Robot-aided neurorehabilitation

Abstract

Our goal is to apply robotics and automation technology to assist, enhance, quantify, and document neurorehabilitation. This paper reviews a clinical trial involving 20 stroke patients with a prototype robot-aided rehabilitation facility developed at the Massachusetts Institute of Technology, Cambridge, (MIT) and tested at Burke Rehabilitation Hospital, White Plains, NY. It also presents our approach to analyze kinematic data collected in the robot-aided assessment procedure. In particular, we present evidence 1) that robot-aided therapy does not have adverse effects, 2) that patients tolerate the procedure, and 3) that peripheral manipulation of the impaired limb may influence brain recovery. These results are based on standard clinical assessment procedures. We also present one approach using kinematic data in a robot-aided assessment procedure.

Fig. 1

(a) MIT-MANUS: Assembly sketch. The sketch shows a patient during the robot-aided neurorehabilitation session. Patient sat facing the robot and was required to move the robot end-effector according to the game’s goals. If the patient could not perform the task in response to a visual and auditory cue, the robot assisted and guided the patient’s hand. The left side sketch shows a patient working only with the planar module, a direct-drive five bar-linkage SCARA mechanism which provides two translational degrees of freedom for elbow and forearm motion [see Fig. 1(b)]. A custom-made hand-holder connects the patient’s impaired limb to the robot end-effector [see Fig. 1(c)]. The right side sketch shows a patient working with the planar module and the wrist module [see Fig. 1(d)], which is mounted on the end of the planar module and provides three degrees of freedom for wrist motion. (b) MIT-MANUS: Planar module (not in scale). (c) MIT-MANUS: Custom-made hand-holders for the planar module (not in scale). (d) MIT-MANUS: Wrist module (not in scale).

Fig. 1

(a) MIT-MANUS: Assembly sketch. The sketch shows a patient during the robot-aided neurorehabilitation session. Patient sat facing the robot and was required to move the robot end-effector according to the game’s goals. If the patient could not perform the task in response to a visual and auditory cue, the robot assisted and guided the patient’s hand. The left side sketch shows a patient working only with the planar module, a direct-drive five bar-linkage SCARA mechanism which provides two translational degrees of freedom for elbow and forearm motion [see Fig. 1(b)]. A custom-made hand-holder connects the patient’s impaired limb to the robot end-effector [see Fig. 1(c)]. The right side sketch shows a patient working with the planar module and the wrist module [see Fig. 1(d)], which is mounted on the end of the planar module and provides three degrees of freedom for wrist motion. (b) MIT-MANUS: Planar module (not in scale). (c) MIT-MANUS: Custom-made hand-holders for the planar module (not in scale). (d) MIT-MANUS: Wrist module (not in scale).

Fig. 2

(a) Video-games: Elbow and shoulder exercises. Targets were arranged so that diagonal paths required predominantly elbow or shoulder motions while vertical, horizontal or curved paths required coordination of both. (b) Video-games: Visual displays. The games included drawing circles, stars, squares, diamonds, and navigating through windows.

Fig. 3

(a) Drawing four circles at constant speed. Subject grasped the robot handle with the palm and was instructed to draw circles at constant speed. His hand was within view, but no explicit feedback was provided. The trial was repeated at faster speeds until the subject considered himself unable to maintain constant speed. The left column shows the circle traces and the right column shows the speed profiles at different target constant speeds. Note the oscillatory characteristic of the speed in all trials. (b) Polar plots of four circles drawn at different target constant speeds. The plot shows a composite of all trials of (a). Speed was normalized and the initial accelerating and decelerating phases were omitted. Note the kinematic (not temporal) characteristic of plot.

Fig. 4

Irregular sampling radial basis function (ISRBF). The figure illustrates the ISRBF approach. It resembles a pyramidal filter, in which a “mother” shape is fitted to the function’s largest peak. If the remainder is above an error bound, and the number of bases does not surpass the upperbound limit obtained from the curvature analysis, the process is reiterated.

Fig. 5

Patient (A) drawing clockwise circles starting and ending at position P2 (9 o’clock). The patient was wearing a hand-holder that connects his/her palm to the robot end-effector and the elbow was supported against gravity. The patient was instructed to draw a smooth circle. The hand was within view, but no explicit feedback was provided.

Fig. 6

Application of the irregular sampling radial basis function algorithm (ISRBF) to the circles of Fig. 5 In the first step (left column), the maximum number of bases (Cn) is obtained from the peaks in curvature. Peaks at the edge of the interval and below 5% of maximum peak are not considered, but an extra peak is added to Cn to account for the edges. The “mother” shape is fitted to the symmetric regions around the peaks. The process stops if the error of the approximation is below a predetermined threshold or the process uses Cn bases. The resulting approximation is shown in the right column.

Fig. 7

Apparent changes in movement composition with recovery. The ISRBF algorithm suggests that movement duration is decreasing (see right column: time duration), apparent segment duration is increasing (see right column: submovement mean duration), and the degree of overlap of submovements may be increasing, indicating blending (see right column: ratio of submovement overlap). There is no apparent trend for the path length, number of submovements, or mean peak speed.

Fig. 8

Robot-aided neurorehabilitation: Classroom or group session. This figure illustrates the potential for cost-containment by allowing a clinician to work with more than one patient at a time.

Fig. 9

Robot-aided neurorehabilitation: Self-care therapy at home. The clinician telementors an out-patient via a bilateral link between the robot in the home and the robot in the clinic.