Maximum-Entropy-Rate Selection of Features for Classifying Changes in Knee and Ankle Dynamics During Running
Abstract
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.
0
Turn this paper into a full lesson
ArcXiv compiles a staged curriculum from this paper: 8-12 lessons across beginner → advanced, synthesised section guides, visuals, flashcards, a quiz, exercises, and on-demand deep dives per section. Grounded in the abstract, never invented.