@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 = {

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.

}, 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 {8305478, title = {Reflection on System Dynamics Principles Improves Student Performance in Haptic Paddle Labs}, journal = {IEEE Transactions on Education}, volume = {61}, number = {3}, year = {2018}, month = {08/2018}, pages = {245-252}, keywords = {abstract conceptualization, CE, computer aided instruction, concrete experience, educational courses, haptic devices, Haptic interfaces, haptics, lab report grades, laboratory, laboratory exercises, learning cycle, learning outcomes, low-cost educational tools, Mechanical engineering, mechanical engineering computing, mechanical engineering curricula, mechanical engineering curriculum, Mechatronics, mechatronics content, multiple student GPA quartiles, Performance evaluation, reflection, reflection phase, reflective curriculum, reflective observation, standard haptic paddle lab curriculum, standard nonreflective curriculum, Standards, student performance improvement, System dynamics, system dynamics principles, Tools, undergraduate, virtual environment rendering}, issn = {0018-9359}, doi = {10.1109/TE.2018.2804327}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Rose2018ieee-reflection.pdf}, author = {C. G. Rose and C. G. McDonald and J. P. Clark and M. K. O{\textquoteright}Malley} } @proceedings {1885, title = {A Cable-based Series Elastic Actuator with Conduit Sensor for Wearable Exoskeletons}, year = {2017}, month = {05/2017}, publisher = {IEEE}, address = {Singapore}, keywords = {actuation system design, Actuators, cable tension control, cable tension measurement, cable-based series elastic actuator, cable-conduit transmission, cables (mechanical), compliance control, compliant force sensor, conduit sensor, DC motor, DC motors, deflection measurement, dynamic effect, Exoskeletons, Feedback, flexible cable conduit transmission, Force, Force control, force sensors, full wearable exosuit, gearbox, Hall effect sensors, Hall effect transducers, human arm, human-robot interaction, Impedance, Magnetic flux, physical assistance, robot dynamics, Robots, series elastic force sensor, soft exosuit, soft wearable exoskeleton, springs (mechanical), translational steel compression spring, transmission conduit, user interface, virtual impedance, wearable robotic device}, doi = {10.1109/ICRA.2017.7989790}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/blumenschein2017ieee.pdf}, author = {L. H. Blumenschein and C. G. McDonald and M. K. O{\textquoteright}Malley} } @proceedings {1923, title = {Combining functional electrical stimulation and a powered exoskeleton to control elbow flexion}, year = {2017}, month = {11/2017}, pages = {87-88}, keywords = {Elbow, elbow flexion, Exoskeletons, extension trajectory, functional electrical stimulation, hybrid FES, hybrid system, Iron, medical robotics, Muscles, neuromuscular stimulation, Patient rehabilitation, robotic exoskeleton system, Robots, Torque, Trajectory, upper-limb paralysis}, doi = {10.1109/WEROB.2017.8383860}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/Wolf2017werob.pdf}, author = {D. Wolf and N. Dunkelberger and C. G. McDonald and K. Rudy and C. Beck and M. K. O{\textquoteright}Malley and E. Schearer} } @proceedings {1813, title = {A bio-inspired algorithm for identifying unknown kinematics from a discrete set of candidate models by using collision detection}, year = {2016}, pages = {418-423}, abstract = {

Many robots are composed of interchangeable modular components, each of which can be independently controlled, and collectively can be disassembled and reassembled into new configurations. When assembling these modules into an open kinematic chain, there are some discrete choices dictated by the module geometry; for example, the order in which the modules are placed, the axis of rotation of each module with respect to the previous module, and/or the overall shape of the assembled robot. Although it might be straightforward for a human user to provide this information, there is also a practical benefit in the robot autonomously identifying these unknown, discrete forward kinematics. To date, a variety of techniques have been proposed to identify unknown kinematics; however, these methods cannot be directly applied during situations where we seek to identify the correct model amid a discrete set of options. In this paper, we introduce a method specifically for finding discrete robot kinematics, which relies on collision detection, and is inspired by the biological concepts of body schema and evolutionary algorithms. Under the proposed method, the robot maintains a population of possible models, stochastically identifies a motion which best distinguishes those models, and then performs that motion while checking for a collision. Models which correctly predicted whether a collision would occur produce candidate models for the next iteration. Using this algorithm during simulations with a Baxter robot, we were able to correctly determine the order of the links in 84\% of trials while exploring around 0.01\% of all possible models, and we were able to correctly determine the axes of rotation in 94\% of trials while exploring \< 0.1\% of all possible models.

}, isbn = {978-1-5090-3287-7}, issn = {978-1-5090-3287-7}, doi = {10.1109/BIOROB.2016.7523663}, url = {http://ieeexplore.ieee.org/abstract/document/7523663/}, attachments = {https://mahilab.rice.edu/sites/default/files/publications/BioRob_2016_Algorithm.pdf}, author = {Dylan P. Losey and C. G. McDonald and Marcia K. O{\textquoteright}Malley} }