Cargando…
Key components of mechanical work predict outcomes in robotic stroke therapy
BACKGROUND: Clinical practice typically emphasizes active involvement during therapy. However, traditional approaches can offer only general guidance on the form of involvement that would be most helpful to recovery. Beyond assisting movement, robots allow comprehensive methods for measuring practic...
Autores principales: | Wright, Zachary A., Majeed, Yazan A., Patton, James L., Huang, Felix C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7175566/ https://www.ncbi.nlm.nih.gov/pubmed/32316977 http://dx.doi.org/10.1186/s12984-020-00672-8 |
Ejemplares similares
-
Regression techniques employing feature selection to predict clinical outcomes in stroke
por: Abdel Majeed, Yazan, et al.
Publicado: (2018) -
Effects of robot viscous forces on arm movements in chronic stroke survivors: a randomized crossover study
por: Abdel Majeed, Yazan, et al.
Publicado: (2020) -
Haptic/Graphic Rehabilitation: Integrating a Robot into a Virtual Environment Library and Applying it to Stroke Therapy
por: Sharp, Ian, et al.
Publicado: (2011) -
Robot-assisted Therapy in Stroke Rehabilitation
por: Chang, Won Hyuk, et al.
Publicado: (2013) -
A novel method for the quantification of key components of manual dexterity after stroke
por: Térémetz, Maxime, et al.
Publicado: (2015)