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Distance Measurements by Taping01:18

Distance Measurements by Taping

Tapes are essential in surveying for accurate, durable, and short-distance measurements. Made from lightweight, nylon-coated steel, they offer flexibility and strength for rugged outdoor use. The nylon coating protects against rust and wear, extending the tape's life. Standard lengths, around 30 meters, are marked in meters and millimeters for precision.Surveyors select tapes based on site conditions and accuracy needs. Lightweight, nylon-coated tapes are commonly used for ease of handling and...

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A Physics-Based Digital Twin for Trail Running Race Performance Prediction: A Proof-of-Concept Study.

Diego Jaén-Carrillo1, Daniel Pattis2

  • 1Department of Sport Science, University of Innsbruck, 6020 Innsbruck, Austria.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

This study introduces a physics-based digital twin for trail running, improving race time predictions by integrating terrain adaptation and physiological calibration. The model accurately forecasts performance across varied trail conditions, enhancing athlete preparation.

Keywords:
biomechanicsgrade-adjusted pacemodellingoff-roadperformance prediction

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Area of Science:

  • Sports Science
  • Biomechanics
  • Physiology

Background:

  • Trail running presents unique biomechanical challenges due to irregular terrain, making traditional pacing models insufficient.
  • Accurate prediction of trail running performance requires accounting for variable terrain and individual physiological responses.

Purpose of the Study:

  • To develop and validate a physics-based digital twin for predicting trail running finish times.
  • To integrate a terrain-adaptive grade-adjusted pace (GAP) model with individual physiological calibration.

Main Methods:

  • Utilized a physics-based digital twin incorporating a grade-adjusted pace (GAP) model with Minetti's metabolic cost polynomial.
  • Incorporated altitude-VO2max correction, a fatigue term (TRIMP-based), and pacing decay.
  • Calibrated individual parameters using race data and validated the model through sequential and leave-one-out cross-validation.

Main Results:

  • Sequential validation showed model accuracy (R²) increasing from 0.763 to 0.905.
  • Leave-one-out cross-validation yielded R² = 0.864, with a Mean Absolute Error of 18.2 minutes.
  • The model demonstrated a small positive bias (+2.0 min), indicating reliable prediction accuracy.

Conclusions:

  • Integrating biomechanical terrain correction with physiological calibration significantly enhances trail running race-time prediction.
  • The developed framework provides a foundation for athlete-specific pre-race simulations in trail running.