2019 - Fellow of the Indian National Academy of Engineering (INAE)
David J. Reinkensmeyer mainly investigates Physical medicine and rehabilitation, Robot, Rehabilitation, Physical therapy and Artificial intelligence. His studies in Physical medicine and rehabilitation integrate themes in fields like Treadmill, Treadmill training, Biomechanics, Spinal cord injury and Motor control. His Robot research integrates issues from Work, Feedback control, Gait training and Simulation.
His Rehabilitation research is multidisciplinary, incorporating elements of Stroke and Robotic paradigms. His research investigates the connection with Physical therapy and areas like Hemiparesis which intersect with concerns in Arm exercise. The Artificial intelligence study combines topics in areas such as Neurologic injury, Communication and Motor learning.
David J. Reinkensmeyer mostly deals with Physical medicine and rehabilitation, Rehabilitation, Robot, Artificial intelligence and Simulation. His research integrates issues of Treadmill, Physical therapy, Spinal cord injury, Motor control and Stroke in his study of Physical medicine and rehabilitation. The concepts of his Rehabilitation study are interwoven with issues in Computer game, Activities of daily living, Hemiparesis, Work and Functional movement.
David J. Reinkensmeyer has included themes like Gait, Pneumatic actuator, Orthotics and Trajectory in his Robot study. David J. Reinkensmeyer studies Robotics, a branch of Artificial intelligence. His Simulation research is multidisciplinary, relying on both Motion, Mechanism, Rehabilitation robotics and Control engineering, Control theory.
His primary areas of investigation include Physical medicine and rehabilitation, Rehabilitation, Stroke, Motor learning and Simulation. His studies deal with areas such as Neurorehabilitation, Motor control and Finger movement as well as Physical medicine and rehabilitation. David J. Reinkensmeyer combines subjects such as Robotics, Randomized controlled trial, Artificial intelligence and Medical education with his study of Rehabilitation.
His Stroke research includes elements of Motor system, Powered exoskeleton, Wrist and Neurological injury. His Motor learning research includes themes of Human–computer interaction, Functional ability, Haptic technology and Fugl meyer. His Simulation study integrates concerns from other disciplines, such as Robot, Control theory, Robot kinematics and Lever.
His primary areas of study are Physical medicine and rehabilitation, Rehabilitation, Stroke, Robotics and Artificial intelligence. His biological study spans a wide range of topics, including Principal component analysis and Simulation. His study in Rehabilitation is interdisciplinary in nature, drawing from both Wrist, Activities of daily living and Motor control.
His study explores the link between Stroke and topics such as Physical therapy that cross with problems in Randomized controlled trial. His Robotics study is concerned with the field of Robot as a whole. The various areas that David J. Reinkensmeyer examines in his Robot study include Nursing and Communication.
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Review of control strategies for robotic movement training after neurologic injury
Laura Marchal-Crespo;David J Reinkensmeyer.
Journal of Neuroengineering and Rehabilitation (2009)
Understanding and treating arm movement impairment after chronic brain injury: progress with the ARM guide.
D J Reinkensmeyer;L E Kahn;L E Kahn;M Averbuch;M Averbuch;A McKenna-Cole;A McKenna-Cole.
Journal of Rehabilitation Research and Development (2014)
Optimizing Compliant, Model-Based Robotic Assistance to Promote Neurorehabilitation
E.T. Wolbrecht;V. Chan;D.J. Reinkensmeyer;J.E. Bobrow.
international conference of the ieee engineering in medicine and biology society (2008)
Robotics, motor learning, and neurologic recovery.
David J. Reinkensmeyer;Jeremy L. Emken;Steven C. Cramer.
Annual Review of Biomedical Engineering (2004)
Retraining the injured spinal cord
V. Reggie Edgerton;Ray D. de Leon;Susan J. Harkema;John A. Hodgson.
The Journal of Physiology (2001)
Persistence of motor adaptation during constrained, multi-joint, arm movements.
Robert A. Scheidt;David J. Reinkensmeyer;Michael A. Conditt;W. Zev Rymer.
Journal of Neurophysiology (2000)
Robot-assisted reaching exercise promotes arm movement recovery in chronic hemiparetic stroke: a randomized controlled pilot study.
Leonard E Kahn;Leonard E Kahn;Michele L Zygman;W Zev Rymer;W Zev Rymer;David J Reinkensmeyer;David J Reinkensmeyer.
Journal of Neuroengineering and Rehabilitation (2006)
Automating Arm Movement Training Following Severe Stroke: Functional Exercises With Quantitative Feedback in a Gravity-Reduced Environment
R.J. Sanchez;Jiayin Liu;S. Rao;P. Shah.
international conference of the ieee engineering in medicine and biology society (2006)
A randomized controlled trial of gravity-supported, computer-enhanced arm exercise for individuals with severe hemiparesis.
Sarah J. Housman;Kelly M. Scott;David J. Reinkensmeyer;David J. Reinkensmeyer.
Neurorehabilitation and Neural Repair (2009)
Web-based telerehabilitation for the upper extremity after stroke
D.J. Reinkensmeyer;C.T. Pang;J.A. Nessler;C.C. Painter.
international conference of the ieee engineering in medicine and biology society (2002)
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