG-triggered, robot-assisted therapy are reported.

Kinematic robot-based evaluation scales and clinical counterparts to measure upper limb motor performance in patients with chronic stroke.

Background. Human-administered clinical scales are the accepted standard for quantifying motor performance of stroke subjects. Although they are widely accepted, these measurement tools are limited by interrater and intrarater reliability and are time-consuming to apply. In contrast, robot-based measures are highly repeatable, have high resolution, and could potentially reduce assessment time. Although robotic and other objective metrics have proliferated in the literature, they are not as well established as clinical scales and their relationship to clinical scales is mostly unknown. Objective. To test the performance of linear regression models to estimate clinical scores for the upper extremity from systematic robot-based metrics. Methods. Twenty kinematic and kinetic metrics were derived from movement data recorded with the shoulder-and-elbow InMotion2 robot (Interactive Motion Technologies, Inc), a commercial version of the MIT-Manus. Kinematic metrics were aggregated into macro-metrics and micro-metrics and collected from 111 chronic stroke subjects. Multiple linear regression models were developed to calculate Fugl-Meyer Assessment, Motor Status Score, Motor Power, and Modified Ashworth Scale from these robot-based metrics. Results. Best performance—complexity trade-off was achieved by the Motor Status Score model with 8 kinematic macro-metrics (R = .71 for training; R = .72 for validation). Models including kinematic micro-metrics did not achieve significantly higher performance. Performances of the Modified Ashworth Scale models were consistently low (R = .35-.42 for training; R = .08-.17 for validation). Conclusions. The authors identified a set of kinetic and kinematic macro-metrics that may be used for fast outcome evaluations. These metrics represent a first step toward the development of unified, automated measures of therapy outcome.

A paradigm shift for rehabilitation robotics

Therapeutic robots enhance clinician productivity in facilitating patient recovery. In this article, we presented an overview of the remarkable growth in the activities in the area of therapeutic robotics and of experiences with our devices. We briefly review the published clinical literature in this emerging field and our initial clinical results in stroke. However, we also report our initial efforts that go beyond stroke, broadening the potential population that might benefit from this class of technology by discussing case studies of applications to other neurological diseases. We will also highlight the underexploited potential of this technology as an evaluation tool.

Submovement changes characterize generalization of motor recovery after stroke

Submovements are hypothesized to be discrete building blocks of human movement. Changes in their parameters appear to account for features observed in processes of motor learning and motor recovery from stroke. Our previous studies analyzed submovement changes in subjects recovering from stroke. Subjects were trained on point-to-point movements with the assistance of a rehabilitation robot as part of a stroke treatment protocol. Results suggested that recovery starts first by regaining the ability to generate submovements and then, over a longer time period, by reacquiring the means to combine submovements. Over recovery submovements became fewer, longer, and faster and such changes contributed to changes in movement smoothness. Taken together these results lent support to the theory that movement is produced via centrally generated submovements and that changes in submovements characterize recovery. More recently, we investigated generalization of training. We found that stroke subjects trained on point-to-point movements became progressively better able to draw circles, a task on which they had received no training. The goal of this paper was to further investigate the changes that occur in untrained movements during motor recovery from stroke. Specifically we wanted to test whether changes in smoothness and submovements also characterize untrained movements. We analyzed circle drawing movements performed by 47 chronic stroke subjects who underwent training on point-to-point movements over an 18-session robot-assisted therapy program. We found that during recovery the shapes drawn by subjects became not only closer to circles (a task not trained during therapy) but also smoother. Concurrently, submovements grew fewer, longer, and faster. These results are consistent with the theory that movement is produced via submovements and suggest that changes in smoothness and submovements might characterize and describe the process of motor recovery from stroke. Also, they are consistent with the idea that motor recovery after a stroke shares similar traits with motor learning.

Robotic devices as therapeutic and diagnostic tools for stroke recovery.

The understanding that recovery of brain function after stroke is imperfect has prompted decades of effort to engender speedier and better recovery through environmental manipulation. Clinical evidence has shown that the performance plateau exhibited by patients with chronic stroke, usually signaling an end of standard rehabilitation, might represent a period of consolidation rather than a performance optimum. These results highlight the difficulty of translating pertinent neurological data into pragmatic changes in clinical programs. This opinion piece focuses on upper limb impairment reduction after robotic training. We propose that robotic devices be considered as novel tools that might be used alone or in combination with novel pharmacology and other bioengineered devices. Additionally, robotic devices can measure motor performance objectively and will contribute to a detailed phenotype of stroke recovery.

Short-duration robotic therapy in stroke patients with severe upper-limb motor impairment

Chronic motor deficits in the upper limb (UL) are a major contributor to disability following stroke. This study investigated the effect of short-duration robot-assisted therapy on motor impairment, as measured by clinical scales and robot-derived performance measures in patients with chronic, severe UL impairments after stroke. As part of a larger study, 15 individuals with chronic, severe UL paresis (Fugl-Meyer < 15) after stroke (minimum 6 mo postonset) performed 18 sessions of robot-assisted UL rehabilitation that consisted of goal-directed planar reaching tasks over a period of 3 weeks. Outcome measures included the Fugl-Meyer Assessment, the Motor Power Assessment, the Wolf Motor Function Test, the Stroke Impact Scale, and five robot-derived measures that reflect motor control (aiming error, mean speed, peak speed, mean:peak speed ratio, and movement duration). Robot-assisted training produced statistically significant improvements from baseline to posttreatment in the Fugl-Meyer and Motor Power Assessment scores and the quality of motion (quantified by a reduction in aiming error and movement duration with an increase in mean speed and mean:peak speed ratio). Our findings indicate that robot-assisted UL rehabilitation can reduce UL impairment and improve motor control in patients with severe UL paresis from chronic stroke.

Robotic Measurement of Arm Movements After Stroke Establishes Biomarkers of Motor Recovery

Background and Purpose— Because robotic devices record the kinematics and kinetics of human movements with high resolution, we hypothesized that robotic measures collected longitudinally in patients after stroke would bear a significant relationship to standard clinical outcome measures and, therefore, might provide superior biomarkers. Methods— In patients with moderate-to-severe acute ischemic stroke, we used clinical scales and robotic devices to measure arm movement 7, 14, 21, 30, and 90 days after the event at 2 clinical sites. The robots are interactive devices that measure speed, position, and force so that calculated kinematic and kinetic parameters could be compared with clinical assessments. Results— Among 208 patients, robotic measures predicted well the clinical measures (cross-validated R2 of modified Rankin scale=0.60; National Institutes of Health Stroke Scale=0.63; Fugl-Meyer=0.73; Motor Power=0.75). When suitably scaled and combined by an artificial neural network, the robotic measures demonstrated greater sensitivity in measuring the recovery of patients from day 7 to day 90 (increased standardized effect=1.47). Conclusions— These results demonstrate that robotic measures of motor performance will more than adequately capture outcome, and the altered effect size will reduce the required sample size. Reducing sample size will likely improve study efficiency.