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Improving Neurorehabilitation of the Upper Limb through Big Data
Published in Ervin Sejdić, Tiago H. Falk, Signal Processing and Machine Learning for Biomedical Big Data, 2018
The objective of the neurorehabilitation process is to aid the recovery of function after injuries to the nervous system, such as in spinal cord injury (SCI) or stroke. The focus is on achieving outcomes that enable the injured individual to participate in society and enjoy a high quality of life [1]. Neurorehabilitation is, by nature, a highly individualized process, in which a multidisciplinary clinical team attempts to address each patient’s unique impairments, priorities, and recovery profile over time. The heterogeneous nature of the process makes it very challenging to extract evidence that can guide clinical best practices and maximize outcomes. In order to overcome this limitation of current neurorehabilitation practice, it will be necessary to leverage new technologies to enable large-scale data gathering and paint an accurate picture of current clinical practices and outcomes. Powerful analytic approaches must then be called upon to disentangle the complex relationships between patient characteristics, interventions, environments, and outcome metrics, and finally optimize the outcomes of the neurorehabilitation process. The science of big data, with its emphasis on large, continuously growing volumes of data from mixed sources, has much to offer for extracting new insights from the large and complex data sets that will become increasingly prevalent in neurorehabilitation,
Developing a Telemonitoring System for Stroke Rehabilitation
Published in Philip D. Bust, Contemporary Ergonomics 2007, 2018
S. Wilson, R. Davies, T. Stone, J. Hammerton, P. Ware, S. Mawson, N. Harris, C. Eccleston, H. Zheng, N. Black, G. Mountain
The use of sensor technology and ICT to deliver rehabilitation services over large distances has the potential to complement traditional neurorehabilitation therapies and increase patient access to supervised neurorehabilitation. But just as usability plays a critical role in the acceptance and efficacy of telemedicine applications (Beith, 1999), the extent to which the components (products, systems, processes and procedures) of a telerehabilitation system are, or are not usable, will influence the acceptance of telerehabilitation and its effectiveness as a model for rehabilitation therapy provision.
Effectiveness of robot-assisted gait training on patients with burns: a preliminary study
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2020
So Young Joo, Seung Yeol Lee, Yoon Soo Cho, Kuem Ju Lee, Sang-Hyun Kim, Cheong Hoon Seo
Robot-assisted gait training (RAGT) has been developed to facilitate neurorehabilitation, and many studies have demonstrated its efficacy and feasibility among stroke and spinal cord injury patients (Yang et al. 2014). Recent studies applied RAGT in patients with musculoskeletal injury such as knee arthritis (Goto et al. 2017; Tanaka et al. 2017). RAGT provides a normal gait pattern with ideal kinematics, which is computer programmed automatically (Bae et al. 2014). Our institution has confirmed the clinical effects of robot-assisted treatment for patients with hand burn injuries. Robot-assisted training for hand burns was performed in patients undergoing STSG, who were initially admitted to the rehabilitation department after epithelialization (Joo et al. 2020). However, no study has investigated the therapeutic effects of RAGT on patients with burn injury. In this study, we hypothesized that the use of RAGT would provide better outcomes in patients with lower extremity burn injury. This study aimed to evaluate the efficacy of RAGT on patients with lower extremity burn for the first time.
Workshops of the seventh international brain-computer interface meeting: not getting lost in translation
Published in Brain-Computer Interfaces, 2019
Jane E. Huggins, Christoph Guger, Erik Aarnoutse, Brendan Allison, Charles W. Anderson, Steven Bedrick, Walter Besio, Ricardo Chavarriaga, Jennifer L. Collinger, An H. Do, Christian Herff, Matthias Hohmann, Michelle Kinsella, Kyuhwa Lee, Fabien Lotte, Gernot Müller-Putz, Anton Nijholt, Elmar Pels, Betts Peters, Felix Putze, Rüdiger Rupp, Gerwin Schalk, Stephanie Scott, Michael Tangermann, Paul Tubig, Thorsten Zander
Clinical neurorehabilitation for neurological injuries including stroke, spinal cord injury (SCI), and traumatic brain injury (TBI) is severely limited by a lack of satisfactory means to restore lost neurological functions. BCIs have increasingly been studied as one such means and may either act as neuroprostheses to replace the lost motor function in those with complete paralysis or as tools that facilitate neural repair mechanisms to improve residual motor functions in patients with partial paralysis. There are many preclinical studies in humans and early phase clinical trials of BCI systems [142,153,155,231–233], but there are no Phase III/pivotal trials to demonstrate safety, efficacy at reducing disability, and reliability for these systems. Consequently, BCI systems are not yet used in mainstream rehabilitation.
Rehabilitation robotics after stroke: a bibliometric literature review
Published in Expert Review of Medical Devices, 2022
Giacomo Zuccon, Basilio Lenzo, Matteo Bottin, Giulio Rosati
Post-stroke rehabilitation robotics is a research field that continues to show great interest for engineers and physicians. In the light of the fast improvement in rehabilitation technology, robotics-based therapy is acquiring more and more value in optimal rehabilitation programs. Robot-mediated neurorehabilitation is a rapidly advancing field that seeks to use advances in robotics, coupled with neuroscience and rehabilitation theories, to develop new methods of treating neurological injuries such as stroke, spinal cord injury and traumatic brain injury.