Feature Selection and Validation of a Machine Learning-Based Lower Limb Risk Assessment Tool: A Feasibility Study

Early and self-identification of locomotive degradation facilitates us with awareness and motivation to prevent further deterioration. We propose the usage of nine squat and four one-leg standing exercise features as input parameters to Machine Learning (ML) classifiers in order to perform lower lim...

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Detalles Bibliográficos
Autores principales: Das, Swagata, Sakoda, Wataru, Ramasamy, Priyanka, Tadayon, Ramin, Ramirez, Antonio Vega, Kurita, Yuichi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512098/
https://www.ncbi.nlm.nih.gov/pubmed/34640779
http://dx.doi.org/10.3390/s21196459

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8512098/
https://www.ncbi.nlm.nih.gov/pubmed/34640779
http://dx.doi.org/10.3390/s21196459

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