What question did this study set out to answer?

The aim is to evaluate a smartphone app's accuracy for measuring vertical jump time using a reference accelerometer.

May 8, 2026Open Access

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

Puntos clave

The aim is to evaluate a smartphone app's accuracy for measuring vertical jump time using a reference accelerometer.
A total of 550 jump trials were recorded using a smartphone and a BioPlux accelerometer.
Two predictive models, Least Squares (LSQ) and Multilayer Perceptron (MLP), were developed to analyze the data.
Model performance was assessed based on mean error and mean absolute error in flight time estimation.
The LSQ model achieved a mean error of 0.43% (mean absolute error: 5.32%).
The MLP model achieved a mean error of 1.2% (mean absolute error: 5.36%).
Both models demonstrated low average percentage errors compared to the reference system.

Resumen

Abstract 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.

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