Evaluación de la experiencia del usuario de un sistema de estabilometría para esclerosis múltiple basado en visión por computadora
Evaluation of user experience of a computer vision-based stabilometry system in Multiple Sclerosis
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.
Mostrar biografía de los autores
Computarized posturography is a set of methods and techniques intended to provide objective measures of the balance function of a subject with postural control system alterations, in order to support diagnostic and therapeutic procedures. Modern computerized posturography systems yield accurate and reliable representations of the patient performance, such as force platform-based stabilograms (an account of the center of pressure trajectory along a balance test). However, such tests are quite expensive and usually imply uncomfortable displacements and procedures, such as marker placement protocols. As an alternative, recent developments on video-based stabilometry systems offer portable, low-cost computarized posturography solutions. This work presents an exploratory study on the user experience of the application of such systems in balance function assessment tests for both patients with diagnosis of Multiple Sclerosis and clinical personnel. The reception reported by the survey is highly positive, yet it points out that some improvements in the preparation of clinical staff to interpert stabilometry results are required, and summarized balance function descriptors could be necessary.
Visitas del artículo 739 | Visitas PDF 250
- Bogle LD, Newton RA. Use of the berg balance test to predict falls in elderly persons. Physical therapy. 1996 Jun 01; 76 (6): 576–583. https://doi.org/10.1093/ptj/76.6.576
- Jana A. Kinect for windows SDK programming guide. Packt Publishing Ltd; 2012.
- Judge JO, King MB, Whipple R, Clive J, Wolf LI son. Dynamic balance in older persons: effects of reduced visual and proprioceptive input. The Journals of Gerontology Series A. 1995 sep 01; 50 (5): 263-270. https://doi.org/10.1093/gerona/50A.5.M263
- Nashner LM. Practical biomechanics and physiology of balance. Balance Function Assessment and Management; 2014. 431p.
- Preiningerova JL, Novotna K, Rusz J, Sucha L, Ruzicka E, Havrdova E. Spatial and temporal characteristics of gait as outcome measures in multiple sclerosis (edss 0 to 6.5). Journal of neuroengineering and rehabilitation. 2015; 12 (1): 14. https://doi.org/10.1186/s12984-015-0001-0
- Sosa GD, Sánchez J, Franco H. Improved front-view tracking of human skeleton from kinect data for rehabilitation support in multiple sclerosis. In Signal Processing, Images and Computer Vision (STSIVA). IEEE. 2015 Nov 19; 20th Symposium on, 1–7. https://doi.org/10.1109/STSIVA.2015.7330422
- Sosa GD. Regression model based on real-time video skeleton tracking as a cost-effective alternative to stabilometry. [Master’s thesis]. [Colombia]: Universidad Central and Universidad Jorge Tadeo Lozano; 2018.
- Vaughan CL, Davis BL, Connor J. Dynamics of human gait. 2nd edition. Kiboho Publishers Cape Town. South Africa; 1999.
- Williams B, Chang A, Landefeld C, Ahalt C, Conant R, Chen H. Current Diagnosis and Treatment: Geriatrics 2E. McGraw Hill Professional. 2014.