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

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...

Full description

Bibliographic Details
Main Authors: Swagata Das, Wataru Sakoda, Priyanka Ramasamy, Ramin Tadayon, Antonio Vega Ramirez, Yuichi Kurita
Format: Article
Language:English
Published: MDPI AG 2021-09-01
Series:Sensors
Subjects:
artificial neural network (ANN)
Random Forest regressor
skill assessment
squat
one-leg standing
locomotive syndrome
Online Access:https://www.mdpi.com/1424-8220/21/19/6459

Internet

https://www.mdpi.com/1424-8220/21/19/6459

Similar Items