ArticleScientific data2026
A dataset of paired smartphone and BioPlux accelerometer measurements for vertical jump analysis.
Article in Scientific data, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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6 authors.
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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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