D. Reinkensmeyer

发表

Using associative content-addressable memories to control robots

David J. Reinkensmeyer, Christopher G. Atkeson, C. Atkeson, 1989, Proceedings, 1989 International Conference on Robotics and Automation.

Using associative content-addressable memories to control robots

David J. Reinkensmeyer, Christopher G. Atkeson, C. Atkeson, 1988, Proceedings of the 27th IEEE Conference on Decision and Control.

Model-based robot learning

David J. Reinkensmeyer, Christopher G. Atkeson, Joseph McIntyre, 1988 .

Task-level robot learning

David J. Reinkensmeyer, Christopher G. Atkeson, Eric W. Aboaf, 1988, Proceedings. 1988 IEEE International Conference on Robotics and Automation.

Arm-Training with T-WREX After Chronic Stroke: Preliminary Results of a Randomized Controlled Trial

D. Reinkensmeyer, T. Rahman, S. Housman, 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

Comparison of Robot-Assisted Reaching to Free Reaching in Promoting Recovery From Chronic Stroke

W. Rymer, D. Reinkensmeyer, L. Kahn, 2001 .

Web-Based Telerehabilitation for the Upper

D. Reinkensmeyer, C. T. Pang, J. Nessler, 2002 .

Effect of robot-assisted and unassisted exercise on functional reaching in chronic hemiparesis

W. Rymer, D. Reinkensmeyer, L. Kahn, 2001, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Robotic devices for physical rehabilitation of stroke patients: fundamental requirements, target therapeutic techniques, and preliminary designs

W. Rymer, D. Reinkensmeyer, J. Dewald, 1996 .

Mechatronic assessment of arm impairment after chronic brain injury.

W. Rymer, D. Reinkensmeyer, B. Schmit, 1999, Technology and health care : official journal of the European Society for Engineering and Medicine.

Guidance-based quantification of arm impairment following brain injury: a pilot study.

W. Rymer, D. Reinkensmeyer, J. Dewald, 1999, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

Understanding and treating arm movement impairment after chronic brain injury: progress with the ARM guide.

W. Rymer, D. Reinkensmeyer, L. Kahn, 2014, Journal of rehabilitation research and development.

Review of control strategies for robotic movement training after neurologic injury

D. Reinkensmeyer, L. Marchal-Crespo, 2009, Journal of NeuroEngineering and Rehabilitation.

Impedance control and internal model formation when reaching in a randomly varying dynamical environment.

R A Scheidt, D J Reinkensmeyer, C D Takahashi, 2001, Journal of neurophysiology.

Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: a randomized controlled pilot study

W. Rymer, D. Reinkensmeyer, L. Kahn, 2006, Journal of NeuroEngineering and Rehabilitation.

Swinging from the hip: use of dynamic motion optimization in the design of robotic gait rehabilitation

David J. Reinkensmeyer, James E. Bobrow, Chia-Yu E. Wang, 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

The bimanual lifting rehabilitator: an adaptive machine for therapy of stroke patients

S. P. Lum, S. L. Lehman, D. J. Reinkensmeyer, 1995 .

An assistive robotic device that can synchronize to the pelvic motion during human gait training

S.J. Harkema, D.J. Reinkensmeyer, D. Aoyagi, 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

Retraining the injured spinal cord

S. Harkema, V. Edgerton, D. Reinkensmeyer, 2001, The Journal of physiology.

Adaptive assistance for guided force training in chronic stroke

W. Rymer, D. Reinkensmeyer, L. Kahn, 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Robotic Devices for Movement Therapy After Stroke: Current Status and Challenges to Clinical Acceptance

W. Rymer, D. Reinkensmeyer, C. Burgar, 2002, Topics in stroke rehabilitation.

Rehabilitators, Robots, and Guides: New Tools for Neurological Rehabilitation

N. Hogan, D. Reinkensmeyer, H. Krebs, 2000 .

Controlling pre-movement sensorimotor rhythm can improve finger extension after stroke

J. Wolpaw, D. McFarland, D. Reinkensmeyer, 2018, Journal of neural engineering.

Automating Arm Movement Training Following Severe Stroke: Functional Exercises With Quantitative Feedback in a Gravity-Reduced Environment

D.J. Reinkensmeyer, Jiayin Liu, S.C. Cramer, 2006, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Emerging Technologies for Improving Access to Movement Therapy following Neurologic Injury

D. Reinkensmeyer, J. Winters, C. Robinson, 2002 .

Assessment of Active and Passive Restraint During Guided Reaching After Chronic Brain Injury

W. Rymer, D. Reinkensmeyer, B. Schmit, 1999, Annals of Biomedical Engineering.

Journal of NeuroEngineering and Rehabilitation reviewer acknowledgement 2015

D. Reinkensmeyer, 2016, Journal of NeuroEngineering and Rehabilitation.

Robotics for Gait Training After Spinal Cord Injury

D. Reinkensmeyer, J. Galvez, 2005 .

Effect of visual distraction and auditory feedback on patient effort during robot-assisted movement training after stroke

D. Reinkensmeyer, M. Milot, G. Rosati, 2011, Journal of NeuroEngineering and Rehabilitation.

Substituting auditory for visual feedback to adapt to altered dynamic and kinematic environments during reaching

D. Reinkensmeyer, F. Avanzini, G. Rosati, 2012, Experimental Brain Research.

A Standardized Approach to the Fugl-Meyer Assessment and Its Implications for Clinical Trials

Cathy C. Y. Chou, D. Reinkensmeyer, S. Cramer, 2013, Neurorehabilitation and neural repair.

Neural Correlates of Passive Position Finger Sense After Stroke

Vicky Chan, David J. Reinkensmeyer, Eric T. Wolbrecht, 2019, Neurorehabilitation and neural repair.

A Home-Based Telerehabilitation Program for Patients With Stroke

Lucy Dodakian, Steven C Cramer, Nizan Friedman, 2017, Neurorehabilitation and neural repair.

The rat lumbosacral spinal cord adapts to robotic loading applied during stance.

V R Edgerton, R R Roy, D J Reinkensmeyer, 2002, Journal of neurophysiology.

Robotic Rehabilitation: Ten Critical Questions about Current Status and Future Prospects Answered by Emerging Researchers

A. J. del-Ama, D. Reinkensmeyer, Vijaykumar Rajasekaran, 2014 .

Bimanual wheelchair propulsion by people with severe hemiparesis after stroke

Brendan W. Smith, D. Reinkensmeyer, L. Montano, 2019, Disability and rehabilitation. Assistive technology.

Title Computational neurorehabilitation : modeling plasticity and learning to predict recovery Permalink

J. Krakauer, S. Swinnen, D. Reinkensmeyer, 2016 .

Computational neurorehabilitation: modeling plasticity and learning to predict recovery

J. Krakauer, S. Swinnen, D. Reinkensmeyer, 2016, Journal of NeuroEngineering and Rehabilitation.

Retraining and assessing hand movement after stroke using the MusicGlove: comparison with conventional hand therapy and isometric grip training

Andrea N. Reinkensmeyer, D. Reinkensmeyer, M. Bachman, 2014, Journal of NeuroEngineering and Rehabilitation.

Alterations in reaching after stroke and their relation to movement direction and impairment severity.

D. Reinkensmeyer, D. Kamper, L. Kahn, 2002, Archives of physical medicine and rehabilitation.

A bimanual reflex during two hand grasp

D. Reinkensmeyer, P. Lum, S. Lehman, 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

Java Therapy : Web-Based Robotic Rehabilit ation

D. Reinkensmeyer, J. Nessler, C. Painter, 2001 .

Locomotor ability in spinal rats is dependent on the amount of activity imposed on the hindlimbs during treadmill training.

V. Edgerton, D. Reinkensmeyer, R. Roy, 2007, Journal of neurotrauma.

Robot-assisted movement training for the stroke-impaired arm: Does it matter what the robot does?

W. Rymer, D. Reinkensmeyer, L. Kahn, 2006, Journal of rehabilitation research and development.

Can Robots Help the Learning of Skilled Actions?

J. Patton, D. Reinkensmeyer, 2009, Exercise and sport sciences reviews.

Directional control of reaching is preserved following mild/moderate stroke and stochastically constrained following severe stroke

D. Reinkensmeyer, D. Kamper, L. Kahn, 2002, Experimental Brain Research.

A Randomized Controlled Trial of Gravity-Supported, Computer-Enhanced Arm Exercise for Individuals With Severe Hemiparesis

Sarah J. Housman, D. Reinkensmeyer, S. Housman, 2009, Neurorehabilitation and neural repair.

Technologies and combination therapies for enhancing movement training for people with a disability

D. Reinkensmeyer, M. Boninger, 2012, Journal of NeuroEngineering and Rehabilitation.

The effect of haptic guidance, aging, and initial skill level on motor learning of a steering task

D. Reinkensmeyer, S. Cramer, L. Marchal-Crespo, 2009, Experimental Brain Research.

Effects of robotically modulating kinematic variability on motor skill learning and motivation.

Jaime E. Duarte, D. Reinkensmeyer, 2015, Journal of neurophysiology.

Human-robot cooperative movement training: Learning a novel sensory motor transformation during walking with robotic assistance-as-needed

D. Reinkensmeyer, J. Emken, Raul Benitez, 2007, Journal of NeuroEngineering and Rehabilitation.

JNER at 15 years: analysis of the state of neuroengineering and rehabilitation

D. Reinkensmeyer, 2019, Journal of NeuroEngineering and Rehabilitation.

Finger strength, individuation, and their interaction: Relationship to hand function and corticospinal tract injury after stroke

Vicky Chan, David J. Reinkensmeyer, Eric T. Wolbrecht, 2018, Clinical Neurophysiology.

Title Corticospinal Excitability as a Predictor of Functional Gains at the Affected Upper Limb Following Robotic Training in Chronic Stroke

Cathy C. Y. Chou, J. Bobrow, D. Reinkensmeyer, 2014 .

A crossover pilot study evaluating the functional outcomes of two different types of robotic movement training in chronic stroke survivors using the arm exoskeleton BONES

Vicky Chan, Cathy C. Y. Chou, Steven C Cramer, 2013, Journal of NeuroEngineering and Rehabilitation.

Hemiparetic stroke impairs anticipatory control of arm movement

David J. Reinkensmeyer, Craig D. Takahashi, D. Reinkensmeyer, 2003, Experimental Brain Research.

Comparison of Three-Dimensional, Assist-as-Needed Robotic Arm/Hand Movement Training Provided with Pneu-WREX to Conventional Tabletop Therapy After Chronic Stroke

Vicky Chan, Cathy C. Y. Chou, Steven C Cramer, 2012, American journal of physical medicine & rehabilitation.

How a diverse research ecosystem has generated new rehabilitation technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Levin J. Sliker, Jordana L. Maisel, J. Patton, 2017, Journal of NeuroEngineering and Rehabilitation.

Big Data Analytics and Sensor-Enhanced Activity Management to Improve Effectiveness and Efficiency of Outpatient Medical Rehabilitation

John T. Morris, D. Reinkensmeyer, F. DeRuyter, 2020, International journal of environmental research and public health.

A novel device for studying weight supported, quadrupedal overground locomotion in spinal cord injured rats

David J. Reinkensmeyer, Ray D. de Leon, D. Reinkensmeyer, 2015, Journal of Neuroscience Methods.

Hindlimb loading determines stepping quantity and quality following spinal cord transection

David J. Reinkensmeyer, V. Reggie Edgerton, Roland R. Roy, 2005, Brain Research.

Spinal Cord-Transected Mice Learn to Step in Response to Quipazine Treatment and Robotic Training

Roland R Roy, V Reggie Edgerton, David J Reinkensmeyer, 2005, The Journal of Neuroscience.

Robotic gait analysis of bipedal treadmill stepping by spinal contused rats: characterization of intrinsic recovery and comparison with BBB.

D. Reinkensmeyer, R. D. de Leon, J. Nessler, 2006, Journal of neurotrauma.

Design and preliminary evaluation of the FINGER rehabilitation robot: controlling challenge and quantifying finger individuation during musical computer game play

Justin B. Rowe, D. Reinkensmeyer, E. Wolbrecht, 2014, Journal of NeuroEngineering and Rehabilitation.

Somatosensory system integrity explains differences in treatment response after stroke

Vicky Chan, Steven C Cramer, David J Reinkensmeyer, 2019, Neurology.

Breaking Proportional Recovery After Stroke

D. Reinkensmeyer, M. Senesh, 2019, Neurorehabilitation and neural repair.

Robotic Assistance for Training Finger Movement Using a Hebbian Model: A Randomized Controlled Trial

Vicky Chan, Justin B. Rowe, Steven C Cramer, 2017, Neurorehabilitation and neural repair.

Use of a robotic device to measure age-related decline in finger proprioception

Vicky Chan, David J. Reinkensmeyer, Eric T. Wolbrecht, 2015, Experimental Brain Research.

Robotic Rehabilitator of the Rodent Upper Extremity: A System and Method for Assessing and Training Forelimb Force Production after Neurological Injury.

Jaime E. Duarte, O. Steward, D. Reinkensmeyer, 2016, Journal of neurotrauma.

Movement Anticipation and EEG: Implications for BCI-Contingent Robot Therapy

David J. Reinkensmeyer, Steven C. Cramer, Ramesh Srinivasan, 2016, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Trainer variability during step training after spinal cord injury: Implications for robotic gait-training device design.

David J Reinkensmeyer, Susan J Harkema, S. Harkema, 2011, Journal of rehabilitation research and development.

Manually‐Assisted Versus Robotic‐Assisted Body Weight−Supported Treadmill Training in Spinal Cord Injury: What Is the Role of Each?

David J Reinkensmeyer, D. Reinkensmeyer, T. Hornby, 2010, PM & R : the journal of injury, function, and rehabilitation.

Quantification of Therapists' Manual Assistance on the Leg during Treadmill Gait Training with Partial Body-Weight Support after Spinal Cord Injury

S. Harkema, D. Reinkensmeyer, J. Galvez, 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Comparison of 3D, Assist-as-Needed Robotic Arm/Hand Movement Training Provided with Pneu-WREX to Conventional Table Top Therapy Following Chronic Stroke

Vicky Chan, David J. Reinkensmeyer, Eric T. Wolbrecht, 2013 .

Upper-Extremity Therapy with Spring Orthoses

D. Reinkensmeyer, D. Zondervan, 2016 .

Universal Access to Participatory Musical Experiences for People with Disabilities

David J. Reinkensmeyer, Mark Bachman, Nizan Friedman, 2013, HCI.

Accelerating motor adaptation by influencing neural computations

D. Reinkensmeyer, J. Emken, J.L. Emken, 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Feasibility of Wearable Sensing for In-Home Finger Rehabilitation Early After Stroke

An H. Do, D. Reinkensmeyer, S. Cramer, 2020, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

A Pilot Study of a Sensor Enhanced Activity Management System for Promoting Home Rehabilitation Exercise Performed during the COVID-19 Pandemic: Therapist Experience, Reimbursement, and Recommendations for Implementation

D. Reinkensmeyer, V. Chan, D. Zondervan, 2021, International journal of environmental research and public health.

Robotic assistance for upper extremity training after stroke.

D. Reinkensmeyer, 2009, Studies in health technology and informatics.

Using Large-Scale Sensor Data to Test Factors Predictive of Perseverance in Home Movement Rehabilitation: Optimal Challenge and Steady Engagement

D. Reinkensmeyer, Amanda R. Rabinowitz, D. Zondervan, 2022, Frontiers in Neurology.

Evaluation of an exercise-enabling control interface for powered wheelchair users: a feasibility study with Duchenne muscular dystrophy

J. M. Font-Llagunes, C. Torras, D. Reinkensmeyer, 2020, Journal of neuroengineering and rehabilitation.

2nd Workshop on upper-extremity assistive technology for people with Duchenne: Effectiveness and usability of arm supports Irvine, USA, 22nd–23rd January 2018

D. Reinkensmeyer, T. Rahman, R. Foulds, 2019, Neuromuscular Disorders.

Robot-assisted hindlimb extension increases the probability of swing initiation during treadmill walking by spinal cord contused rats

D. Reinkensmeyer, J. Nessler, K. Sharp, 2007, Journal of Neuroscience Methods.

A Dynamic Wheelchair Armrest for Promoting Arm Exercise and Mobility After Stroke

D. Reinkensmeyer, V. Chan, D. Zondervan, 2022, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Providing Real-Time Wearable Feedback to Increase Hand Use after Stroke: A Randomized, Controlled Trial

Justin B. Rowe, D. Reinkensmeyer, S. Cramer, 2022, Sensors.

Neurorehabilitation Technology

V. Dietz, D. Reinkensmeyer, 2022 .

Time flies when you are in a groove: using entrainment to mechanical resonance to teach a desired movement distorts the perception of the movement’s timing

Jaime E. Duarte, Justin B. Rowe, D. Reinkensmeyer, 2014, Experimental Brain Research.

Robotics, motor learning, and neurologic recovery.

Steven C Cramer, David J Reinkensmeyer, Jeremy L Emken, 2004, Annual review of biomedical engineering.

JNER at 15 years: analysis of the state of neuroengineering and rehabilitation

D. Reinkensmeyer, 2019, Journal of neuroengineering and rehabilitation.

Rehabilitation and Health Care Robotics

David J. Reinkensmeyer, H. F. Machiel Van der Loos, D. Reinkensmeyer, 2008, Springer Handbook of Robotics, 2nd Ed..

Judging complex movement performances for excellence: a principal components analysis-based technique applied to competitive diving.

David J Reinkensmeyer, Cole Young, Cole R. Young, 2014, Human movement science.

Neural circuits activated by error amplification and haptic guidance training techniques during performance of a timing-based motor task by healthy individuals

Laura Marchal-Crespo, Steven C Cramer, Marie-Hélène Milot, 2018, Experimental Brain Research.

Comparison of error-amplification and haptic-guidance training techniques for learning of a timing-based motor task by healthy individuals

David J. Reinkensmeyer, Steven C. Cramer, Laura Marchal-Crespo, 2010, Experimental Brain Research.

Journal of Neuroengineering and Rehabilitation Open Access Human-machine Interfaces Based on Emg and Eeg Applied to Robotic Systems

D. Reinkensmeyer, J. Emken, Raul Benitez, 2004 .

Corticospinal Excitability as a Predictor of Functional Gains at the Affected Upper Limb Following Robotic Training in Chronic Stroke Survivors

Vicky Chan, Cathy C. Y. Chou, Steven C Cramer, 2014, Neurorehabilitation and neural repair.

Dynamic Motion Planning For Robot Manipulators Using B-Splines

David J. Reinkensmeyer, Chia-Yu Eric Wang, A. Sideris, 2001 .

Human control of a simple two-hand grasp

David J. Reinkensmeyer, Peter S. Lum, Steven L. Lehman, 1992, Biological Cybernetics.

Persistence of motor adaptation during constrained, multi-joint, arm movements.

R A Scheidt, F A Mussa-Ivaldi, M A Conditt, 2000, Journal of neurophysiology.

Real-time slacking as a default mode of grip force control: implications for force minimization and personal grip force variation.

Brendan W. Smith, Justin B. Rowe, David J Reinkensmeyer, 2018, Journal of neurophysiology.

Robot-assisted Guitar Hero for finger rehabilitation after stroke

Vicky Chan, David J. Reinkensmeyer, David Gardner, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

A day in the life: a qualitative study of clinical decision-making and uptake of neurorehabilitation technology

J. Patton, D. Reinkensmeyer, M. Rafferty, 2021, Journal of neuroengineering and rehabilitation.

Real-Time Decision-Making on Whether to Incorporate Neurorehabilitation Technology: Informing Developers on a Day in the Life of a Therapist

J. Patton, D. Reinkensmeyer, M. Rafferty, 2021 .

Development and Evaluation of MOVit: An Exercise-Enabling Interface for Driving a Powered Wheelchair

David J. Reinkensmeyer, Joan Lobo-Prat, Nariman Sharifrazi, 2019, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Modeling Reaching Impairment After Stroke Using a Population Vector Model of Movement Control That Incorporates Neural Firing-Rate Variability

Derek G. Kamper, David J. Reinkensmeyer, Mario G. Iobbi, 2003, Neural Computation.

Robot-Assisted Rehabilitation Therapy: Recovery Mechanisms and Their Implications for Machine Design

David J. Reinkensmeyer, Laura De Rijcke, Serena Maggioni, 2016 .

Neuromotor noise limits motor performance, but not motor adaptation, in children.

Dan Nemet, David J Reinkensmeyer, Craig D Takahashi, 2003, Journal of neurophysiology.

Feasibility of Manual Teach-and-Replay and Continuous Impedance Shaping for Robotic Locomotor Training Following Spinal Cord Injury

David J. Reinkensmeyer, Susan J. Harkema, Jeremy L. Emken, 2008, IEEE Transactions on Biomedical Engineering.

A Robot and Control Algorithm That Can Synchronously Assist in Naturalistic Motion During Body-Weight-Supported Gait Training Following Neurologic Injury

S.J. Harkema, D.J. Reinkensmeyer, D. Aoyagi, 2007, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Robotic movement training as an optimization problem: designing a controller that assists only as needed

D.J. Reinkensmeyer, J.L. Emken, J.E. Bobrow, 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification

D.J. Reinkensmeyer, J.L. Emken, D. Reinkensmeyer, 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Design and Control of a Novel Grip Amplifier to Support Pinch Grip with a Minimal Soft Hand Exoskeleton

Brendan W. Smith, David J. Reinkensmeyer, Joan Lobo-Prat, 2018, 2018 7th IEEE International Conference on Biomedical Robotics and Biomechatronics (Biorob).

UNIVERSITY OF CALIFORNIA, IRVINE Grip Amplifier: A Residual Force Control Strategy to Support Pinch Grip with a Minimalistic Hand Exoskeleton THESIS submitted in partial satisfaction of the requirements for the degree of MASTER OF SCIENCE in Mechanical and Aerospace Engineering

D. Reinkensmeyer, F. Jabbari, Quentin Sanders, 2018 .

Home-based hand rehabilitation after chronic stroke: Randomized, controlled single-blind trial comparing the MusicGlove with a conventional exercise program.

Steven C Cramer, Nizan Friedman, David J Reinkensmeyer, 2016, Journal of rehabilitation research and development.

Machine-Based, Self-guided Home Therapy for Individuals With Severe Arm Impairment After Stroke

Steven C Cramer, Nizan Friedman, David J Reinkensmeyer, 2015, Neurorehabilitation and neural repair.

Simple Mechanical Constraint to Single-Joint Arm Movements Studied Using a Control Strategies for the Transition From Multijoint

David J. Reinkensmeyer, Ferdinando A. Mussa-Ivaldi, Robert A. Scheidt, 2015 .

Impaired Motor Control in Patients With Benign Focal Epilepsy of Childhood

D. Reinkensmeyer, C. Takahashi, J. Rho, 2006, Journal of child neurology.

Optimized Home Rehabilitation Technology Reduces Upper Extremity Impairment Compared to a Conventional Home Exercise Program: A Randomized, Controlled, Single-Blind Trial in Subacute Stroke

D. Reinkensmeyer, D. Zondervan, Christopher Johnson, 2023, Neurorehabilitation and neural repair.

Assessment of Infant Movement With a Compact Wireless Accelerometer System

K. Goldberg, David J. Reinkensmeyer, Pai H. Chou, 2012 .

Robotic assist devices for bimanual physical therapy: preliminary experiments

David J. Reinkensmeyer, Peter S. Lum, Steven L. Lehman, 1993 .

A robotic wheelchair trainer: design overview and a feasibility study

Laura Marchal-Crespo, David J Reinkensmeyer, Jan Furumasu, 2010, Journal of NeuroEngineering and Rehabilitation.

Haptic Guidance Can Enhance Motor Learning of a Steering Task

David J. Reinkensmeyer, Laura Marchal Crespo, Laura Marchal Crespo, 2008, Journal of motor behavior.

There is plenty of room for motor learning at the bottom of the Fugl-Meyer: Acquisition of a novel bimanual wheelchair skill after chronic stroke using an unmasking technology

Brendan W. Smith, Vicky Chan, David J. Reinkensmeyer, 2017, 2017 International Conference on Rehabilitation Robotics (ICORR).

Designing Robots That Challenge to Optimize Motor Learning

Jaime E. Duarte, David A. Brown, David J. Reinkensmeyer, 2016 .

Effort, performance, and motivation: Insights from robot-assisted training of human golf putting and rat grip strength

Jaime E. Duarte, David J. Reinkensmeyer, Justin B. Rowe, 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

Biomimetic orthosis for the neurorehabilitation of the elbow and shoulder (BONES)

D.J. Reinkensmeyer, R. Smith, J.E. Bobrow, 2008, 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

MusicGlove: Motivating and quantifying hand movement rehabilitation by using functional grips to play music

Vicky Chan, David J. Reinkensmeyer, Mark Bachman, 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Optimizing Compliant, Model-Based Robotic Assistance to Promote Neurorehabilitation

D.J. Reinkensmeyer, J.E. Bobrow, E.T. Wolbrecht, 2008, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Using robotics to teach the spinal cord to walk

Ray D de Leon, Marc D Kubasak, Patricia E Phelps, 2002, Brain Research Reviews.

Chapter 11 Use of robotics in assessing the adaptive capacity of the rat lumbar spinal cord

David J. Reinkensmeyer, V. Reggie Edgerton, Roland R. Roy, 2002 .

Using Sound feedback to counteract visual distractor during robot-assisted movement training

David J. Reinkensmeyer, Giulio Rosati, Riccardo Secoli, 2009, 2009 IEEE International Workshop on Haptic Audio visual Environments and Games.

Rudimentary Dexterity Corresponds With Reduced Ability to Move in Synergy After Stroke: Evidence of Competition Between Corticoreticulospinal and Corticospinal Tracts?

D. Reinkensmeyer, M. Senesh, Karina Barragán, 2020, Neurorehabilitation and neural repair.

A computational model of use-dependent motor recovery following a stroke: Optimizing corticospinal activations via reinforcement learning can explain residual capacity and other strength recovery dynamics

David J. Reinkensmeyer, Emmanuel Guigon, Marc A. Maier, 2012, Neural Networks.

Measuring human trainers' skill for the design of better robot control algorithms for gait training after spinal cord injury

J.A. Galvez, G. Kerdanyan, S. Maneekobkunwong, 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

Dissociating motor learning from recovery in exoskeleton training post-stroke

D. Reinkensmeyer, N. Schweighofer, Denis Mottet, 2018, Journal of NeuroEngineering and Rehabilitation.

Improving robotics for neurorehabilitation: Enhancing engagement, performance, and learning with auditory feedback

David J. Reinkensmeyer, Simone Spagnol, Federico Avanzini, 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

Real-time computer modeling of weakness following stroke optimizes robotic assistance for movement therapy

D.J. Reinkensmeyer, S.C. Cramer, J.E. Bobrow, 2007, 2007 3rd International IEEE/EMBS Conference on Neural Engineering.

Facilitating limb movement after stroke

Andria J. Farrens, D. Reinkensmeyer, D. Kamper, 2023, Nature Medicine.

The Badges Program: A Self-Directed Learning Guide for Residents for Conducting Research and a Successful Peer-Reviewed Publication

D. Reinkensmeyer, Danielle Perret Karimi, K. Onishi, 2016, MedEdPORTAL : the journal of teaching and learning resources.

Motor adaptation as a greedy optimization of error and effort.

Raul Benitez, Athanasios Sideris, David J Reinkensmeyer, 2007, Journal of neurophysiology.

Multi-Joint, Arm Movements Persistence of Motor Adaptation During Constrained,

David J. Reinkensmeyer, Reza Shadmehr, John W. Krakauer, 2015 .

Pneumatic Control of Robots for Rehabilitation

David J. Reinkensmeyer, Eric T. Wolbrecht, James E. Bobrow, 2010, Int. J. Robotics Res..

Targeting Neuroplasticity to Improve Motor Recovery after Stroke

David J. Reinkensmeyer, Sumner L. Norman, Jonathan R. Wolpaw, 2020, bioRxiv.

Gesture Therapy: An Upper Limb Virtual Reality-Based Motor Rehabilitation Platform

David J. Reinkensmeyer, Luis Enrique Sucar, Felipe Orihuela-Espina, 2014, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Slacking by the human motor system: Computational models and implications for robotic orthoses

David J. Reinkensmeyer, Daniel P. Ferris, Keith E. Gordon, 2009, 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Effect of robotic guidance on motor learning of a timing task

D.J. Reinkensmeyer, L.M. Crespo, D. Reinkensmeyer, 2008, 2008 2nd IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics.

Effect of muscle fatigue on internal model formation and retention during reaching with the arm.

David J Reinkensmeyer, Craig D Takahashi, Dan Nemet, 2006, Journal of applied physiology.

and retention during reaching with the arm Effect of muscle fatigue on internal model formation

David J. Reinkensmeyer, J. L. Emken, Raul Benitez, 2015 .

Recent trends in assistive technology for mobility

Michael L Boninger, Mary M Rodgers, Benjamin J Fregly, 2012, Journal of NeuroEngineering and Rehabilitation.

Gesture Therapy - A Low-Cost Vision-Based System for Rehabilitation after Stroke

David J. Reinkensmeyer, Ron S. Leder, Luis Enrique Sucar, 2008, HEALTHINF.

A pneumatic robot for re-training arm movement after stroke: rationale and mechanical design

D.J. Reinkensmeyer, R. Smith, S. Cramer, 2005, 9th International Conference on Rehabilitation Robotics, 2005. ICORR 2005..

Breaking It Down Is Better: Haptic Decomposition of Complex Movements Aids in Robot-Assisted Motor Learning

J. Klein, S. J. Spencer, D. J. Reinkensmeyer, 2012, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Personalized neuromusculoskeletal modeling to improve treatment of mobility impairments: a perspective from European research sites

Michael L Boninger, Benjamin J Fregly, David J Reinkensmeyer, 2012, Journal of NeuroEngineering and Rehabilitation.

Neural circuits activated by error amplification and haptic guidance training techniques during performance of a timing-based motor task by healthy individuals

D. Reinkensmeyer, S. Cramer, L. Marchal-Crespo, 2018, Experimental Brain Research.

Dynamic motion planning for the design of robotic gait rehabilitation.

David J Reinkensmeyer, Chia-Yu E. Wang, James E Bobrow, 2005, Journal of biomechanical engineering.

A robotic device for manipulating human stepping

David J. Reinkensmeyer, Susan J. Harkema, Jeremy L. Emken, 2006, IEEE Transactions on Robotics.

Optimization of a Parallel Shoulder Mechanism to Achieve a High-Force, Low-Mass, Robotic-Arm Exoskeleton

David J. Reinkensmeyer, Steven J. Spencer, James E. Bobrow, 2010, IEEE Transactions on Robotics.

Web-based telerehabilitation for the upper extremity after stroke

D.J. Reinkensmeyer, J.A. Nessler, C.T. Pang, 2002, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Feedforward stabilization in a bimanual unloading task

L. W. Stark, Steven L. Lehman, D. J. Reinkensmeyer, 1992, Experimental Brain Research.

A robotic device for studying rodent locomotion after spinal cord injury

M. Merlo, D.J. Reinkensmeyer, V.R. Edgerton, 2005, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

Motor adaptation to a small force field superimposed on a large background force

D. Reinkensmeyer, Jiayin Liu, 2007, Experimental Brain Research.

Measuring Movement Quality of the Stroke-Impaired Upper Extremity with a Wearable Sensor: Toward a Smoothness Metric for Home Rehabilitation Exercise Programs

D. Reinkensmeyer, V. Chan, Shusuke Okita, 2021, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

The Effectiveness of Protective Headgear in Attenuating Ball-to-Forehead Impacts in Water Polo

Derek C. Monroe, Nicholas J. Cecchi, D. Reinkensmeyer, 2019, Front. Sports Act. Living.

Motor Adaptation, in Children Neuromotor Noise Limits Motor Performance, But Not

David J. Reinkensmeyer, Helen J. Huang, Craig D. Takahashi, 2015 .

Feasibility of home hand rehabilitation using musicglove after chronic spinal cord injury

D. Reinkensmeyer, S. Cramer, V. Chan, 2022, Spinal Cord Series and Cases.

Use of a robotic device to measure age-related decline in finger proprioception

Justin B. Rowe, D. Reinkensmeyer, S. Cramer, 2015, Experimental Brain Research.

Using a bimanual lever-driven wheelchair for arm movement practice early after stroke: A pilot, randomized, controlled, single-blind trial

Brendan W. Smith, Cathy C. Y. Chou, D. Reinkensmeyer, 2021, Clinical rehabilitation.

Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: a randomized controlled pilot study

W. Rymer, D. Reinkensmeyer, L. Kahn, 2006, Journal of NeuroEngineering and Rehabilitation.

Development and evaluation of two control methods for MOVit: An exercise-enabling driving interface for powered wheelchair users THESIS

D. Reinkensmeyer, 2018 .

Design and experimental evaluation of yoked hand-clutching for a lever drive chair

Brendan W. Smith, David J Reinkensmeyer, Yasemin Sarigul-Klijn, 2018, Assistive technology : the official journal of RESNA.

How do strength and coordination recovery interact after stroke? A computational model for informing robotic training

David J. Reinkensmeyer, Joan Lobo-Prat, Sumner L. Norman, 2017, 2017 International Conference on Rehabilitation Robotics (ICORR).

Dissociating motor learning from recovery in exoskeleton training post-stroke

Nicolas Schweighofer, Isabelle Laffont, Denis Mottet, 2018, Journal of NeuroEngineering and Rehabilitation.

Model-based assistance-as-needed for robotic movement therapy after stroke

David J. Reinkensmeyer, Eric T. Wolbrecht, Hossein Taheri, 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

Design of a thumb module for the FINGER rehabilitation robot

Joel C. Perry, David J. Reinkensmeyer, Eric T. Wolbrecht, 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

The Resonating Arm Exerciser: design and pilot testing of a mechanically passive rehabilitation device that mimics robotic active assistance

Lorena Palafox, David J Reinkensmeyer, Daniel K Zondervan, 2013, Journal of NeuroEngineering and Rehabilitation.

Single Degree-of-Freedom Exoskeleton Mechanism Design for Thumb Rehabilitation

David J. Reinkensmeyer, Eric T. Wolbrecht, Yimesker Yihun, 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Single degree-of-freedom exoskeleton mechanism design for finger rehabilitation

David J. Reinkensmeyer, Eric T. Wolbrecht, Alba Perez-Gracia, 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

Functional Assisted Gaming for Upper-Extremity Therapy After Stroke: Background, Evaluation, and Future Directions of the Spring Orthosis Approach

David J. Reinkensmeyer, D. Reinkensmeyer, 2012 .

The Manumeter: A non-obtrusive wearable device for monitoring spontaneous use of the wrist and fingers

David J. Reinkensmeyer, Justin B. Rowe, Mark Bachman, 2013, 2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR).

A Real-Time Interactive MIDI Glove for Domicile Stroke Rehabilitation

David J. Reinkensmeyer, Mark Bachman, Nizan Friedman, 2011, HCI.

How to retrain movement after neurologic injury: a computational rationale for incorporating robot (or therapist) assistance

David J. Reinkensmeyer, D. Reinkensmeyer, D. Reinkensmeyer, 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

Substituting auditory for visual feedback to adapt to altered dynamic and kinematic environments during reaching

D. Reinkensmeyer, F. Avanzini, G. Rosati, 2012, Experimental Brain Research.

Control of a Pneumatic Orthosis for Upper Extremity Stroke Rehabilitation

D.J. Reinkensmeyer, J. Leavitt, J.E. Bobrow, 2006, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society.

A Systematic Review of the Learning Dynamics of Proprioception Training: Specificity, Acquisition, Retention, and Transfer

Andria J. Farrens, Seo Jung Yun, D. Reinkensmeyer, 2023, Neurorehabilitation and neural repair.

Tools for understanding and optimizing robotic gait training.

Roger Weber, Roland R Roy, V Reggie Edgerton, 2006, Journal of rehabilitation research and development.

Can Robot-Assisted Therapy Promote Generalization of Motor Learning Following Stroke?: Preliminary Results

W.Z. Rymer, D. Reinkensmeyer, H. Fischer, 2006, The First IEEE/RAS-EMBS International Conference on Biomedical Robotics and Biomechatronics, 2006. BioRob 2006..

Dissociating Sensorimotor Recovery and Compensation During Exoskeleton Training Following Stroke

D. Reinkensmeyer, N. Schweighofer, Denis Mottet, 2021, Frontiers in Human Neuroscience.

Magnetically Counting Hand Movements: Validation of a Calibration-Free Algorithm and Application to Testing the Threshold Hypothesis of Real-World Hand Use after Stroke

Vicky Chan, David J. Reinkensmeyer, Justin B. Rowe, 2021, Sensors.

The Manumeter: A Wearable Device for Monitoring Daily Use of the Wrist and Fingers

David J. Reinkensmeyer, Justin B. Rowe, Mark Bachman, 2014, IEEE Journal of Biomedical and Health Informatics.

Design of robot assistance for arm movement therapy following stroke

David J. Reinkensmeyer, Leonard E. Kahn, Craig D. Takahashi, 2001, Adv. Robotics.

Noninvasive brain-computer interface driven hand orthosis

David J. Reinkensmeyer, Zoran Nenadic, Shunji Moromugi, 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Persistence of motor adaptation during constrained, multi-joint, arm movements.

W. Rymer, F. Mussa-Ivaldi, D. Reinkensmeyer, 2000, Journal of neurophysiology.

The effect of dynamic arm support on grip forces and steering errors during simulated driving

David J. Reinkensmeyer, Julius Klein, Marc Obrador, 2008 .

and Effort Motor Adaptation as a Greedy Optimization of Error

Jaime E. Duarte, David J. Reinkensmeyer, Brian P Selgrade, 2015 .

Recent trends in assistive technology for mobility

B. Fregly, D. Reinkensmeyer, M. Rodgers, 2012, Journal of NeuroEngineering and Rehabilitation.

Title Recent trends in assistive technology for mobility Permalink

B. Fregly, D. Reinkensmeyer, M. Rodgers, 2012 .

Finger strength, individuation, and their interaction: Relationship to hand function and corticospinal tract injury after stroke

Justin B. Rowe, D. Reinkensmeyer, S. Cramer, 2018, Clinical Neurophysiology.

Personalized neuromusculoskeletal modeling to improve treatment of mobility impairments: a perspective from European research sites

B. Fregly, D. Reinkensmeyer, M. Boninger, 2012, Journal of NeuroEngineering and Rehabilitation.

Wearable sensing for rehabilitation after stroke: Bimanual jerk asymmetry encodes unique information about the variability of upper extremity recovery

Vicky Chan, David J. Reinkensmeyer, Justin B. Rowe, 2017, 2017 International Conference on Rehabilitation Robotics (ICORR).

Proprioceptive Gaming: Making Finger Sensation Training Intense and Engaging with the P-Pong Game and PINKIE Robot

Dylan S. Reinsdorf, D. Reinkensmeyer, Erin E. Mahan, 2021, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

Some Key Problems for Robot-Assisted Movement Therapy Research: A Perspective from the University of California at Irvine

J.A. Galvez, D.J. Reinkensmeyer, J.E. Bobrow, 2007, 2007 IEEE 10th International Conference on Rehabilitation Robotics.

A Computational Model of Human-Robot Load Sharing during Robot-Assisted Arm Movement Training after Stroke

David J. Reinkensmeyer, Eric Wolbrecht, James Bobrow, 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

Selection of Robotic Therapy Algorithms for the Upper Extremity in Chronic Stroke: Insights from MIME and ARM Guide Results

David J. Reinkensmeyer, Peter S. Lum, Leonard E. Kahn, 2003 .

Ocular biomechanics and biotransport.

C. R. Ethier, D. Reinkensmeyer, J. Emken, 2004, Annual review of biomedical engineering.

A simulator and assist-as-needed control strategy for learning to drive a power wheelchair

David J. Reinkensmeyer, Faryar Jabbari, James E. Bobrow, 2006 .

Time flies when you are in a groove: using entrainment to mechanical resonance to teach a desired movement distorts the perception of the movement’s timing

Jaime E. Duarte, Daniel K. Zondervan, David J. Reinkensmeyer, 2014, Experimental Brain Research.

Technologies and combination therapies for enhancing movement training for people with a disability

D. Reinkensmeyer, M. Boninger, 2012, Journal of NeuroEngineering and Rehabilitation.

Journal of Neuroengineering and Rehabilitation Review of Control Strategies for Robotic Movement Training after Neurologic Injury

D. Reinkensmeyer, Laura Marchal-Crespo, David, 2022 .

"If I can't do it once, why do it a hundred times?": Connecting volition to movement success in a virtual environment motivates people to exercise the arm after stroke

David J. Reinkensmeyer, Sarah J. Housman, D. Reinkensmeyer, 2007, 2007 Virtual Rehabilitation.

A robotic tool for studying locomotor adaptation and rehabilitation

David J. Reinkensmeyer, Susan J. Harkema, J. H. Wynne, 2002, Proceedings of the Second Joint 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society] [Engineering in Medicine and Biology.