Comparison of Three-Dimensional, Assist-as-Needed Robotic Arm/Hand Movement Training Provided with Pneu-WREX to Conventional Tabletop Therapy After Chronic Stroke

Abstract

Objective Robot-assisted movement training can help individuals with stroke reduce arm and hand impairment, but robot therapy is typically only about as effective as conventional therapy. Refining the way that robots assist during training may make them more effective than conventional therapy. Here we measured the therapeutic effect of a robot that required individuals with a stroke to achieve virtual tasks in three dimensions against gravity. Design The robot continuously estimated how much assistance patients needed to perform the tasks and provided slightly less assistance than needed in order to reduce patient slacking. Individuals with a chronic stroke (n = 26, baseline upper extremity Fugl-Meyer score = 23 ± 8) were randomized into two groups and underwent 24 one hour training sessions over 2 months. One group received the assist-as-needed robot training and the other received conventional table top therapy with the supervision of a physical therapist. Results Training helped both groups significantly reduce their motor impairment, as measured by the primary outcome measure, the Fugl-Meyer score, but the improvement was small (3.0 ± 4.9 points for robot therapy, versus 0.9 ± 1.7 for conventional therapy). There was a trend for greater reduction for the robot trained group (p = 0.07). The robot group largely sustained this gain at the three-month follow-up. The robot-trained group also experienced significant improvements in Box and Blocks score and hand grip strength, while the control group did not, but these improvements were not sustained at follow-up. In addition, the robot-trained group showed a trend toward greater improvement in sensory function, as measured by the Nottingham Sensory Test (p = 0.06). Conclusions These results suggest that, in patients with chronic stroke and moderate-severe deficits, assisting in three dimensional virtual tasks with an assist-as-needed controller may make robotic training more effective than conventional table top training.

A crossover pilot study evaluating the functional outcomes of two different types of robotic movement training in chronic stroke survivors using the arm exoskeleton BONES

“…[10-12,15]). However, the improvement in motor impairment was more modest in our study than in others.…”

“…Because BONES allows the arm to move through the normal wide workspace, as opposed to some end-effector robots [2] or other limited DOF exoskeletons [10], it could be thought that training the upper limb in shoulder, elbow, forearm, wrist, and hand DOF increases the chances of a transfer of gains to ADL. Another reason could be that the significant gains in strength in muscle groups playing an important role in the upper-limb function [32], such as the shoulder adductors and elbow flexors, combined with a significant improvement in the speed of execution of a reaching task, might have facilitated the carry-over benefits to ADL.…”

Robotic Therapy Provides a Stimulus for Upper Limb Motor Recovery After Stroke That Is Complementary to and Distinct From Conventional Therapy

“…FM gains were 3.7 points using the GENTLE/s robot, 20 3.3 points using the T-WREX, 39 and 3 points using the Pneu-WREX. 40 We had smaller FM gains of 1.75 points after robot training, but hours of treatment were ½ of that in the T-WREX and Pneu-WREX studies, and the GENTLE/s and Pneu-WREX studies included subjects in the subacute recovery stage, when larger gains are more likely. In our study, total FM gain after receiving both treatments was 5.1 points, suggesting available treatment time might be most effective if divided between robotic and conventional therapies.…”

Neuromotor recovery from stroke: computational models at central, functional, and muscle synergy level

“…Only few examples of model-based robot controllers for treatment have been proposed so far (Wolbrecht et al, 2008; Reinkensmeyer et al, 2012b). One major difficulty is that to incorporate recovery models in the robot controller requires to achieve a dual goal: the controller should select the goal and the difficulty level of the exercise based on the subject’s state as predicted by the model and, at the same time, the model should be continuously adjusted, on the basis of the observed subject and robot performance while the treatment proceeds.…”