@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}
}
