@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}
}
@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 {1880,
	title = {Robotic training and clinical assessment of forearm and wrist movements after incomplete spinal cord injury: A case study},
	year = {2011},
	month = {June},
	pages = {619-622},
	abstract = {<p>The effectiveness of a robotic training device was evaluated in a 24-year-old male, cervical level four, ASIA Impairment Scale D injury. Robotic training of both upper extremities was provided for three hr/day for ten consecutive sessions using the RiceWrist, an electrically-actuated forearm and wrist haptic exoskeleton device that has been designed for rehabilitation applications. Training involved wrist flexion/extension, radial/ulnar deviation and forearm supination/pronation. Therapy sessions were tailored, based on the patient{\textquoteright}s movement capabilities for the wrist and forearm, progressed gradually by increasing number of repetitions and resistance. Outcome measures included the ASIA upper-extremity motor score, grip and pinch strength, the Jebsen-Taylor Hand Function test and the Functional Independence Measure. After the training, improvements were observed in pinch strength, and functional tasks. The data from one subject provides valuable information on the feasibility and effectiveness of robotic-assisted training of forearm and hand functions after incomplete spinal cord injury.</p>},
	keywords = {age 24 yr, arm motor function recovery, ASIA upper-extremity motor score, biomechanics, clinical assessment, electrically-actuated forearm, Forearm, forearm movement, forearm pronation, forearm supination, functional independence measure, functional tasks, grip, Haptic interfaces, Humans, injuries, Jebsen-Taylor hand function test, Joints, Male, medical robotics, Medical treatment, Muscles, neurophysiology, patient movement capabilities, Patient rehabilitation, Patient treatment, pinch strength, radial-ulnar deviation, rehabilitation applications, robotic training, Robots, Spinal Cord Injuries, spinal cord injury, training, Wrist, wrist extension, wrist flexion, wrist haptic exoskeleton device, wrist movement, Young Adult},
	doi = {10.1109/ICORR.2011.5975425},
	attachments = {https://mahilab.rice.edu/sites/default/files/publications/yozbatiran2011ieee.pdf},
	author = {N. Yozbatiran and J. Berliner and C. Boake and M. K. O{\textquoteright}Malley and Z. Kadivar and G. E. Francisco}
}
