@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 {9031340,
	title = {A Myoelectric Control Interface for Upper-Limb Robotic Rehabilitation Following Spinal Cord Injury},
	journal = {IEEE Transactions on Neural Systems and Rehabilitation Engineering},
	volume = {28},
	number = {4},
	year = {2020},
	month = {April},
	pages = {978-987},
	abstract = {<p>Spinal cord injury (SCI) is a widespread, life-altering injury leading to impairment of sensorimotor function that, while once thought to be permanent, is now being treated with the hope of one day being able to restore function. Surface electromyography (EMG) presents an opportunity to examine and promote human engagement at the neuromuscular level, enabling new protocols for intervention that could be combined with robotic rehabilitation, particularly when robot motion or force sensing may be unusable due to the user{\textquoteright}s impairment. In this paper, a myoelectric control interface to an exoskeleton for the elbow and wrist was evaluated on a population of ten able-bodied participants and four individuals with cervical-level SCI. The ability of an EMG classifier to discern intended direction of motion in single-degree-of-freedom (DoF) and multi-DoF control modes was assessed for usability in a therapy-like setting. The classifier demonstrated high accuracy for able-bodied participants (averages over 99\% for single-DoF and near 90\% for multi-DoF), and performance in the SCI group was promising, warranting further study (averages ranging from 85\% to 95\% for single-DoF, and variable multi-DoF performance averaging around 60\%). These results are encouraging for the future use of myoelectric interfaces in robotic rehabilitation for SCI.</p>},
	keywords = {Electromyography, injuries, Muscles, myoelectric control, pattern recognition, Rehabilitation robotics, Robot kinematics, Robot sensing systems, spinal cord injury, Wrist},
	issn = {1558-0210},
	doi = {10.1109/TNSRE.2020.2979743},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/TNSRE_2020_McDonald.pdf},
	author = {C. G. McDonald and J. L. Sullivan and T. A. Dennis and M. K. O{\textquoteright}Malley}
}
@proceedings {1882,
	title = {Detecting movement intent from scalp EEG in a novel upper limb robotic rehabilitation system for stroke},
	year = {2014},
	month = {08/2014},
	keywords = {Accuracy, Adult, bioelectric potentials, brain-computer interfaces, closed loop systems, closed-loop brain-machine interfaces, Computer-Assisted, diseases, electroencephalography, Electromyography, Exoskeletons, hemiparesis, Humans, Male, medical robotics, medical signal detection, medical signal processing, Middle Aged, Movement, movement intent detection, neurophysiology, Paresis, Patient rehabilitation, Robotics, Robots, scalp electroencephalography, Signal Processing, stroke, stroke rehabilitation, Support Vector Machine, Support vector machines, training, Upper Extremity, upper extremity dysfunction, upper limb robotic rehabilitation system, Young Adult},
	doi = {10.1109/EMBC.2014.6944532},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/bhagat2014ieee.pdf},
	author = {N. A. Bhagat and J. French and A. Venkatakrishnan and N. Yozbatiran and G. E. Francisco and M. K. O{\textquoteright}Malley and J. L. Contreras-Vidal}
}
