Literature DB >> 22592063

Robots integrated with virtual reality simulations for customized motor training in a person with upper extremity hemiparesis: a case study.

Gerard G Fluet1, Alma S Merians, Qinyin Qiu, Ian Lafond, Soha Saleh, Viviana Ruano, Andrea R Delmonico, Sergei V Adamovich.   

Abstract

BACKGROUND AND
PURPOSE: A majority of studies examining repetitive task practice facilitated by robots for the treatment of upper extremity paresis utilize standardized protocols applied to large groups. Others utilize interventions tailored to patients but do not describe the clinical decision-making process utilized to develop and modify interventions. This case study describes a robot-based intervention customized to match the goals and clinical presentation of person with upper extremity hemiparesis secondary to stroke.
METHODS: The patient, P.M., was an 85-year-old man with left hemiparesis secondary to an intracerebral hemorrhage 5 years prior to examination. Outcomes were measured before and after a 1-month period of home therapy and after a 1-month robotic intervention. The intervention was designed to address specific impairments identified during his physical therapy examination. When necessary, activities were modified on the basis of response to the first week of treatment. OUTCOMES: P.M. trained in 12 sessions, using six virtually simulated activities. Modifications to original configurations of these activities resulted in performance improvements in five of these activities. P.M. demonstrated a 35-second improvement in Jebsen Test of Hand Function time and a 44-second improvement in Wolf Motor Function Test time subsequent to the robotic training intervention. Reaching kinematics, 24-hour activity measurement, and scores on the Hand and Activities of Daily Living scales of the Stroke Impact Scale all improved as well. DISCUSSION: A customized program of robotically facilitated rehabilitation was associated with short-term improvements in several measurements of upper extremity function in a patient with chronic hemiparesis.

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Year:  2012        PMID: 22592063      PMCID: PMC4195597          DOI: 10.1097/NPT.0b013e3182566f3f

Source DB:  PubMed          Journal:  J Neurol Phys Ther        ISSN: 1557-0576            Impact factor:   3.649


  32 in total

1.  Robot-assisted adaptive training: custom force fields for teaching movement patterns.

Authors:  James L Patton; Ferdinando A Mussa-Ivaldi
Journal:  IEEE Trans Biomed Eng       Date:  2004-04       Impact factor: 4.538

2.  Control of hand orientation and arm movement during reach and grasp.

Authors:  Jing Fan; Jiping He; Stephen I Helms Tillery
Journal:  Exp Brain Res       Date:  2005-11-24       Impact factor: 1.972

Review 3.  Virtual reality in stroke rehabilitation: a systematic review of its effectiveness for upper limb motor recovery.

Authors:  Amy Henderson; Nicol Korner-Bitensky; Mindy Levin
Journal:  Top Stroke Rehabil       Date:  2007 Mar-Apr       Impact factor: 2.119

4.  An objective and standardized test of hand function.

Authors:  R H Jebsen; N Taylor; R B Trieschmann; M J Trotter; L A Howard
Journal:  Arch Phys Med Rehabil       Date:  1969-06       Impact factor: 3.966

5.  Translating animal doses of task-specific training to people with chronic stroke in 1-hour therapy sessions: a proof-of-concept study.

Authors:  Rebecca L Birkenmeier; Eliza M Prager; Catherine E Lang
Journal:  Neurorehabil Neural Repair       Date:  2010-04-27       Impact factor: 3.919

6.  The stroke impact scale version 2.0. Evaluation of reliability, validity, and sensitivity to change.

Authors:  P W Duncan; D Wallace; S M Lai; D Johnson; S Embretson; L J Laster
Journal:  Stroke       Date:  1999-10       Impact factor: 7.914

Review 7.  Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review.

Authors:  Gert Kwakkel; Boudewijn J Kollen; Hermano I Krebs
Journal:  Neurorehabil Neural Repair       Date:  2007-09-17       Impact factor: 3.919

8.  Minimal detectable change and clinically important difference of the Wolf Motor Function Test in stroke patients.

Authors:  Keh-chung Lin; Yu-wei Hsieh; Ching-yi Wu; Chia-ling Chen; Yuh Jang; Jung-sen Liu
Journal:  Neurorehabil Neural Repair       Date:  2009-03-16       Impact factor: 3.919

9.  Upper extremity use in people with hemiparesis in the first few weeks after stroke.

Authors:  Catherine E Lang; Joanne M Wagner; Dorothy F Edwards; Alexander W Dromerick
Journal:  J Neurol Phys Ther       Date:  2007-06       Impact factor: 3.649

10.  Exploring the bases for a mixed reality stroke rehabilitation system, Part II: design of interactive feedback for upper limb rehabilitation.

Authors:  Nicole Lehrer; Yinpeng Chen; Margaret Duff; Steven L Wolf; Thanassis Rikakis
Journal:  J Neuroeng Rehabil       Date:  2011-09-08       Impact factor: 4.262

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  10 in total

1.  Exploring the impact of visual and movement based priming on a motor intervention in the acute phase post-stroke in persons with severe hemiparesis of the upper extremity.

Authors:  Jigna Patel; Qinyin Qiu; Mathew Yarossi; Alma Merians; Supriya Massood; Eugene Tunik; Sergei Adamovich; Gerard Fluet
Journal:  Disabil Rehabil       Date:  2016-09-16       Impact factor: 3.033

2.  Emergence of virtual reality as a tool for upper limb rehabilitation: incorporation of motor control and motor learning principles.

Authors:  Mindy F Levin; Patrice L Weiss; Emily A Keshner
Journal:  Phys Ther       Date:  2014-09-11

3.  Motor skill changes and neurophysiologic adaptation to recovery-oriented virtual rehabilitation of hand function in a person with subacute stroke: a case study.

Authors:  Gerard G Fluet; Jigna Patel; Qinyin Qiu; Matthew Yarossi; Supriya Massood; Sergei V Adamovich; Eugene Tunik; Alma S Merians
Journal:  Disabil Rehabil       Date:  2016-09-27       Impact factor: 3.033

4.  Movement rehabilitation in virtual reality from then to now: how are we doing?

Authors:  Alma S Merians; Gerard Fluet; Eugene Tunik; Q Qiu; Soha Saleh; Sergei Adamovich
Journal:  Int J Disabil Hum Dev       Date:  2014-08-12

5.  Mirrored feedback in chronic stroke: recruitment and effective connectivity of ipsilesional sensorimotor networks.

Authors:  Soha Saleh; Sergei V Adamovich; Eugene Tunik
Journal:  Neurorehabil Neural Repair       Date:  2013-12-26       Impact factor: 3.919

Review 6.  Robotic technologies and rehabilitation: new tools for stroke patients' therapy.

Authors:  Patrizia Poli; Giovanni Morone; Giulio Rosati; Stefano Masiero
Journal:  Biomed Res Int       Date:  2013-11-20       Impact factor: 3.411

7.  The Efficacy of a Haptic-Enhanced Virtual Reality System for Precision Grasp Acquisition in Stroke Rehabilitation.

Authors:  Shih-Ching Yeh; Si-Huei Lee; Rai-Chi Chan; Yi Wu; Li-Rong Zheng; Sheryl Flynn
Journal:  J Healthc Eng       Date:  2017-11-05       Impact factor: 2.682

8.  A Novel Robot-Aided Upper Limb Rehabilitation Training System Based on Multimodal Feedback.

Authors:  Lizheng Pan; Lu Zhao; Aiguo Song; Zeming Yin; Shigang She
Journal:  Front Robot AI       Date:  2019-11-08

9.  Virtual Rehabilitation of the Paretic Hand and Arm in Persons With Stroke: Translation From Laboratory to Rehabilitation Centers and the Patient's Home.

Authors:  Gerard Fluet; Qinyin Qiu; Jigna Patel; Ashley Mont; Amanda Cronce; Mathew Yarossi; Alma Merians; Sergei Adamovich
Journal:  Front Neurol       Date:  2021-01-28       Impact factor: 4.003

10.  Robotic Table and Serious Games for Integrative Rehabilitation in the Early Poststroke Phase: Two Case Reports.

Authors:  Grigore Burdea; Nam Kim; Kevin Polistico; Ashwin Kadaru; Namrata Grampurohit; Jasdeep Hundal; Simcha Pollack
Journal:  JMIR Rehabil Assist Technol       Date:  2022-04-13
  10 in total

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