@article {1993, title = {The SE-AssessWrist for robot-aided assessment of wrist stiffness and range of motion: Development and experimental validation}, journal = {Journal of Rehabilitation and Assistive Technologies Engineering}, volume = {8}, year = {2021}, month = {04/2021}, pages = {2055668320985774}, abstract = {

IntroductionPhysical human-robot interaction offers a compelling platform for assessing recovery from neurological injury; however, robots currently used for assessment have typically been designed for the requirements of rehabilitation, not assessment. In this work, we present the design, control, and experimental validation of the SE-AssessWrist, which extends the capabilities of prior robotic devices to include complete wrist range of motion assessment in addition to stiffness evaluation.MethodsThe SE-AssessWrist uses a Bowden cable-based transmission in conjunction with series elastic actuation to increase device range of motion while not sacrificing torque output. Experimental validation of robot-aided wrist range of motion and stiffness assessment was carried out with five able-bodied individuals.ResultsThe SE-AssessWrist achieves the desired maximum wrist range of motion, while having sufficient position and zero force control performance for wrist biomechanical assessment. Measurements of two-degree-of-freedom wrist range of motion and stiffness envelopes revealed that the axis of greatest range of motion and least stiffness were oblique to the conventional anatomical axes, and approximately parallel to each other.ConclusionsSuch an assessment could be beneficial in the clinic, where standard clinical measures of recovery after neurological injury are subjective, labor intensive, and graded on an ordinal scale.

}, doi = {10.1177/2055668320985774}, url = {https://doi.org/10.1177/2055668320985774}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/JRATE_2021_Erwin_SE-AssessWrist_press.pdf}, author = {Andrew Erwin and Craig G McDonald and Nicholas Moser and Marcia K O{\textquoteright}Malley} } @article {1940, title = {Improving short-term retention after robotic training by leveraging fixed-gain controllers}, journal = {Journal of Rehabilitation and Assistive Technologies Engineering}, volume = {6}, year = {2019}, month = {01/2019}, abstract = {

IntroductionWhen developing control strategies for robotic rehabilitation, it is important that end-users who train with those strategies retain what they learn. Within the current state-of-the-art, however, it remains unclear what types of robotic controllers are best suited for promoting retention. In this work, we experimentally compare short-term retention in able-bodied end-users after training with two common types of robotic control strategies: fixed- and variable-gain controllers.MethodsOur approach is based on recent motor learning research, where reward signals are employed to reinforce the learning process. We extend this approach to now include robotic controllers, so that participants are trained with a robotic control strategy and auditory reward-based reinforcement on tasks of different difficulty. We then explore retention after the robotic feedback is removed.ResultsOverall, our results indicate that fixed-gain control strategies better stabilize able-bodied users{\textquoteright} motor adaptation than either a no controller baseline or variable-gain strategy. When breaking these results down by task difficulty, we find that assistive and resistive fixed-gain controllers lead to better short-term retention on less challenging tasks but have opposite effects on the learning and forgetting rates.ConclusionsThis suggests that we can improve short-term retention after robotic training with consistent controllers that match the task difficulty.

}, keywords = {Control systems, haptic device, motor learning, neurorehabilitation, Robot-assisted rehabilitation}, doi = {10.1177/2055668319866311}, url = {https://doi.org/10.1177/2055668319866311}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Losey2019RATE.pdf}, author = {Dylan P Losey and Laura H Blumenschein and Janelle P Clark and Marcia K O{\textquoteright}Malley} } @article {1719, title = {Current Trends in Robot-Assisted Upper-Limb Stroke Rehabilitation: Promoting Patient Engagement in Therapy}, journal = {Current Physical Medicine and Rehabilitation Reports}, year = {2014}, doi = {10.1007/s40141-014-0056-z}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/2014_CPMRR_press.pdf}, author = {Amy A Blank and James A French and Ali Utku Pehlivan and Marcia K O{\textquoteright}Malley} }