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Physically interactive robotic technology for neuromotor rehabilitation
Abstract
Robotic technology can provide innovative responses to the severe challenges of providing cost-effective care to restore sensory-motor function following neurological and biomechanical injury. It may be deployed at several points on a continuum of care, to provide precisely controlled sensory-motor therapy to ameliorate disability and promote recovery of function, or to provide assistance to compensate for functions that cannot be recovered, or to replace limbs lost irretrievably. This chapter reviews recent progress using robotic technology to capitalize on neural plasticity and promote recovery after neurological injury such as stroke (cerebral vascular accident), research on brain-computer interfaces as a source of control signals for assistive technologies, and research on high-performance multiple-degree-of-freedom upper-extremity prosthetic limbs.
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“Once the relevant features were identified, ensemble models comprising 10 neural network predictors were constructed using the same network topology and training parameters but initialized with a different random number seed. The predictions of these models were averaged to produce an ensemble prediction.”
From QSAR without borders (2020) · cites this paper as [277]
In the citing paper
“All models were cross-validated using the standard jackknife approach that divided the training data into 10 disjoint subsets containing 10% of the patterns each, systematically removing each subset from the training set, building a model with the remaining patterns, and predicting the clinical scores of the removed patterns using the optimized network parameters.”
From QSAR without borders (2020) · cites this paper as [277]
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“The resulting models recapitulated the human scored clinical scales with a cross-validated R2 of 0.73, 0.75, 0.63, and 0.60 for the FM, MP, NIH and MR scales, respectively.”
From QSAR without borders (2020) · cites this paper as [277]
In the citing paper
“The models also showed lower but still useful predictive power for the external validation set (non-completers).”
From QSAR without borders (2020) · cites this paper as [277]
In the citing paper
“Finally, the models were used to derive novel composite robotic endpoints with improved sensitivity (and effect size) compared to existing scales.”
From QSAR without borders (2020) · cites this paper as [277]
In the citing paper
“Finally, the best models identified by cross-validation were used to predict performance of the non-completers, who formed an independent test set.”
From QSAR without borders (2020) · cites this paper as [277]
In the citing paper
“The models had better prediction accuracy for the FM and MP scales that are more closely related to motor function than the NIH and MR metrics.”
From QSAR without borders (2020) · cites this paper as [277]
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Citations
Cites
Learning to control a brain–machine interface for reaching and grasping by primates
Carmena, J. M., Lebedev, M. A., Crist, R. E., O'Doherty, J. E., Santucci, D. M., Dimitrov, D. F., Patil, P. G., Henriquez, C. S., & Nicolelis, M. A. L. (2003) · PLoS Biology · doi:10.1371/journal.pbio.0000042 · Cited by 1,761
Robot-Assisted Therapy for Long-Term Upper-Limb Impairment after Stroke
Lo, A. C., Guarino, P. D., Richards, L. G., Haselkorn, J. K., Wittenberg, G. F., Federman, D. G., Ringer, R. J., Wagner, T. H., Krebs, H. I., Volpe, B. T., et al. (2010) · New England Journal of Medicine · doi:10.1056/nejmoa0911341 · Cited by 1,397
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Cited by
Sorted by citations
Muratov, E. N., Bajorath, J., Sheridan, R. P., Tetko, I. V., Filimonov, D., Poroikov, V., Oprea, T. I., Baskin, I. I., Varnek, A., Roitberg, A., et al. (2020) · Chemical Society Reviews · doi:10.1039/d0cs00098a · Cited by 913
Metadata
- Authors
- Hogan, N., & Krebs, H. I.
- Affiliations
- Massachusetts Institute of Technology (US)
- Corresponding author
- Hogan, Neville
- Venue
- Progress in Brain Research
- Publisher
- Elsevier
- Published
- 2011
- Volume
- 192
- Pages
- 59-68
- ISSN
- 0079-6123 (ISSN-L)
- OpenAlex
- W184818863
- Cited by
- 26
- References
- 2
- Citations by year
- 2026: 1 · 2025: 2 · 2021: 2 · 2020: 3 · 2019: 1 · 2018: 1 · 2017: 3 · 2015: 1 · 2014: 5 · 2013: 5 · 2012: 2
- FWCI
- 15.75
- Type
- book chapter
- Language
- en
- Open access
- closed
- Locations
- Progress in brain research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- U.S. Department of Veterans Affairs
- Toyota Motor Corporation
- Rehabilitation Research and Development Service
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD045343)
- Topics
- Health Sciences › Medicine › Rehabilitation › Stroke Rehabilitation and Recovery
- Physical Sciences › Engineering › Biomedical Engineering › Prosthetics and Rehabilitation Robotics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Keywords
- rehabilitation robotics, neuromotor rehabilitation, stroke, neuroplasticity, brain-computer interface, assistive technology, upper limb prosthesis, neurological injury
- MeSH terms
- Stroke Rehabilitation, Cost-Benefit Analysis, Humans, Neuronal Plasticity, Robotics, User-Computer Interface, Randomized Controlled Trials as Topic, Prostheses and Implants, Recovery of Function, Trauma, Nervous System, Stroke
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