@article {1798, title = {Effects of assist-as-needed upper extremity robotic therapy after incomplete spinal cord injury: a parallel-group controlled trial}, journal = {Frontiers in Neurobotics}, volume = {11}, number = {26}, year = {2017}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/fnbot-11-00026.pdf}, author = {John M. Frullo and Jared Elinger and Ali Utku Pehlivan and Kyle Fitle and Kathryn Nedley and Gerard Francisco and Fabrizio Sergi and Marcia K. O{\textquoteright}Malley} } @proceedings {1817, title = {Maintaining Subject Engagement during Robotic Rehabilitation with a Minimal Assist-as-Needed (mAAN) Controller}, year = {2017}, month = {07/2017}, publisher = {IEEE}, address = {London, UK}, abstract = {

One challenge of robotic rehabilitation interventions is devising ways to encourage and maintain high levels of subject involvement over long duration therapy sessions. Assist-as-needed controllers have been proposed which modulate robot intervention in movements based on measurements of subject involvement. This paper presents a minimal assist-as-needed controller, which modulates allowable error bounds and robot intervention based on sensorless force measurement accomplished through a nonlinear disturbance observer. While similar algorithms have been validated using healthy subjects, this paper presents a validation of the proposed mAAN control algorithm{\textquoteright}s ability to encourage user involvement with an impaired individual. User involvement is inferred from muscle activation, measured via surface electromyography (EMG). Experimental validation shows increased EMG muscle activation when using the proposed mAAN algorithm compared to non-adaptive algorithms.

}, issn = {978-1-5386-2296-4}, doi = {10.1109/ICORR.2017.8009222}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Pehlivan_2017_Maintaining.pdf}, author = {Ali Utku Pehlivan and Dylan P. Losey and Rose, Chad G.} } @article {1825, title = {Minimal assist-as-needed controller for upper limb robotic rehabilitation}, journal = {IEEE Transactions on Robotics}, volume = {32}, number = {1}, year = {2016}, month = {02/2016}, pages = {113-124}, chapter = {113}, abstract = {

Robotic rehabilitation of the upper limb following neurological injury is most successful when subjects are engaged in the rehabilitation protocol. Developing assistive control strategies that maximize subject participation is accordingly an active area of research, with aims to promote neural plasticity and, in turn, increase the potential for recovery of motor coordination. Unfortunately, state-of-the-art control strategies either ignore more complex subject capabilities or assume underlying patterns govern subject behavior and may therefore intervene suboptimally. In this paper, we present a minimal assist-as-needed (mAAN) controller for upper limb rehabilitation robots. The controller employs sensorless force estimation to dynamically determine subject inputs without any underlying assumptions as to the nature of subject capabilities and computes a corresponding assistance torque with adjustable ultimate bounds on position error. Our adaptive input estimation scheme is shown to yield fast, stable, and accurate measurements regardless of subject interaction and exceeds the performance of current approaches that estimate only position-dependent force inputs from the user. Two additional algorithms are introduced in this paper to further promote active participation of subjects with varying degrees of impairment. First, a bound modification algorithm is described, which alters allowable error. Second, a decayed disturbance rejection algorithm is presented, which encourages subjects who are capable of leading the reference trajectory. The mAAN controller and accompanying algorithms are demonstrated experimentally with healthy subjects in the RiceWrist-S exoskeleton.

}, issn = {1552-3098}, doi = {10.1109/TRO.2015.2503726}, url = {http://ieeexplore.ieee.org/abstract/document/7360218/}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/TRO_2016.pdf}, author = {Ali Utku Pehlivan and Dylan P. Losey 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} }