Bimanual motor skill learning and robotic assistance for chronic hemiparetic stroke: a randomized controlled trial.

Using robotic devices might improve recovery post-stroke, but the optimal way to apply robotic assistance has yet to be determined. The current study aimed to investigate whether training under the robotic active-assisted mode improves bimanual motor skill learning (biMSkL) more than training under the active mode in stroke patients. Twenty-six healthy individuals (HI) and 23 chronic hemiparetic stroke patients with a detectable lesion on MRI or CT scan, who demonstrated motor deficits in the upper limb, were randomly allocated to two parallel groups. The protocol included a two-day training on a new bimanual cooperative task, LIFT-THE-TRAY, under either the active or active-assisted modes (where assistance decreased in a pre-determined stepwise fashion) with the bimanual version of the REAplan® robotic device. The hypothesis was that the active-assisted mode would result in greater biMSkL than the active mode. The biMSkL was quantified by a speed-accuracy trade-off (SAT) before (T1) and immediately after (T2) training on days 1 and 2 (T3 and T4). The change in SAT after 2 days of training (T4/T1) indicated that both HI and stroke patients learned and retained the bimanual cooperative task. After 2 days of training, the active-assisted mode did not improve biMSkL more than the active mode (T4/T1) in HI nor stroke patients. Whereas HI generalized the learned bimanual skill to different execution speeds in both the active and active-assisted subgroups, the stroke patients generalized the learned skill only in the active subgroup. Taken together, the active-assisted mode, applied in a pre-determined stepwise decreasing fashion, did not improve biMSkL more than the active mode in HI and stroke subjects. Stroke subjects might benefit more from robotic assistance when applied "as-needed." This study was approved by the local ethical committee (Comité d'éthique médicale, CHU UCL Namur, Mont-Godinne, Yvoir, Belgium; Internal number: 54/2010, EudraCT number: NUB B039201317382) on July 14, 2016 and was registered with ClinicalTrials.gov (Identifier: NCT03974750) on June 5, 2019.

Assessment of upper limb spasticity in stroke patients using the robotic device REAplan.

OBJECTIVE:  To assess the capacity of the robotic device REAplan to measure overall upper limb peak resistance force, as a reflection of upper limb spasticity. METHODS:  Twelve patients with chronic stroke presenting upper limb spasticity were recruited to the study. Patients underwent musculocutaneous motor nerve block to reduce the spasticity of elbow flexor muscles. Each patient was assessed before and after the motor nerve block. Overall the REAplan measured upper limb resistance force. The robot passively mobilized the patient's upper limb at various velocities (10, 20, 30, 40 and 50 cm/s) in a back-and-forth trajectory (30 cm). The peak resistance force was analysed for each forward movement. Ten movements were performed and averaged at each velocity condition. RESULTS: The overall upper limb resistance force increased proportionally to the mobilization velocity (p< 0.001). Resistance force decreased after the motor nerve block at 40 and 50 cm/s (p < 0.05). Overall upper limb resistance force results showed excellent correlation with the Modified Ashworth Scale for elbow flexor muscles, for each velocity condition equal or higher than 30 cm/s (ρ >0.6). CONCLUSION:  This study proposes a new, valid, reliable and sensitive protocol to quantify upper limb resistance force using the REAplan, as a reflection of upper limb spasticity.

Optimal design of an alignment-free two-DOF rehabilitation robot for the shoulder complex.

This paper presents the optimal design of an alignment-free exoskeleton for the rehabilitation of the shoulder complex. This robot structure is constituted of two actuated joints and is linked to the arm through passive degrees of freedom (DOFs) to drive the flexion-extension and abduction-adduction movements of the upper arm. The optimal design of this structure is performed through two steps. The first step is a multi-objective optimization process aiming to find the best parameters characterizing the robot and its position relative to the patient. The second step is a comparison process aiming to select the best solution from the optimization results on the basis of several criteria related to practical considerations. The optimal design process leads to a solution outperforming an existing solution on aspects as kinematics or ergonomics while being more simple.