Literature DB >> 17894266

Design and control of RUPERT: a device for robotic upper extremity repetitive therapy.

Thomas G Sugar1, Jiping He, Edward J Koeneman, James B Koeneman, Richard Herman, H Huang, Robert S Schultz, D E Herring, J Wanberg, Sivakumar Balasubramanian, Pete Swenson, Jeffrey A Ward.   

Abstract

The structural design, control system, and integrated biofeedback for a wearable exoskeletal robot for upper extremity stroke rehabilitation are presented. Assisted with clinical evaluation, designers, engineers, and scientists have built a device for robotic assisted upper extremity repetitive therapy (RUPERT). Intense, repetitive physical rehabilitation has been shown to be beneficial overcoming upper extremity deficits, but the therapy is labor intensive and expensive and difficult to evaluate quantitatively and objectively. The RUPERT is developed to provide a low cost, safe and easy-to-use, robotic-device to assist the patient and therapist to achieve more systematic therapy at home or in the clinic. The RUPERT has four actuated degrees-of-freedom driven by compliant and safe pneumatic muscles (PMs) on the shoulder, elbow, and wrist. They are programmed to actuate the device to extend the arm and move the arm in 3-D space. It is very important to note that gravity is not compensated and the daily tasks are practiced in a natural setting. Because the device is wearable and lightweight to increase portability, it can be worn standing or sitting providing therapy tasks that better mimic activities of daily living. The sensors feed back position and force information for quantitative evaluation of task performance. The device can also provide real-time, objective assessment of functional improvement. We have tested the device on stroke survivors performing two critical activities of daily living (ADL): reaching out and self feeding. The future improvement of the device involves increased degrees-of-freedom and interactive control to adapt to a user's physical conditions.

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Year:  2007        PMID: 17894266     DOI: 10.1109/TNSRE.2007.903903

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  20 in total

1.  A Context-Aware Application to Increase Elderly Users Compliance with Physical Rehabilitation Exercises at Home via Animatronic Biofeedback.

Authors:  Borja Gamecho; Hugo Silva; José Guerreiro; Luis Gardeazabal; Julio Abascal
Journal:  J Med Syst       Date:  2015-08-30       Impact factor: 4.460

2.  Breaking it down is better: haptic decomposition of complex movements aids in robot-assisted motor learning.

Authors:  Julius Klein; Steven J Spencer; David J Reinkensmeyer
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2012-04-18       Impact factor: 3.802

3.  Incorporating haptic effects into three-dimensional virtual environments to train the hemiparetic upper extremity.

Authors:  Sergei V Adamovich; Gerard G Fluet; Alma S Merians; Abraham Mathai; Qinyin Qiu
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2009-08-07       Impact factor: 3.802

4.  Current Trends in Robot-Assisted Upper-Limb Stroke Rehabilitation: Promoting Patient Engagement in Therapy.

Authors:  Amy A Blank; James A French; Ali Utku Pehlivan; Marcia K O'Malley
Journal:  Curr Phys Med Rehabil Rep       Date:  2014-09

Review 5.  Pneumatic robotic systems for upper limb rehabilitation.

Authors:  Ricardo Morales; Francisco Javier Badesa; Nicolás García-Aracil; José María Sabater; Carlos Pérez-Vidal
Journal:  Med Biol Eng Comput       Date:  2011-08-06       Impact factor: 2.602

6.  Normalized movement quality measures for therapeutic robots strongly correlate with clinical motor impairment measures.

Authors:  Ozkan Celik; Marcia K O'Malley; Corwin Boake; Harvey S Levin; Nuray Yozbatiran; Timothy A Reistetter
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2010-04-12       Impact factor: 3.802

7.  Human arm weight compensation in rehabilitation robotics: efficacy of three distinct methods.

Authors:  Fabian Just; Özhan Özen; Stefano Tortora; Verena Klamroth-Marganska; Robert Riener; Georg Rauter
Journal:  J Neuroeng Rehabil       Date:  2020-02-05       Impact factor: 4.262

8.  A robotic system to train activities of daily living in a virtual environment.

Authors:  Marco Guidali; Alexander Duschau-Wicke; Simon Broggi; Verena Klamroth-Marganska; Tobias Nef; Robert Riener
Journal:  Med Biol Eng Comput       Date:  2011-07-28       Impact factor: 2.602

Review 9.  Robotic neurorehabilitation: a computational motor learning perspective.

Authors:  Vincent S Huang; John W Krakauer
Journal:  J Neuroeng Rehabil       Date:  2009-02-25       Impact factor: 4.262

Review 10.  Review of control strategies for robotic movement training after neurologic injury.

Authors:  Laura Marchal-Crespo; David J Reinkensmeyer
Journal:  J Neuroeng Rehabil       Date:  2009-06-16       Impact factor: 4.262

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