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<p>This study presents and validates a dataset designed to evaluate the accuracy of a smartphone application for measuring vertical jump time. A total of 550 trials were recorded, with jump flight time simultaneously measured by a smartphone (Android) and a reference wearable accelerometer (BioPlux). Two predictive models, Least Squares (LSQ) and Multilayer Perceptron (MLP), were trained to estimate BioPlux flight time from smartphone readings. The LSQ model achieved a mean error of 0.43% and a mean absolute error of 5.32%, while the MLP model obtained 1.2% and 5.36%, respectively. Both models showed low average percentage error relative to the reference system. This work provides a robust dataset and modeling framework for evaluating low-cost, mobile-based movement assessment tools, with applications in neurology, rehabilitation, and sports biomechanics.</p>

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A dataset of paired smartphone and BioPlux accelerometer measurements for vertical jump analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月6日

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  • Youssef Radid,
  • Nuno M. Garcia,
  • Saif Al-jumaili,
  • Frederico Branco,
  • Paulo Jorge Coelho,
  • Ivan Miguel Pires

摘要

This study presents and validates a dataset designed to evaluate the accuracy of a smartphone application for measuring vertical jump time. A total of 550 trials were recorded, with jump flight time simultaneously measured by a smartphone (Android) and a reference wearable accelerometer (BioPlux). Two predictive models, Least Squares (LSQ) and Multilayer Perceptron (MLP), were trained to estimate BioPlux flight time from smartphone readings. The LSQ model achieved a mean error of 0.43% and a mean absolute error of 5.32%, while the MLP model obtained 1.2% and 5.36%, respectively. Both models showed low average percentage error relative to the reference system. This work provides a robust dataset and modeling framework for evaluating low-cost, mobile-based movement assessment tools, with applications in neurology, rehabilitation, and sports biomechanics.