Gait Speed Estimation Based on Artificial Neural Network: Comparison with the Application of the Inverted Pendulum Model

Author:

Castellanos Gianna Arencibia,Montero Fidel E. Hernández,Rodríguez Tania Y. Aznielle

Publisher

Springer International Publishing

Reference22 articles.

1. Schlachetzki, J.C.M., Barth, J., Marxreiter, F., Gossler, J., Kohl, Z., Reinfelder, S., et al.: Wearable sensors objectively measure gait parameters in Parkinson’s disease. PLoS ONE 12(10), 1–18 (2017)

2. Agustín, D.G., Morejón, C.D.D.S., Pérez, Z.R.: Physical performance tests in the prognosis of adverse outcomes in the elderly. MEDISAN 22(6), 466–470 (2018)

3. García-Agustin, D., Morgade-Fonte, R.M., Bobes, M.A., Galán-García, L., Rodríguez-Rodríguez, V.: Association between gait speed decline and EEG abnormalities in a cohort of active older adults living in the community. medRxiv, 1 January 2020; 2020.05.03.20089540. http://medrxiv.org/content/early/2020/05/08/2020.05.03.20089540.abstract

4. Munguia, L., Solis, V., Meaney, E., Ramirez-Sanchez, I., Villarreal, F., Perez-Duran, J., et al.: Association of physical performance tests with frailty indicators and oxidative stress markers in a sample of a community-dwelling elderly population. Biomed Res. 29(17), 3344–3350 (2018)

5. Terrier, P., Schutz, Y.: How useful is satellite positioning system (GPS) to track gait parameters? A review. J. Neuroeng. Rehabil. 2, 1–11 (2005)

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