Literature DB >> 26005600

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

Amy A Blank1, James A French1, Ali Utku Pehlivan1, Marcia K O'Malley1.   

Abstract

Stroke is one of the leading causes of long-term disability today; therefore, many research efforts are focused on designing maximally effective and efficient treatment methods. In particular, robotic stroke rehabilitation has received significant attention for upper-limb therapy due to its ability to provide high-intensity repetitive movement therapy with less effort than would be required for traditional methods. Recent research has focused on increasing patient engagement in therapy, which has been shown to be important for inducing neural plasticity to facilitate recovery. Robotic therapy devices enable unique methods for promoting patient engagement by providing assistance only as needed and by detecting patient movement intent to drive to the device. Use of these methods has demonstrated improvements in functional outcomes, but careful comparisons between methods remain to be done. Future work should include controlled clinical trials and comparisons of effectiveness of different methods for patients with different abilities and needs in order to inform future development of patient-specific therapeutic protocols.

Entities:  

Keywords:  Assist-as-needed; Intent to move; Patient engagement; Robotic stroke rehabilitation; Upper-limb rehabilitation

Year:  2014        PMID: 26005600      PMCID: PMC4441271          DOI: 10.1007/s40141-014-0056-z

Source DB:  PubMed          Journal:  Curr Phys Med Rehabil Rep        ISSN: 2167-4833


  73 in total

1.  Towards brain-robot interfaces in stroke rehabilitation.

Authors:  M Gomez-Rodriguez; M Grosse-Wentrup; J Hill; A Gharabaghi; B Scholkopf; J Peters
Journal:  IEEE Int Conf Rehabil Robot       Date:  2011

2.  Whole-body intensive rehabilitation is feasible and effective in chronic stroke survivors: a retrospective data analysis.

Authors:  Kay Wing; James V Lynskey; Pamela R Bosch
Journal:  Top Stroke Rehabil       Date:  2008 May-Jun       Impact factor: 2.119

3.  Minimally assistive robot training for proprioception enhancement.

Authors:  Maura Casadio; Pietro Morasso; Vittorio Sanguineti; Psiche Giannoni
Journal:  Exp Brain Res       Date:  2009-01-13       Impact factor: 1.972

4.  Auto-adaptive robot-aided therapy using machine learning techniques.

Authors:  Francisco J Badesa; Ricardo Morales; Nicolas Garcia-Aracil; J M Sabater; Alicia Casals; Loredana Zollo
Journal:  Comput Methods Programs Biomed       Date:  2013-09-23       Impact factor: 5.428

5.  The coordination of arm movements: an experimentally confirmed mathematical model.

Authors:  T Flash; N Hogan
Journal:  J Neurosci       Date:  1985-07       Impact factor: 6.167

6.  Adaptive control of a serial-in-parallel robotic rehabilitation device.

Authors:  Ali Utku Pehlivan; Fabrizio Sergi; Marcia K O'Malley
Journal:  IEEE Int Conf Rehabil Robot       Date:  2013-06

7.  Brain Computer Interface based robotic rehabilitation with online modification of task speed.

Authors:  Mine Sarac; Ela Koyas; Ahmetcan Erdogan; Mujdat Cetin; Volkan Patoglu
Journal:  IEEE Int Conf Rehabil Robot       Date:  2013-06

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

Authors:  Leonard E Kahn; Michele L Zygman; W Zev Rymer; David J Reinkensmeyer
Journal:  J Neuroeng Rehabil       Date:  2006-06-21       Impact factor: 4.262

Review 9.  A working model of stroke recovery from rehabilitation robotics practitioners.

Authors:  Hermano Igo Krebs; Bruce Volpe; Neville Hogan
Journal:  J Neuroeng Rehabil       Date:  2009-02-25       Impact factor: 4.262

10.  Performance adaptive training control strategy for recovering wrist movements in stroke patients: a preliminary, feasibility study.

Authors:  Lorenzo Masia; Maura Casadio; Psiche Giannoni; Giulio Sandini; Pietro Morasso
Journal:  J Neuroeng Rehabil       Date:  2009-12-07       Impact factor: 4.262

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  24 in total

1.  Design and Validation of a Lower-Limb Haptic Rehabilitation Robot.

Authors:  Alexander R Dawson-Elli; Peter G Adamczyk
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2020-07       Impact factor: 3.802

2.  Improving robotic stroke rehabilitation by incorporating neural intent detection: Preliminary results from a clinical trial.

Authors:  Jennifer L Sullivan; Nikunj A Bhagat; Nuray Yozbatiran; Ruta Paranjape; Colin G Losey; Robert G Grossman; Jose L Contreras-Vidal; Gerard E Francisco; Marcia K O'Malley
Journal:  IEEE Int Conf Rehabil Robot       Date:  2017-07

Review 3.  Sensors and Actuation Technologies in Exoskeletons: A Review.

Authors:  Monica Tiboni; Alberto Borboni; Fabien Vérité; Chiara Bregoli; Cinzia Amici
Journal:  Sensors (Basel)       Date:  2022-01-24       Impact factor: 3.576

4.  Finger Movement Recognition via High-Density Electromyography of Intrinsic and Extrinsic Hand Muscles.

Authors:  Xuhui Hu; Aiguo Song; Jianzhi Wang; Hong Zeng; Wentao Wei
Journal:  Sci Data       Date:  2022-06-29       Impact factor: 8.501

Review 5.  Upper Limb Home-Based Robotic Rehabilitation During COVID-19 Outbreak.

Authors:  Hemanth Manjunatha; Shrey Pareek; Sri Sadhan Jujjavarapu; Mostafa Ghobadi; Thenkurussi Kesavadas; Ehsan T Esfahani
Journal:  Front Robot AI       Date:  2021-05-24

6.  Proportional estimation of finger movements from high-density surface electromyography.

Authors:  Nicolò Celadon; Strahinja Došen; Iris Binder; Paolo Ariano; Dario Farina
Journal:  J Neuroeng Rehabil       Date:  2016-08-04       Impact factor: 4.262

7.  An EEG Tool for Monitoring Patient Engagement during Stroke Rehabilitation: A Feasibility Study.

Authors:  Gadi Bartur; Katherin Joubran; Sara Peleg-Shani; Jean-Jacques Vatine; Goded Shahaf
Journal:  Biomed Res Int       Date:  2017-09-24       Impact factor: 3.411

8.  Mirror Visual Feedback Prior to Robot-Assisted Training Facilitates Rehabilitation After Stroke: A Randomized Controlled Study.

Authors:  Jifeng Rong; Li Ding; Li Xiong; Wen Zhang; Weining Wang; Meikui Deng; Yana Wang; Zhen Chen; Jie Jia
Journal:  Front Neurol       Date:  2021-07-08       Impact factor: 4.003

9.  Design and Optimization of an EEG-Based Brain Machine Interface (BMI) to an Upper-Limb Exoskeleton for Stroke Survivors.

Authors:  Nikunj A Bhagat; Anusha Venkatakrishnan; Berdakh Abibullaev; Edward J Artz; Nuray Yozbatiran; Amy A Blank; James French; Christof Karmonik; Robert G Grossman; Marcia K O'Malley; Gerard E Francisco; Jose L Contreras-Vidal
Journal:  Front Neurosci       Date:  2016-03-31       Impact factor: 4.677

Review 10.  Combining Upper Limb Robotic Rehabilitation with Other Therapeutic Approaches after Stroke: Current Status, Rationale, and Challenges.

Authors:  Stefano Mazzoleni; Christophe Duret; Anne Gaëlle Grosmaire; Elena Battini
Journal:  Biomed Res Int       Date:  2017-09-13       Impact factor: 3.411

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