Multifractal Analysis of Physiological Signals: A Novel Approach to Optimizing Pacing Strategy in a Pilot Study

TL;DR

This paper introduces a multifractal analysis method to classify physiological signals from marathon runners, providing new insights into performance differences and potential strategies for improvement.

Contribution

It presents a novel application of multifractal techniques to physiological data for classifying runner performance and optimizing pacing strategies.

Findings

Multifractal parameters differentiate runners of varying skill levels.

The analysis offers new insights into physiological patterns during marathons.

Potential for personalized pacing advice based on multifractal analysis.

Abstract

Marathons are one of the ultimate challenges of human endeavor. In this paper, we apply recently introduced multifractal techniques which yield a new classification parameter in the processing of physiological data captured on marathon runners. The comparison of their values gives a new insight on the way that runners of different level conduct their run, and ultimately, can be used in order to give advice on how to improve their performance.