Estimating the Standing Long Jump Length from Smartphone Inertial Sensors through Machine Learning Algorithms

The length of the standing long jump (SLJ) is widely recognized as an indicator of developmental motor competence or sports conditional performance. This work aims at defining a methodology to allow athletes/coaches to easily measure it using the inertial measurement units embedded on a smartphone. A sample group of 114 trained young participants was recruited and asked to perform the instrumented SLJ task. A set of features was identified based on biomechanical knowledge, then Lasso regression allowed the identification of a subset of predictors of the SLJ length that was used as input of different optimized machine learning architectures. Results obtained from the use of the proposed configuration allow an estimate of the SLJ length with a Gaussian Process Regression model with a RMSE of 0.122 m in the test phase, Kendall's t < 0.1. The proposed models give homoscedastic results, meaning that the error of the models does not depend on the estimated quantity. This study proved the feasibility of using low-cost smartphone sensors to provide an automatic and objective estimate of SLJ performance in ecological settings.

Automated summary

This study aims to define a methodology using smartphone inertial measurement units to measure standing long jump (SLJ) easily. A group of 114 trained young participants performed the instrumented SLJ task and a subset of predictors of SLJ length was identified using Lasso regression and optimized machine learning architectures. The proposed configuration achieved an estimate of SLJ length with a RMSE of 0.122m and Kendall's t < 0.1 using a Gaussian Process Regression model. This study demonstrates the feasibility of using low-cost smartphone sensors for automatic and objective SLJ performance estimate in ecological settings.