TY - JOUR T1 - Myoelectric Control and Neuromusculoskeletal Modeling: Complementary Technologies for Rehabilitation Robotics JF - Current Opinion in Biomedical Engineering Y1 - 2021 A1 - Jeffrey Berning A1 - Gerard E. Francisco A1 - Shuo-Hsiu Chang A1 - Benjamin J. Fregly A1 - Marcia K. O'Malley KW - Electromyography KW - neuromusculoskeletal modeling KW - robotic rehabilitation KW - upper limb motor impairment AB -

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’s electromyography signals may provide accurate predictions of the subject’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.

UR - https://www.sciencedirect.com/science/article/pii/S2468451121000532 ER - TY - JOUR T1 - In the Fundamentals of Endovascular and Vascular Surgery model motion metrics reliably differentiate competency JF - Journal of Vascular Surgery Y1 - 2020 A1 - Viony Belvroy A1 - Barathwaj Murali A1 - Malachi G. Sheahan A1 - Marcia K. O'Malley A1 - Jean Bismuth VL - 72 ER - TY - JOUR T1 - Neural activity modulations and motor recovery following brain-exoskeleton interface mediated stroke rehabilitation JF - NeuroImage: Clinical Y1 - 2020 A1 - Nikunj A. Bhagat A1 - Nuray Yozbatiran A1 - Jennifer L. Sullivan A1 - Ruta Paranjape A1 - Colin Losey A1 - Zachary Hernandez A1 - Zafer Keser A1 - Robert Grossman A1 - Gerard E. Francisco A1 - Marcia K. O'Malley A1 - Jose L. Contreras-Vidal KW - Brain-machine interface KW - Clinical trial KW - Exoskeletons KW - Movement related cortical potentials KW - stroke rehabilitation AB -

Brain-machine interfaces (BMI) based on scalp EEG have the potential to promote cortical plasticity following stroke, which has been shown to improve motor recovery outcomes. However, the efficacy of BMI enabled robotic training for upper-limb recovery is seldom quantified using clinical, EEG-based, and kinematics-based metrics. Further, a movement related neural correlate that can predict the extent of motor recovery still remains elusive, which impedes the clinical translation of BMI-based stroke rehabilitation. To address above knowledge gaps, 10 chronic stroke individuals with stable baseline clinical scores were recruited to participate in 12 therapy sessions involving a BMI enabled powered exoskeleton for elbow training. On average, 132 ± 22 repetitions were performed per participant, per session. BMI accuracy across all sessions and subjects was 79 ± 18% with a false positives rate of 23 ± 20%. Post-training clinical assessments found that FMA for upper extremity and ARAT scores significantly improved over baseline by 3.92 ± 3.73 and 5.35 ± 4.62 points, respectively. Also, 80% participants (7 with moderate-mild impairment, 1 with severe impairment) achieved minimal clinically important difference (MCID: FMA-UE >5.2 or ARAT >5.7) during the course of the study. Kinematic measures indicate that, on average, participants’ movements became faster and smoother. Moreover, modulations in movement related cortical potentials, an EEG-based neural correlate measured contralateral to the impaired arm, were significantly correlated with ARAT scores (ρ = 0.72, p < 0.05) and marginally correlated with FMA-UE (ρ = 0.63, p = 0.051). This suggests higher activation of ipsi-lesional hemisphere post-intervention or inhibition of competing contra-lesional hemisphere, which may be evidence of neuroplasticity and cortical reorganization following BMI mediated rehabilitation therapy.

VL - 28 UR - http://www.sciencedirect.com/science/article/pii/S2213158220303399 ER - TY - Generic T1 - Towards Automated Performance Assessment using Velocity-based Motion Quality Metrics T2 - International Symposium on Medical Robotics (ISMR) Y1 - 2020 A1 - Barathwaj Murali A1 - Viony Belvroy A1 - Shivam Pandey A1 - Michael D. Byrne A1 - Jean Bismuth A1 - Marcia K. O'Malley JF - International Symposium on Medical Robotics (ISMR) ER - TY - Generic T1 - A Bowden Cable-Based Series Elastic Actuation Module for Assessing the Human Wrist T2 - ASME Dynamic Systems and Controls Conference Y1 - 2018 A1 - Andrew Erwin A1 - Nick Moser A1 - Craig. G. McDonald A1 - Marcia K. O'Malley JF - ASME Dynamic Systems and Controls Conference PB - ASME CY - Atlanta, GA ER - TY - JOUR T1 - Closure to “A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction" JF - ASME Applied Mechanics Reviews Y1 - 2018 A1 - Dylan P. Losey A1 - Craig G. McDonald A1 - Edoardo Battaglia A1 - Marcia K. O'Malley AB -

In their discussion article on our review paper, Professors James Schmiedeler and Patrick Wensing have provided an insightful and informative perspective of the roles of intent detection, arbitration, and communication as three pillars of a framework for the implementation of shared control in physical human–robot interaction (pHRI). The authors both have significant expertise and experience in robotics, bipedal walking, and robotic rehabilitation. Their commentary introduces commonalities between the themes of the review paper and issues in locomotion with the aid of an exoskeleton or lower-limb prostheses, and presents several important topics that warrant further exploration. These include mechanical design as it pertains to the physical coupling between human and robot, modeling the human to improve intent detection and the arbitration of control, and finite-state machines as an approach for implementation. In this closure, we provide additional thoughts and discussion of these topics as they relate to pHRI.

VL - 70 UR - http://appliedmechanicsreviews.asmedigitalcollection.asme.org/article.aspx?articleID=2672398 ER - TY - Generic T1 - Learning from Physical Human Corrections, One Feature at a Time T2 - Human-Robot Interaction Y1 - 2018 A1 - Andrea Bajcsy A1 - Dylan P. Losey A1 - Marcia K. O'Malley A1 - Anca D. Dragan AB -

We focus on learning robot objective functions from human guidance: specifically, from physical corrections provided by the person while the robot is acting. Objective functions are typically parametrized in terms of features, which capture aspects of the task that might be important. When the person intervenes to correct the robot's behavior, the robot should update its understanding of which features matter, how much, and in what way. Unfortunately, real users do not provide optimal corrections that isolate exactly what the robot was doing wrong. Thus, when receiving a correction, it is difficult for the robot to determine which features the person meant to correct, and which features were changed unintentionally. In this paper, we propose to improve the efficiency of robot learning during physical interactions by reducing unintended learning. Our approach allows the human-robot team to focus on learning one feature at a time, unlike state-of-the-art techniques that update all features at once. We derive an online method for identifying the single feature which the human is trying to change during physical interaction, and experimentally compare this one-at-a-time approach to the all-at-once baseline in a user study. Our results suggest that users teaching one-at-a-time perform better, especially in tasks that require changing multiple features.

JF - Human-Robot Interaction PB - ACM/IEEE CY - Chicago, USA ER - TY - JOUR T1 - A review of intent detection, arbitration, and communication aspects of shared control for physical human-robot interaction JF - ASME Applied Mechanics Reviews Y1 - 2018 A1 - Dylan P. Losey A1 - Craig G. McDonald A1 - Edoardo Battaglia A1 - Marcia K. O'Malley AB -

As robotic devices are applied to problems beyond traditional manufacturing and industrial settings, we find that interaction between robots and humans, especially physical interaction, has become a fast developing field. Consider the application of robotics in healthcare, where we find telerobotic devices in the operating room facilitating dexterous surgical procedures, exoskeletons in the rehabilitation domain as walking aids and upper-limb movement assist devices, and even robotic limbs that are physically integrated with amputees who seek to restore their independence and mobility. In each of these scenarios, the physical coupling between human and robot, often termed physical human robot interaction (pHRI), facilitates new human performance capabilities and creates an opportunity to explore the sharing of task execution and control between humans and robots. In this review, we provide a unifying view of human and robot sharing task execution in scenarios where collaboration and cooperation between the two entities are necessary, and where the physical coupling of human and robot is a vital aspect. We define three key themes that emerge in these shared control scenarios, namely, intent detection, arbitration, and feedback. First, we explore methods for how the coupled pHRI system can detect what the human is trying to do, and how the physical coupling itself can be leveraged to detect intent. Second, once the human intent is known, we explore techniques for sharing and modulating control of the coupled system between robot and human operator. Finally, we survey methods for informing the human operator of the state of the coupled system, or the characteristics of the environment with which the pHRI system is interacting. At the conclusion of the survey, we present two case studies that exemplify shared control in pHRI systems, and specifically highlight the approaches used for the three key themes of intent detection, arbitration, and feedback for applications of upper limb robotic rehabilitation and haptic feedback from a robotic prosthesis for the upper limb.

VL - 70 UR - http://appliedmechanicsreviews.asmedigitalcollection.asme.org/article.aspx?articleID=2671581 ER - TY - JOUR T1 - Trajectory deformations from physical human–robot interaction JF - IEEE Transactions on Robotics Y1 - 2018 A1 - Dylan P. Losey A1 - Marcia K. O'Malley AB -

Robots are finding new applications where physical interaction with a human is necessary, such as manufacturing, healthcare, and social tasks. Accordingly, the field of physical human–robot interaction (pHRI) has leveraged impedance control approaches, which support compliant interactions between human and robot. However, a limitation of traditional impedance control is that—despite provisions for the human to modify the robot’s current trajectory—the human cannot affect the robot’s future desired trajectory through pHRI. In this paper, we present an algorithm for physically interactive trajectory deformations which, when combined with impedance control, allows the human to modulate both the actual and desired trajectories of the robot. Unlike related works, our method explicitly deforms the future desired trajectory based on forces applied during pHRI, but does not require constant human guidance. We present our approach and verify that this method is compatible with traditional impedance control. Next, we use constrained optimization to derive the deformation shape. Finally, we describe an algorithm for real-time implementation, and perform simulations to test the arbitration parameters. Experimental results demonstrate reduction in the human’s effort and improvement in the movement quality when compared to pHRI with impedance control alone.

VL - 34 UR - http://ieeexplore.ieee.org/document/8115323/ ER - TY - JOUR T1 - Effects of assist-as-needed upper extremity robotic therapy after incomplete spinal cord injury: a parallel-group controlled trial JF - Frontiers in Neurobotics Y1 - 2017 A1 - John M. Frullo A1 - Jared Elinger A1 - Ali Utku Pehlivan A1 - Kyle Fitle A1 - Kathryn Nedley A1 - Gerard Francisco A1 - Fabrizio Sergi A1 - Marcia K. O'Malley VL - 11 ER - TY - Generic T1 - Effects of Discretization on the K-Width of Series Elastic Actuators T2 - International Conference on Robotics and Automation (ICRA) Y1 - 2017 A1 - Dylan P. Losey A1 - Marcia K. O'Malley AB -

Rigid haptic devices enable humans to physically interact with virtual environments, and the range of impedances that can be safely rendered using these rigid devices is quantified by the Z-Width metric. Series elastic actuators (SEAs) similarly modulate the impedance felt by the human operator when interacting with a robotic device, and, in particular, the robot's perceived stiffness can be controlled by changing the elastic element's equilibrium position. In this paper, we explore the K-Width of SEAs, while specifically focusing on how discretization inherent in the computer-control architecture affects the system's passivity. We first propose a hybrid model for a single degree-of-freedom (DoF) SEA based on prior hybrid models for rigid haptic systems. Next, we derive a closed-form bound on the K-Width of SEAs that is a generalization of known constraints for both rigid haptic systems and continuous time SEA models. This bound is first derived under a continuous time approximation, and is then numerically supported with discrete time analysis. Finally, experimental results validate our finding that large pure masses are the most destabilizing operator in human-SEA interactions, and demonstrate the accuracy of our theoretical K-Width bound.

JF - International Conference on Robotics and Automation (ICRA) PB - IEEE CY - Singapore SN - 978-1-5090-4633-1 UR - http://ieeexplore.ieee.org/abstract/document/7989054/ ER - TY - Generic T1 - Learning Robot Objectives from Physical Human Interaction T2 - Conference on Robot Learning (CoRL) Y1 - 2017 A1 - Andrea Bajcsy A1 - Dylan P. Losey A1 - Marcia K. O'Malley A1 - Anca D. Dragan KW - learning from demonstration KW - physical human-robot interaction AB -

When humans and robots work in close proximity, physical interaction is inevitable. Traditionally, robots treat physical interaction as a disturbance, and resume their original behavior after the interaction ends. In contrast, we argue that physical human interaction is informative: it is useful information about how the robot should be doing its task. We formalize learning from such interactions as a dynamical system in which the task objective has parameters that are part of the hidden state, and physical human interactions are observations about these parameters. We derive an online approximation of the robot’s optimal policy in this system, and test it in a user study. The results suggest that learning from physical interaction leads to better robot task performance with less human effort.

JF - Conference on Robot Learning (CoRL) PB - PMLR CY - Mountain View, CA UR - http://proceedings.mlr.press/v78/bajcsy17a.html ER - TY - Generic T1 - The Rice Haptic Rocker: skin stretch haptic feedback with the Pisa/IIT SoftHand T2 - World Haptics Conference (WHC) Y1 - 2017 A1 - Edoardo Battaglia A1 - Janelle P. Clark A1 - Matteo Bianchi A1 - Manuel G. Catalano A1 - Antonio Bicchi A1 - Marcia K. O'Malley JF - World Haptics Conference (WHC) PB - IEEE CY - Munich, Germany ER - TY - Generic T1 - Simply Grasping Simple Shapes: Commanding a Humanoid Hand with a Shape-Based Synergy T2 - International Symposium on Robotics Research (ISRR) Y1 - 2017 A1 - Logan C. Farrell A1 - Troy A. Dennis A1 - Julia A. Badger A1 - Marcia K. O'Malley KW - Dexterous Hand KW - Grasp KW - Humanoid KW - Manipulation KW - Synergy AB -

Despite rapid advancements in dexterity and mechanical design, the utility of humanoid robots outside of a controlled laboratory setting is limited in part due to the complexity involved in programming robots to grasp common objects. There exists a need for an efficient method to command high degree-of-freedom (DoF) position-controlled dexterous manipulators to grasp a range of objects such that explicit models are not needed for every interaction. The authors propose a method termed geometrical synergies that, similar to the neuroscience concept of postural synergies, aims to decrease the commanded DoF of the humanoid hand. In the geometrical synergy approach, the method relies on grasp design based on intuitive measurements of the object to be grasped, in contrast to postural synergy methods that focus on the principal components of human grasps to determine robot hand joint commands. For this paper, a synergy was designed to grasp cylinder-shaped objects. Using the SynGrasp toolbox, a model of a twelve-DoF hand was created to perform contact analysis around a small set of cylinders dened by a single variable, diameter. Experiments were performed with the robot to validate and update the synergy-based models. Successful manipulation of a large range of cylindrical objects not previously introduced to the robot was demonstrated. This geometric synergy-based grasp planning method can be applied to any position-controlled humanoid hand to decrease the number of commanded DoF based on simple, measureable inputs in order to grasp commonly shaped objects. This method has the potential to vastly expand the library of objects the robot can manipulate.

 

JF - International Symposium on Robotics Research (ISRR) CY - Puerto Varas, Chile ER - TY - Generic T1 - A bio-inspired algorithm for identifying unknown kinematics from a discrete set of candidate models by using collision detection T2 - Biomedical Robotics and Biomechatronics (BioRob), 2016 6th IEEE International Conference on Y1 - 2016 A1 - Dylan P. Losey A1 - C. G. McDonald A1 - Marcia K. O'Malley AB -

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.

JF - Biomedical Robotics and Biomechatronics (BioRob), 2016 6th IEEE International Conference on SN - 978-1-5090-3287-7 UR - http://ieeexplore.ieee.org/abstract/document/7523663/ ER - TY - Generic T1 - Improving the retention of motor skills after reward-based reinforcement by incorporating haptic guidance and error augmentation T2 - Biomedical Robotics and Biomechatronics (BioRob), 2016 6th IEEE International Conference on Y1 - 2016 A1 - Dylan P. Losey A1 - Laura H. Blumenschein A1 - Marcia K. O'Malley AB -

There has been significant research aimed at leveraging programmable robotic devices to provide haptic assistance or augmentation to a human user so that new motor skills can be trained efficiently and retained long after training has concluded. The success of these approaches has been varied, and retention of skill is typically not significantly better for groups exposed to these controllers during training. These findings point to a need to incorporate a more complete understanding of human motor learning principles when designing haptic interactions with the trainee. Reward-based reinforcement has been studied for its role in improving retention of skills. Haptic guidance, which assists a user to complete a task, and error augmentation, which exaggerates error in order to enhance feedback to the user, have been shown to be beneficial for training depending on the task difficulty, subject ability, and task type. In this paper, we combine the presentation of reward-based reinforcement with these robotic controllers to evaluate their impact on retention of motor skill in a visual rotation task with tunable difficulty using either fixed or moving targets. We found that with the reward-based feedback paradigm, both haptic guidance and error augmentation led to better retention of the desired visuomotor offset during a simple task, while during a more complex task, only subjects trained with haptic guidance demonstrated performance superior to those trained without a controller.

JF - Biomedical Robotics and Biomechatronics (BioRob), 2016 6th IEEE International Conference on SN - 978-1-5090-3287-7 UR - http://ieeexplore.ieee.org/abstract/document/7523735/ ER - TY - JOUR T1 - Minimal assist-as-needed controller for upper limb robotic rehabilitation JF - IEEE Transactions on Robotics Y1 - 2016 A1 - Ali Utku Pehlivan A1 - Dylan P. Losey A1 - Marcia K. O'Malley AB -

Robotic rehabilitation of the upper limb following neurological injury is most successful when subjects are engaged in the rehabilitation protocol. Developing assistive control strategies that maximize subject participation is accordingly an active area of research, with aims to promote neural plasticity and, in turn, increase the potential for recovery of motor coordination. Unfortunately, state-of-the-art control strategies either ignore more complex subject capabilities or assume underlying patterns govern subject behavior and may therefore intervene suboptimally. In this paper, we present a minimal assist-as-needed (mAAN) controller for upper limb rehabilitation robots. The controller employs sensorless force estimation to dynamically determine subject inputs without any underlying assumptions as to the nature of subject capabilities and computes a corresponding assistance torque with adjustable ultimate bounds on position error. Our adaptive input estimation scheme is shown to yield fast, stable, and accurate measurements regardless of subject interaction and exceeds the performance of current approaches that estimate only position-dependent force inputs from the user. Two additional algorithms are introduced in this paper to further promote active participation of subjects with varying degrees of impairment. First, a bound modification algorithm is described, which alters allowable error. Second, a decayed disturbance rejection algorithm is presented, which encourages subjects who are capable of leading the reference trajectory. The mAAN controller and accompanying algorithms are demonstrated experimentally with healthy subjects in the RiceWrist-S exoskeleton.

VL - 32 UR - http://ieeexplore.ieee.org/abstract/document/7360218/ ER - TY - Generic T1 - SOM and LVQ classification of endovascular surgeons using motion-based metrics T2 - Workshop on Self-Organizing Maps (WSOM) Y1 - 2016 A1 - Kramer, B.D. A1 - Dylan P. Losey A1 - Marcia K. O'Malley AB -

An increase in the prevalence of endovascular surgery requires a growing number of proficient surgeons. Current endovascular surgeon evaluation techniques are subjective and time-consuming; as a result, there is a demand for an objective and automated evaluation procedure. Leveraging reliable movement metrics and tool-tip data acquisition, we here use neural network techniques such as LVQs and SOMs to identify the mapping between surgeons’ motion data and imposed rating scales. Using LVQs, only 50 % testing accuracy was achieved. SOM visualization of this inadequate generalization, however, highlights limitations of the present rating scale and sheds light upon the differences between traditional skill groupings and neural network clusters. In particular, our SOM clustering both exhibits more truthful segmentation and demonstrates which metrics are most indicative of surgeon ability, providing an outline for more rigorous evaluation strategies.

JF - Workshop on Self-Organizing Maps (WSOM) UR - https://link.springer.com/chapter/10.1007/978-3-319-28518-4_20 ER - TY - JOUR T1 - A Time-Domain Approach To Control Of Series Elastic Actuators: Adaptive Torque And Passivity-Based Impedance Control JF - IEEE/ASME Transactions on Mechatronics Y1 - 2016 A1 - Dylan P. Losey A1 - Andrew Erwin A1 - Craig G. McDonald A1 - Fabrizio Sergi A1 - Marcia K. O'Malley AB -

Robots are increasingly designed to physically interact with humans in unstructured environments, and as such must operate both accurately and safely. Leveraging compliant actuation, typically in the form of series elastic actuators (SEAs), can guarantee this required level of safety. To date, a number of frequency-domain techniques have been proposed which yield effective SEA torque and impedance control; however, these methods are accompanied by undesirable stability constraints. In this paper, we instead focus on a time-domain approach to the control of SEAs, and adapt two existing control techniques for SEA platforms. First, a model reference adaptive controller is developed, which requires no prior knowledge of system parameters and can specify desired closed-loop torque characteristics. Second, the time-domain passivity approach is modified to control desired impedances in a manner that temporarily allows the SEA to passively render impedances greater than the actuator's intrinsic stiffness. This approach also provides conditions for passivity when augmenting any stable SEA torque controller with an arbitrary impedance. The resultant techniques are experimentally validated on a custom prototype SEA.

VL - 21 UR - http://ieeexplore.ieee.org/abstract/document/7457670/ ER - TY - JOUR T1 - Interaction control capabilities of an MR-compatible compliant actuator for wrist sensorimotor protocols during fMRI JF - IEEE/ASME Transactions on Mechatronics Y1 - 2015 A1 - Fabrizio Sergi A1 - Andrew Erwin A1 - Marcia K. O'Malley KW - compliant actuators. KW - Force control KW - functional MRI (fMRI) KW - MR-compatible robotics AB -

This paper describes the mechatronic design and characterization of a novel MR-compatible actuation system designed for a parallel force-feedback exoskeleton for measurement and/or assistance of wrist pointing movements during functional neuroimaging. The developed actuator is based on the interposition of custom compliant elements in series between a non-backdrivable MR-compatible ultrasonic piezoelectric motor and the actuator output. The inclusion of physical compliance allows estimation of interaction force, enabling force-feedback control and stable rendering of a wide range of haptic environments during continuous scanning. Through accurate inner-loop

velocity compensation and force-feedback control, the actuator is capable of displaying both a low-impedance, subject-in-charge mode, and a high stiffness mode. These modes enable the execution of shared haptic protocols during continuous fMRI. 

The detailed experimental characterization of the actuation system is presented, including a backdrivability analysis, demonstrating an achievable impedance range of 22 dB, within a bandwidth of 4 Hz (for low stiffness). The stiffness control bandwidth depends on the specific value of stiffness: a bandwidth of 4 Hz is achieved at low stiffness (10% of the physical springs stiffness), while 8 Hz is demonstrated at higher stiffness. Moreover, coupled stability is demonstrated also for stiffness values substantially (25%) higher than the physical stiffness of the spring. Finally, compatibility tests conducted in a 3T scanner are presented, validating the potential of inclusion of the actuator in an exoskeleton system for support of wrist movements during continuous MR scanning, without significant reduction in image quality.

VL - 20 ER - TY - Generic T1 - Compliant force-feedback actuation for accurate robot-mediated sensorimotor interaction protocols during fMRI T2 - International Conference on Biomedical Robotics and Biomechatronics (BioRob) Y1 - 2014 A1 - Fabrizio Sergi A1 - Andrew Erwin A1 - Brian Cera A1 - Marcia K. O'Malley JF - International Conference on Biomedical Robotics and Biomechatronics (BioRob) PB - IEEE ER - TY - JOUR T1 - Vary Slow Motion: Effect of Task Forces on Movement Variability and Implications for a Novel Skill Augmentation Mechanism JF - IEEE Robotics and Automation Magazine Y1 - 2014 A1 - Ozkan Celik A1 - Marcia K. O'Malley ER - TY - Generic T1 - Design of a series elastic actuator for a compliant parallel wrist rehabilitation robot T2 - International Conference on Rehabilitation Robotics Y1 - 2013 A1 - Fabrizio Sergi A1 - Melissa M. Lee A1 - Marcia K. O'Malley JF - International Conference on Rehabilitation Robotics ER - TY - Generic T1 - Interaction control for rehabilitation robotics via a low-cost force sensing handle T2 - 6th Annual ASME Dynamic Systems and Controls Conference Y1 - 2013 A1 - Andrew Erwin A1 - Fabrizio Sergi A1 - Vinay Chawda A1 - Marcia K. O'Malley JF - 6th Annual ASME Dynamic Systems and Controls Conference CY - Palo Alto, CA ER - TY - Generic T1 - A Method for Selecting Velocity Filter Cutoff Frequency for Maximizing Impedance Width Performance in Haptic Interfaces T2 - 6th Annual ASME Dynamic Systems and Controls Conference Y1 - 2013 A1 - Vinay Chawda A1 - Ozkan Celik A1 - Marcia K. O'Malley JF - 6th Annual ASME Dynamic Systems and Controls Conference CY - Palo Alto, CA ER - TY - Generic T1 - Modeling Basic Aspects of Cyber-Physical Systems, Part II T2 - The Fourth International Workshop on Domain-Specific Languages and Models for Robotic Systems (DSLRob'13) Y1 - 2013 A1 - Yingfu Zeng A1 - Rose, Chad G. A1 - Paul Branner A1 - Walid Taha A1 - Jawad Masood A1 - Roland Philippsen A1 - Marcia K. O'Malley A1 - Robert Cartwright AB -
We continue to consider the question of what
language features are needed to effectively model cyber-physical
systems (CPS). In previous work, we proposed using a core
language as a way to study this question, and showed how
several basic aspects of CPS can be modeled clearly in a
language with a small set of constructs. This paper reports
on the result of our analysis of two, more complex, case studies
from the domain of rigid body dynamics. The first one, a
quadcopter, illustrates that previously proposed core language
can support larger, more interesting systems than previously
shown. The second one, a serial robot, provides a concrete
example of why we should add language support for static
partial derivatives, namely that it would significantly improve
the way models of rigid body dynamics can be expressed.
JF - The Fourth International Workshop on Domain-Specific Languages and Models for Robotic Systems (DSLRob'13) CY - Tokyo, Japan ER - TY - Generic T1 - Reconstructing Surface EMG from Scalp EEG during Myoelectric Control of a Closed Looped Prosthetic Device T2 - IEEE Engineering in Medicine and Biology Conference Y1 - 2013 A1 - Andrew Y. Paek A1 - Jeremy D. Brown A1 - R. B. Gillespie A1 - Marcia K. O'Malley A1 - Patricia A. Shewokis A1 - Jose L. Contreras-Vidal JF - IEEE Engineering in Medicine and Biology Conference ER - TY - Generic T1 - Understanding the Role of Haptic Feedback in a Teleoperated Grasp and Lift Task T2 - International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems and the Fifth Joint World Haptics Conference (HAPTICS) Y1 - 2013 A1 - Jeremy D. Brown A1 - Andrew Paek A1 - Mashaal Syed A1 - Marcia K. O'Malley A1 - Patricia Shewokis A1 - Jose L. Contreras-Vidal A1 - R. B. Gillespie AB -

Achieving dexterous volitional control of an upper-limb prosthetic device will require multimodal sensory feedback that goes beyond vision. Haptic display is well-positioned to provide this additional sensory information. Haptic display, however, includes a diverse set of modalities that encode information differently. We have begun to make a comparison between two of these modalities, force feedback spanning the elbow, and amplitude-modulated vibrotactile feedback, based on performance in a functional grasp and lift task. In randomly ordered trials, we assessed the performance of N=11 participants (8 able-bodied, 3 amputee) attempting to grasp and lift an object using an EMG controlled gripper under three feedback conditions (no feedback, vibrotactile feedback, and force feed-back), and two object weights that were undetectable by vision. Preliminary results indicate differences between able-bodied and amputee participants in coordination of grasp and lift forces. In addition, both force feedback and vibrotactile feedback contribute to significantly better task performance (fewer slips) and better adaptation following an unpredicted weight change. This suggests that the development and utilization of internal models for predictive control is more intuitive in the presence of haptic feedback.

JF - International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems and the Fifth Joint World Haptics Conference (HAPTICS) ER - TY - Generic T1 - Vibrotactile Feedback of Pose Error Enhances Myoelectric Control of a Prosthetic Hand T2 - International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems and the Fifth Joint World Haptics Conference (HAPTICS) Y1 - 2013 A1 - Ryan Christiansen A1 - Jose Luis Contreras-Vidal A1 - R B Gillespie A1 - Patricia Shewokis A1 - Marcia K. O'Malley JF - International Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems and the Fifth Joint World Haptics Conference (HAPTICS) ER - TY - Generic T1 - On the Performance of Passivity-based Control of Haptic Displays Employing Levant's Differentiator for Velocity Estimation T2 - IEEE Haptics Symposium Y1 - 2012 A1 - Vinay Chawda A1 - Marcia K. O'Malley AB -

In impedance-type haptic interfaces, encoders are typically employed to provide high resolution position measurements from which velocity is estimated, most commonly via the finite difference method (FDM). This velocity estimation technique performs reliably, unless very fast sampling is required, in which case noise or delay due to filtering of the position signals reduces accuracy in the estimate. Despite this limitation, FDM is attractive because it is a passive process, and therefore the passivity of the overall system can be guaranteed. Levant's differentiator is a viable alternative to FDM, and exhibits increased accuracy in velocity estimation at high sample rates compared to FDM. However, the passivity of this nonlinear velocity estimation technique cannot be shown using conventional methods. In this paper, we employ a time domain passivity framework to analyze and enforce passive behavior of Levant's differentiator for haptic displays in discrete time. The performance of this approach is explored both in simulation and experimentally on a custom made one degree-of-freedom haptic interface. Results demonstrate the effectiveness of the time domain passivity approach for compensating the active behavior observed with use of Levant's differentiator for velocity estimation.

JF - IEEE Haptics Symposium PB - IEEE CY - Vancouver, BC, Canada SN - 978-1-4673-0808-3 ER - TY - Generic T1 - The RiceWrist Grip: A Means to Measure Grip Strength of Patients Using the RiceWrist Y1 - 2012 A1 - Ryan Quincy A1 - Andrew Erwin A1 - A.U. Pehlivan A1 - Yozbatiran, Nuray A1 - Gerard Francisco A1 - Marcia K. O'Malley ER - TY - JOUR T1 - The Task-Dependent Efficacy of Shared-Control Haptic Guidance Paradigms JF - {IEEE} Transactions on Haptics Y1 - 2012 A1 - Powell, Dane A1 - Marcia K. O'Malley AB -

Shared-control haptic guidance is a common form of robot-mediated training used to teach novice subjects to perform dynamic tasks. Shared-control guidance is distinct from more traditional guidance controllers, such as virtual fixtures, in that it provides novices with real-time visual and haptic feedback from a real or virtual expert. Previous studies have shown varying levels of training efficacy using shared-control guidance paradigms; it is hypothesized that these mixed results are due to interactions between specific guidance implementations ( {amp;\#x201C;paradigms} {amp;\#x201D;)} and tasks. This work proposes a novel guidance paradigm taxonomy intended to help classify and compare the multitude of implementations in the literature, as well as a revised proxy rendering model to allow for the implementation of more complex guidance paradigms. The efficacies of four common paradigms are compared in a controlled study with 50 healthy subjects and two dynamic tasks. The results show that guidance paradigms must be matched to a task's dynamic characteristics to elicit effective training and low workload. Based on these results, we provide suggestions for the future development of improved haptic guidance paradigms.

VL - 5 ER - TY - Generic T1 - Application of Levant’s Differentiator for Velocity Estimation and Increased Z-Width in Haptic Interfaces T2 - IEEE World Haptics Conference 2011 Y1 - 2011 A1 - Vinay Chawda A1 - Ozkan Celik A1 - Marcia K. O'Malley JF - IEEE World Haptics Conference 2011 PB - IEEE CY - Istanbul, Turkey ER - TY - Generic T1 - A Lyapunov Approach for SOSM Based Velocity Estimation and its Application to Improve Bilateral Teleoperation Performance T2 - Proceedings of the ASME 2011 Dynamic Systems and Control Conference Y1 - 2011 A1 - Vinay Chawda A1 - Marcia K. O'Malley JF - Proceedings of the ASME 2011 Dynamic Systems and Control Conference ER - TY - JOUR T1 - Disturbance observer-based force estimation for haptic feedback JF - ASME Journal of Dynamic Systems, Measurement and Control Y1 - 2010 A1 - Abhishek Gupta A1 - Marcia K. O'Malley VL - 133 ER -