| Title: |
Combining Smartphone Inertial Sensors and Machine Learning Algorithms to Estimate Power Variables in Standing Long Jump. |
| Authors: |
De Lazzari, Beatrice, Vannozzi, Giuseppe, Camomilla, Valentina |
| Source: |
Computers (2073-431X); Feb2025, Vol. 14 Issue 2, p31, 20p |
| Subject Terms: |
MACHINE learning, FEATURE extraction, BROAD jump, SMARTPHONES, HOMOSCEDASTICITY |
| Abstract: |
Standing long jump (SLJ) power is recognized as informative of the ability of lower limbs to exert power. The study aims to provide athletes/coaches with a simple and low-cost estimate of selected SLJ power features. A group of 150 trained young participants was recruited and performed a SLJ task while holding a smartphone, whose inertial sensors were used to collect data. Considering the state-of-the-art in SLJ biomechanics, a set of features was extracted and then selected by Lasso regression and used as inputs to several different optimized machine learning architectures to estimate the SLJ power variables. A Multi-Layer Perceptron Regressor was selected as the best-performing model to estimate total and concentric antero-posterior mean power, with an RMSE of 0.37 W/kg, R2 > 0.70, and test phase homoscedasticity (Kendall's τ < 0.1) in both cases. Model performance was dependent on the dataset size rather than the participants' sex. A Multi-Layer Perceptron Regressor was able to also estimate the antero-posterior peak power (RMSE = 2.34 W/kg; R2 = 0.67), although affected by heteroscedasticity. This study proved the feasibility of combining low-cost smartphone sensors and machine learning to automatically and objectively estimate SLJ power variables in ecological settings. [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |
