@article {Berning2021COBME,
	title = {Myoelectric Control and Neuromusculoskeletal Modeling: Complementary Technologies for Rehabilitation Robotics},
	journal = {Current Opinion in Biomedical Engineering},
	year = {2021},
	pages = {100313},
	abstract = {<p>Stroke and spinal cord injury (SCI) are a leading cause of disability in the United States, and researchers have pursued using robotic devices to aid rehabilitation efforts for resulting upper-extremity impairments. To date, however, robotic rehabilitation of the upper limb has produced only limited improvement in functional outcomes compared to traditional therapy. This paper explores the potential of myoelectric control and neuromusculoskeletal modeling for robotic rehabilitation using the current state of the art of each individual field as evidence. Continuing advances in the fields of myoelectric control and neuromusculoskeletal modeling offer opportunities for further improvements of rehabilitation robot control strategies. Specifically, personalized neuromusculoskeletal models driven by a subject{\textquoteright}s electromyography signals may provide accurate predictions of the subject{\textquoteright}s muscle forces and joint moments which, when used to design novel control strategies, could yield new approaches to robotic therapy for stroke and SCI that surpass the efficacy of traditional therapy.</p>},
	keywords = {Electromyography, neuromusculoskeletal modeling, robotic rehabilitation, upper limb motor impairment},
	issn = {2468-4511},
	doi = {https://doi.org/10.1016/j.cobme.2021.100313},
	url = {https://www.sciencedirect.com/science/article/pii/S2468451121000532},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/BerningCOBME2021_preprint.pdf},
	author = {Jeffrey Berning and Gerard E. Francisco and Shuo-Hsiu Chang and Benjamin J. Fregly and Marcia K. O{\textquoteright}Malley}
}
@article {6878466,
	title = {A Subject-Adaptive Controller for Wrist Robotic Rehabilitation},
	journal = {Mechatronics, IEEE/ASME Transactions on},
	volume = {20},
	number = {3},
	year = {2015},
	pages = {1338 - 1350},
	keywords = {adaptive control, Exoskeletons, Force, Iron, Medical treatment, nonlinear systems, parallel mechanisms, robot dynamics, robotic rehabilitation, Robots, Trajectory, Vectors, Wrist},
	issn = {1083-4435},
	doi = {10.1109/TMECH.2014.2340697},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/tmech-pehlivan-press.pdf},
	author = {Pehlivan, A.U. and Sergi, F. and OMalley, M.K.}
}
@proceedings {1706,
	title = {System characterization of RiceWrist-S: A forearm-wrist exoskeleton for upper extremity rehabilitation},
	year = {2013},
	month = {June},
	keywords = {Actuators, closed loop position performance, closed loop systems, distal joints, Exoskeletons, forearm rehabilitation, forearm-wrist exoskeleton, Friction, haptic interface design, Joints, medical robotics, neurological lesions, neurophysiology, Patient rehabilitation, position control, prosthetics, RiceWrist-S, robotic rehabilitation, Robots, serial mechanisms, spatial resolution, spinal cord injury, spinal cord injury rehabilitation, stroke, stroke rehabilitation, system characterization, Torque, torque output, upper extremity rehabilitation, Wrist, wrist rehabilitation},
	doi = {10.1109/ICORR.2013.6650462},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/Pehlivan_RW-S_ICORR2013.pdf},
	author = {Pehlivan, Ali Utku and Rose, Chad G. and O{\textquoteright}Malley, Marcia K.}
}
@proceedings {celik_comparison,
	title = {Comparison of robotic and clinical motor function improvement measures for sub-acute stroke patients},
	year = {2008},
	pages = {2477{\textendash}2482},
	address = {Pasadena, CA},
	abstract = {<p>In this paper, preliminary results in motor function improvement for four sub-acute stroke patients that underwent a hybrid robotic and traditional rehabilitation program are presented. The therapy program was scheduled for three days a week, four hours per day (approximately 60\% traditional constraint induced therapy activities and 40\% robotic therapy). A haptic joystick was used to implement four different operating modes for robotic therapy: unassisted (U), constrained (C), assisted (A), and resisted (R) modes. A target hitting task involving the positioning of a pointer on twelve targets was completed by the patients. Two different robotic measures were utilized to quantify the motor function improvement through the sessions: trajectory error (TE) and smoothness of movement (SM). Fugl-Meyer (FM) and motor activity log (MAL) scales were used as clinical measures. Analysis of results showed that the group demonstrates a significant motor function improvement with respect to both clinical and robotic measures. Regression analyses were carried out on corresponding clinical and robotic measure result pairs. A significant relation between FM scale and robotic measures was found for both of the analyzed modes. Regression of robotic measures on MAL scores resulted in no significance. A regression analysis that compared the two clinical measures revealed a very low agreement. Our findings suggest that it might be possible to obtain objective robotic measures that are significantly correlated to widely-used and reliable clinical measures in considerably different operating modes and control schemes.</p>},
	keywords = {robotic rehabilitation},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/74-CelikICRA2008.pdf},
	author = {Ozkan Celik and O{\textquoteright}Malley, M.K. and Boake, Corwin and H.S. Levin and Fischer, Steven and Reistetter, Timothy}
}
