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<p>This manuscript presents an algorithm for the calculation of the ratio between the real-time maximal mean speed (or power) output and the individual’s historical maximum. The algorithm has been developed using the Garmin Connect IQ SDK, and it runs on compatible devices, such as cycling and running computers. For outdoor running activities, a Kalman Filter (KF) was developed to estimate road gradients and compute ‘gradient-adjusted speed,’ ensuring accurate performance evaluation in varied terrains. Computational times were estimated at ~ 2&#xa0;ms during cycling and ~ 12&#xa0;ms during running, depending on whether the KF was active. The application of this simple methodology on a wearable device, combined with the integration with a web-app for data collection and exchange, makes this contribution novel and significant for the field.</p>

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Real-time assessment of exercising maximal mean power and speed in endurance sports: a Garmin connect IQ App

  • 期刊论文

  • 发表日期：

    2025年10月22日

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  • Andrea Zignoli,
  • Phil Whitehurst

摘要

This manuscript presents an algorithm for the calculation of the ratio between the real-time maximal mean speed (or power) output and the individual’s historical maximum. The algorithm has been developed using the Garmin Connect IQ SDK, and it runs on compatible devices, such as cycling and running computers. For outdoor running activities, a Kalman Filter (KF) was developed to estimate road gradients and compute ‘gradient-adjusted speed,’ ensuring accurate performance evaluation in varied terrains. Computational times were estimated at ~ 2 ms during cycling and ~ 12 ms during running, depending on whether the KF was active. The application of this simple methodology on a wearable device, combined with the integration with a web-app for data collection and exchange, makes this contribution novel and significant for the field.