Robotic devices as therapeutic and diagnostic tools for stroke recovery

Abstract

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.

Figure 1

Different robotic devices. A, The assisted rehabilitation measurement and guide device requires that the patient move the end of the robotic arm. The position and speed of movement of the robotic arm are represented by a cursor on a video screen, and the reaching movements are executed in one direction at a time. If the patient cannot move the robotic arm, the device will assist. B, The robot-assisted bilateral arm trainer requires the patient to attempt to move the affected and unaffected limbs, including wrist pronation and supination, at the same time in response to visual cues. This device moves the patients’ limbs. C and D, The Mirror Image Movement Enabler (MIME) robotic device can be used for the affected limb or paired with a second robotic arm to execute mirror movements with the affected and unaffected limbs simultaneously. If the patient cannot execute the movement, the device will assist. E–G, For each of the Massachusetts Institute of Technology (MIT)–Manus devices, a patient views a video screen and moves the end of the robotic arm. A cursor on a video screen represents the position, direction, and speed of the movement of the robotic arm. Computers connected to the robot record the position, speed, and forces or torques of a patient’s complete movement history during the training. The task is to make point-to-point movements, which the robot can also assist by correcting the path or increasing the speed of movement to the target. The MIT-Manus devices are back-drivable, which means the robot “gets out of the way” of a movement so that the patient experiences a device that moves easily even with weak forces. A spatial extension device expands an MIT-Manus shoulder/elbow device to train movements of the arm in the antigravity plane and to train rotator cuff and scapulation movements (E). A shoulder/elbow MIT-Manus device trains elbow and shoulder flexion and extension and shoulder abduction and adduction (F).^,, The wrist device expansion of the shoulder/elbow MIT-Manus trains wrist pronation, supination, flexion, extension, ulnar, and radial movements (G).

Figure 2

Results from treatments using robotic devices. A, Cumulative probability distributions of total Fugl-Meyer Motor (FM) scale scores (range, most severe=0 to less severe=54; maximum, 66) for patients evaluated at treatment admission, midpoint, treatment discharge, and follow-up 3 months after robotic training has ended. The curve representing this group of treated patients shifts to higher FM scores at the midpoint of training and it shifts to the right at discharge and follow-up indicating progressive increase of motor ability as the FM scores increase. The improvement after training is significant (Kolmogorov-Smirnov test, P<.001). Closer inspection of the cumulative probability values between 0.5 and 0.75 (right side of part A) are taken from the gray region depicted in the main graph. At the 0.5 level, admission to midpoint captures most of the improvement. At the 0.75 level, the improvement occurs after the midpoint evaluation and again at follow-up. For some patients with less severe impairment, even an intensive training experience did not define a performance optimum, as there was additional improvement after discharge. For other patients, the dose of training needs to be optimized. B, The left panel demonstrates the speed with which the patient executes an untrained movement at the start of training. The movement can be decomposed into submovements. The right panels depict an analysis of the individual submovements and, in particular, the peak speed and the duration of each submovement. A patient’s performance on this untrained task is represented by a line from the baseline performance connected to a green (P<.05) or a blue circle (P>.05), and each circle represents the discharge value. At discharge, most patients demonstrate longer submovements that are executed more quickly. These changes in submovements reflect smoother movement. C, Transcranial magnetic stimulation generates motor evoked potentials (MEPs) in the flexor carpi radialis (N=6 patients, bars represent mean [SEM]). The MEPs are recorded from the flexor carpi radialis muscle during a low-level isometric wrist flexion, before and immediately following 20-minute anodal transcranial direct current stimulation (tDCS), then again after 1 hour of robotic wrist therapy. Following tDCS, MEP amplitude is significantly elevated (*P<.05) and remains significantly elevated after robotic therapy (*P<.05), indicating integrity and potentially increased efficiency within the corticomotor pathways.