Clinical application of a modular ankle robot for stroke rehabilitation

Abstract

Background: Advances in our understanding of neuroplasticity and motor learning post-stroke are now being leveraged with the use of robotics technology to enhance physical rehabilitation strategies. Major advances have been made with upper extremity robotics, which have been tested for efficacy in multi-site trials across the subacute and chronic phases of stroke. In contrast, use of lower extremity robotics to promote locomotor re-learning has been more recent and presents unique challenges by virtue of the complex multi-segmental mechanics of gait.

Objectives: Here we review a programmatic effort to develop and apply the concept of joint-specific modular robotics to the paretic ankle as a means to improve underlying impairments in distal motor control that may have a significant impact on gait biomechanics and balance.

Methods: An impedance controlled ankle robot module (anklebot) is described as a platform to test the idea that a modular approach can be used to modify training and measure the time profile of treatment response.

Results: Pilot studies using seated visuomotor anklebot training with chronic patients are reviewed, along with results from initial efforts to evaluate the anklebot's utility as a clinical tool for assessing intrinsic ankle stiffness. The review includes a brief discussion of future directions for using the seated anklebot training in the earliest phases of sub-acute therapy, and to incorporate neurophysiological measures of cerebro-cortical activity as a means to reveal underlying mechanistic processes of motor learning and brain plasticity associated with robotic training.

Conclusions: Finally we conclude with an initial control systems strategy for utilizing the anklebot as a gait training tool that includes integrating an Internal Model-based adaptive controller to both accommodate individual deficit severities and adapt to changes in patient performance.

Conflict of interest statement

Declaration of conflicting interests: The author(s) declared a potential conflict of interest (e.g. a financial relationship with the commercial organizations or products discussed in this article) as follows: Dr. H. I. Krebs is a co-inventor in the MIT 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.

Figures

Fig. 1

The modular 2-DOF actuated and impedance-controlled anklebot is designed for use in seated position with video feedback (left panel – plantar flexion and center panel - dorsiflexion) and for upright walking (right panel).

Fig. 2

Exemplar point-to-point unassisted movements made by a typical (upper) stroke and (lower) healthy subject before (left) and after training (right). (Adapted from Roy et al., J Rehabil Res Dev 2011).

Fig. 3

Least-squares linear regressors showing the mean ± SD of the slope (torque vs. ankle angle) in different directions of stretch (left column-sagittal plane, right column-frontal plane) for (top panel) healthy young (YH), (middle panel) age-matched older controls (AC), and (bottom panel) stroke subjects (ST). By convention, angles in dorsiflexion and eversion were considered positive, and those in plantar flexion and inversion were negative. Torque was assigned a polarity consistent with the direction of the movement that it would generate (e.g., dorsiflexion torque was taken as positive). Anatomical neutral was taken as the “zero” position and was determined by positioning the foot on the ground at 90◦ with respect to the long axis of the leg. (Adapted from Roy et al., J Neurophysiol 2011).

Fig. 4

Changes in PAS before (PRE) and after training (POST): (left panel) Sagittal plane; (right panel) Frontal plane. Unlike the sagittal plane PAS, the frontal plane PAS was not anisotropic at either time points. (Adapted from Roy et al., J Rehabil Res Dev 2013). (*p < 0.05; **p < 0.01).

Fig. 5

(Top panel) Strength of networking between the contralesional frontal region (F) and the bihemispheric parietal regions (P) for the two groups (low reward on the left) as a result of the intervention; (bottom panel) relative change in motor performance as compared to before the 3-week ankle robotic training intervention. The high reward group exhibited greater performance gains and reduced networking (seen as thinner connecting lines) as compared to the low reward group.

Fig. 6

Timing of anklebot torques can be differentially linked to specific sub-tasks within the gait cycle, and may be adjusted according to patient deficits. The diagram shows three sub-events that are relevant to ankle function throughout the gait cycle. (Adapted from Roy et al., Proc. IEEE ICRA, 2013).

Fig. 7

IM-based feedback control system incorporating an optimal controller determining the optimal anklebot torques and an adaptive ARMAX predictor to predict ankle position via least squares estimation.