Maximum-Entropy-Rate Selection of Features for Classifying Changes in Knee and Ankle Dynamics During Running
Author(s)
Einicke, Garry Allan
Sabti, Haider A
Thiel, David
Fernandez, Marta
Griffith University Author(s)
Year published
2018
Metadata
Show full item recordAbstract
This paper investigates deteriorations in knee and ankle dynamics during running. Changes in lower limb accelerations are analyzed by a wearable musculo-skeletal monitoring system. The system employs a machine learning technique to classify joint stiffness. A maximum-entropyrate method is developed to select the most relevant features. Experimental results demonstrate that distance travelled and energy expended can be estimated from observed changes in knee and ankle motions during 5 km runs.This paper investigates deteriorations in knee and ankle dynamics during running. Changes in lower limb accelerations are analyzed by a wearable musculo-skeletal monitoring system. The system employs a machine learning technique to classify joint stiffness. A maximum-entropyrate method is developed to select the most relevant features. Experimental results demonstrate that distance travelled and energy expended can be estimated from observed changes in knee and ankle motions during 5 km runs.
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Journal Title
IEEE Journal of Biomedical and Health Informatics
Note
This publication has been entered into Griffith Research Online as an Advanced Online Version.
Subject
Biomechanical engineering