English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Although running is a common leisure activity and a core training regimentfor several athletes, between $29\%$ and $79\%$ of runners sustain an overuseinjury each year. These injuries are linked to excessive fatigue, which altershow someone runs. In this work, we explore the feasibility of modelling theBorg received perception of exertion (RPE) scale (range: $[6-20]$), awell-validated subjective measure of fatigue, using audio data captured inrealistic outdoor environments via smartphones attached to the runners' arms.Using convolutional neural networks (CNNs) on log-Mel spectrograms, we obtain amean absolute error of $2.35$ in subject-dependent experiments, demonstratingthat audio can be effectively used to model fatigue, while being more easilyand non-invasively acquired than by signals from other sensors.

Fatigue Prediction in Outdoor Running Conditions using Audio Data