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Field-based assessments of athletic performance often require extensive resources and supervision, which can be limited by organizational funding. We present a low-cost IoT system designed to automate multi-stage running tests with biometric authentication and real-time analytics. Participants verify identity via fingerprint recognition, while wireless ecosound gates capture split times along configurable courses. Microcontrollers with LPWAN transmit timestamps to a local device and, if available, to a cloud server for processing and ranking. The read times are corrected for instant latency using a recurrent diagnosis strategy based on ping tests, and the device is tested for accuracy using the analytical period of a pendulum, yielding an uncertainty of \(\pm 3.8\) ms, which is far faster than the reported \(\pm 100\) ms for human assessments. This, in addition to the improved reproducibility and reduced preparation, analysis, and reporting times. The developed automation is intended to be massively used in clubs’ diverse disciplines to gather coherent and accurate data intended for artificial intelligence implementations.

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Introducing an IoT-Based Strategy to Measure Athletes in Field Testing Automatically

  • 会议论文

  • 发表日期：

    2026年

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  • Daniel A. Choco Carabali,
  • Juan S. Serrano Fontecha,
  • Daniel S. Forero Arévalo,
  • Asfur Barandica Lopez,
  • Fernando Yepes-Calderon

摘要

Field-based assessments of athletic performance often require extensive resources and supervision, which can be limited by organizational funding. We present a low-cost IoT system designed to automate multi-stage running tests with biometric authentication and real-time analytics. Participants verify identity via fingerprint recognition, while wireless ecosound gates capture split times along configurable courses. Microcontrollers with LPWAN transmit timestamps to a local device and, if available, to a cloud server for processing and ranking. The read times are corrected for instant latency using a recurrent diagnosis strategy based on ping tests, and the device is tested for accuracy using the analytical period of a pendulum, yielding an uncertainty of \(\pm 3.8\) ms, which is far faster than the reported \(\pm 100\) ms for human assessments. This, in addition to the improved reproducibility and reduced preparation, analysis, and reporting times. The developed automation is intended to be massively used in clubs’ diverse disciplines to gather coherent and accurate data intended for artificial intelligence implementations.