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Source: Journal of NeuroEngineering and Rehabilitation
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Total 22 results found since Jan 2013.
Autonomy support encourages use of more-affected arm post-stroke
Autonomy support, which involves providing individuals the ability to control their own behavior, is associated with improved motor control and learning in various populations in clinical and non-clinical sett...
Source: Journal of NeuroEngineering and Rehabilitation - September 7, 2023 Category: Rehabilitation Authors: Sujin Kim, Yumi Shin, Yeonwoo Jeong, Seungyoung Na and Cheol E. Han Tags: Research Source Type: research
Predicting patient-reported outcome of activities of daily living in stroke rehabilitation: a machine learning study
Machine Learning is increasingly used to predict rehabilitation outcomes in stroke in the context of precision rehabilitation and patient-centered care. However, predictors for patient-centered outcome measure...
Source: Journal of NeuroEngineering and Rehabilitation - February 23, 2023 Category: Rehabilitation Authors: Yu-Wen Chen, Keh-chung Lin, Yi-chun Li and Chia-Jung Lin Tags: Research Source Type: research
The use of machine learning and deep learning techniques to assess proprioceptive impairments of the upper limb after stroke
Robots can generate rich kinematic datasets that have the potential to provide far more insight into impairments than standard clinical ordinal scales. Determining how to define the presence or absence of impa...
Source: Journal of NeuroEngineering and Rehabilitation - January 27, 2023 Category: Rehabilitation Authors: Delowar Hossain, Stephen H. Scott, Tyler Cluff and Sean P. Dukelow Tags: Research Source Type: research
Machine learning methods for functional recovery prediction and prognosis in post-stroke rehabilitation: a systematic review
Rehabilitation medicine is facing a new development phase thanks to a recent wave of rigorous clinical trials aimed at improving the scientific evidence of protocols. This phenomenon, combined with new trends ...
Source: Journal of NeuroEngineering and Rehabilitation - June 3, 2022 Category: Rehabilitation Authors: Silvia Campagnini, Chiara Arienti, Michele Patrini, Piergiuseppe Liuzzi, Andrea Mannini and Maria Chiara Carrozza Tags: Review Source Type: research
Bimanual motor skill learning with robotics in chronic stroke: comparison between minimally impaired and moderately impaired patients, and healthy individuals
Most activities of daily life (ADL) require cooperative bimanual movements. A unilateral stroke may severely impair bimanual ADL. How patients with stroke (re)learn to coordinate their upper limbs (ULs) is lar...
Source: Journal of NeuroEngineering and Rehabilitation - March 17, 2022 Category: Rehabilitation Authors: Elo ïse Gerardin, Damien Bontemps, Nicolas-Thomas Babuin, Benoît Herman, Adrien Denis, Benoît Bihin, Maxime Regnier, Maria Leeuwerck, Thierry Deltombe, Audrey Riga and Yves Vandermeeren Tags: Research Source Type: research
Prediction of robotic neurorehabilitation functional ambulatory outcome in patients with neurological disorders
Conflicting results persist regarding the effectiveness of robotic-assisted gait training (RAGT) for functional gait recovery in post-stroke survivors. We used several machine learning algorithms to construct ...
Source: Journal of NeuroEngineering and Rehabilitation - December 18, 2021 Category: Rehabilitation Authors: Chao-Yang Kuo, Chia-Wei Liu, Chien-Hung Lai, Jiunn-Horng Kang, Sung-Hui Tseng and Emily Chia-Yu Su Tags: Research Source Type: research
Generalizing the predictive relationship between 1-month motor skill retention and Rey –Osterrieth Delayed Recall scores from nondemented older adults to individuals with chronic stroke: a short report
Motor learning is fundamental to motor rehabilitation outcomes. There is growing evidence from non-neurological populations supporting the role of visuospatial memory function in motor learning, but current pr...
Source: Journal of NeuroEngineering and Rehabilitation - June 3, 2021 Category: Rehabilitation Authors: Jennapher Lingo VanGilder, Andrew Hooyman, Pamela R. Bosch and Sydney Y. Schaefer Tags: Short report Source Type: research
Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches
Accurate prediction of motor recovery after stroke is critical for treatment decisions and planning. Machine learning has been proposed to be a promising technique for outcome prediction because of its high ac...
Source: Journal of NeuroEngineering and Rehabilitation - September 29, 2020 Category: Rehabilitation Authors: Hiren Kumar Thakkar, Wan-wen Liao, Ching-yi Wu, Yu-Wei Hsieh and Tsong-Hai Lee Tags: Research Source Type: research
Kinematic parameters obtained with the ArmeoSpring for upper-limb assessment after stroke: a reliability and learning effect study for guiding parameter use
After stroke, kinematic measures obtained with non-robotic and robotic devices are highly recommended to precisely quantify the sensorimotor impairments of the upper-extremity and select the most relevant ther...
Source: Journal of NeuroEngineering and Rehabilitation - September 29, 2020 Category: Rehabilitation Authors: Nabila Brihmat, Isabelle Loubinoux, Evelyne Castel-Lacanal, Philippe Marque and David Gasq Tags: Research Source Type: research
Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach
In clinical practice, therapists often rely on clinical outcome measures to quantify a patient ’s impairment and function. Predicting a patient’s discharge outcome using baseline clinical information may help c...
Source: Journal of NeuroEngineering and Rehabilitation - June 10, 2020 Category: Rehabilitation Authors: Yaar Harari, Megan K. O ’Brien, Richard L. Lieber and Arun Jayaraman Tags: Research Source Type: research
Adaptive multichannel FES neuroprosthesis with learning control and automatic gait assessment
FES (Functional Electrical Stimulation) neuroprostheses have long been a permanent feature in the rehabilitation and gait support of people who had a stroke or have a Spinal Cord Injury (SCI). Over time the we...
Source: Journal of NeuroEngineering and Rehabilitation - February 28, 2020 Category: Rehabilitation Authors: Philipp M üller, Antonio J. del Ama, Juan C. Moreno and Thomas Schauer Tags: Research Source Type: research
Detecting compensatory movements of stroke survivors using pressure distribution data and machine learning algorithms
Compensatory movements are commonly employed by stroke survivors during seated reaching and may have negative effects on their long-term recovery. Detecting compensation is useful for coaching the patient to r...
Source: Journal of NeuroEngineering and Rehabilitation - November 4, 2019 Category: Rehabilitation Authors: Siqi Cai, Guofeng Li, Xiaoya Zhang, Shuangyuan Huang, Haiqing Zheng, Ke Ma and Longhan Xie Tags: Research Source Type: research
What is the impact of user affect on motor learning in virtual environments after stroke? A scoping review
The purported affective impact of virtual reality (VR) and active video gaming (AVG) systems is a key marketing strategy underlying their use in stroke rehabilitation, yet little is known as to how affective c...
Source: Journal of NeuroEngineering and Rehabilitation - June 27, 2019 Category: Rehabilitation Authors: Nina Rohrbach, Emily Chicklis and Danielle Elaine Levac Tags: Review Source Type: research
Correction to: Dissociating motor learning from recovery in exoskeleton training post-stroke
The original article [1] contained an error whereby the co-author, Karima Bakhti ’s name was displayed incorrectly.
Source: Journal of NeuroEngineering and Rehabilitation - December 17, 2018 Category: Rehabilitation Authors: Nicolas Schweighofer, Chunji Wang, Denis Mottet, Isabelle Laffont, Karima Bakhti, David J. Reinkensmeyer and Olivier R émy-Néris Tags: Correction Source Type: research
Dissociating motor learning from recovery in exoskeleton training post-stroke
A large number of robotic or gravity-supporting devices have been developed for rehabilitation of upper extremity post-stroke. Because these devices continuously monitor performance data during training, they ...
Source: Journal of NeuroEngineering and Rehabilitation - October 5, 2018 Category: Rehabilitation Authors: Nicolas Schweighofer, Chunji Wang, Denis Mottet, Isabelle Laffont, Karima Bakthi, David J. Reinkensmeyer and Olivier R émy-Néris Tags: Research Source Type: research
