Cargando…

Machine learning aided jump height estimate democratization through smartphone measures

INTRODUCTION: The peak height reached in a countermovement jump is a well established performance parameter. Its estimate is often entrusted to force platforms or body-worn inertial sensors. To date, smartphones may possibly be used as an alternative for estimating jump height, since they natively e...

Descripción completa

Detalles Bibliográficos
Autores principales: Mascia, Guido, De Lazzari, Beatrice, Camomilla, Valentina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947475/
https://www.ncbi.nlm.nih.gov/pubmed/36845828
http://dx.doi.org/10.3389/fspor.2023.1112739
_version_ 1784892562806407168
author Mascia, Guido
De Lazzari, Beatrice
Camomilla, Valentina
author_facet Mascia, Guido
De Lazzari, Beatrice
Camomilla, Valentina
author_sort Mascia, Guido
collection PubMed
description INTRODUCTION: The peak height reached in a countermovement jump is a well established performance parameter. Its estimate is often entrusted to force platforms or body-worn inertial sensors. To date, smartphones may possibly be used as an alternative for estimating jump height, since they natively embed inertial sensors. METHODS: For this purpose, 43 participants performed 4 countermovement jumps (172 in total) on two force platforms (gold standard). While jumping, participants held a smartphone in their hands, whose inertial sensor measures were recorded. After peak height was computed for both instrumentations, twenty-nine features were extracted, related to jump biomechanics and to signal time-frequency characteristics, as potential descriptors of soft tissues or involuntary arm swing artifacts. A training set (129 jumps – 75%) was created by randomly selecting elements from the initial dataset, the remaining ones being assigned to the test set (43 jumps – 25%). On the training set only, a Lasso regularization was applied to reduce the number of features, avoiding possible multicollinearity. A multi-layer perceptron with one hidden layer was trained for estimating the jump height from the reduced feature set. Hyperparameters optimization was performed on the multi-layer perceptron using a grid search approach with 5-fold cross validation. The best model was chosen according to the minimum negative mean absolute error. RESULTS: The multi-layer perceptron greatly improved the accuracy (4 cm) and precision (4 cm) of the estimates on the test set with respect to the raw smartphone measures estimates (18 and 16 cm, respectively). Permutation feature importance was performed on the trained model in order to establish the influence that each feature had on the outcome. The peak acceleration and the braking phase duration resulted the most influential features in the final model. Despite not being accurate enough, the height computed through raw smartphone measures was still among the most influential features. DISCUSSION: The study, implementing a smartphone-based method for jump height estimates, paves the way to method release to a broader audience, pursuing a democratization attempt.
format Online
Article
Text
id pubmed-9947475
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-99474752023-02-24 Machine learning aided jump height estimate democratization through smartphone measures Mascia, Guido De Lazzari, Beatrice Camomilla, Valentina Front Sports Act Living Sports and Active Living INTRODUCTION: The peak height reached in a countermovement jump is a well established performance parameter. Its estimate is often entrusted to force platforms or body-worn inertial sensors. To date, smartphones may possibly be used as an alternative for estimating jump height, since they natively embed inertial sensors. METHODS: For this purpose, 43 participants performed 4 countermovement jumps (172 in total) on two force platforms (gold standard). While jumping, participants held a smartphone in their hands, whose inertial sensor measures were recorded. After peak height was computed for both instrumentations, twenty-nine features were extracted, related to jump biomechanics and to signal time-frequency characteristics, as potential descriptors of soft tissues or involuntary arm swing artifacts. A training set (129 jumps – 75%) was created by randomly selecting elements from the initial dataset, the remaining ones being assigned to the test set (43 jumps – 25%). On the training set only, a Lasso regularization was applied to reduce the number of features, avoiding possible multicollinearity. A multi-layer perceptron with one hidden layer was trained for estimating the jump height from the reduced feature set. Hyperparameters optimization was performed on the multi-layer perceptron using a grid search approach with 5-fold cross validation. The best model was chosen according to the minimum negative mean absolute error. RESULTS: The multi-layer perceptron greatly improved the accuracy (4 cm) and precision (4 cm) of the estimates on the test set with respect to the raw smartphone measures estimates (18 and 16 cm, respectively). Permutation feature importance was performed on the trained model in order to establish the influence that each feature had on the outcome. The peak acceleration and the braking phase duration resulted the most influential features in the final model. Despite not being accurate enough, the height computed through raw smartphone measures was still among the most influential features. DISCUSSION: The study, implementing a smartphone-based method for jump height estimates, paves the way to method release to a broader audience, pursuing a democratization attempt. Frontiers Media S.A. 2023-02-09 /pmc/articles/PMC9947475/ /pubmed/36845828 http://dx.doi.org/10.3389/fspor.2023.1112739 Text en © 2023 Mascia, De Lazzari and Camomilla. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Sports and Active Living
Mascia, Guido
De Lazzari, Beatrice
Camomilla, Valentina
Machine learning aided jump height estimate democratization through smartphone measures
title Machine learning aided jump height estimate democratization through smartphone measures
title_full Machine learning aided jump height estimate democratization through smartphone measures
title_fullStr Machine learning aided jump height estimate democratization through smartphone measures
title_full_unstemmed Machine learning aided jump height estimate democratization through smartphone measures
title_short Machine learning aided jump height estimate democratization through smartphone measures
title_sort machine learning aided jump height estimate democratization through smartphone measures
topic Sports and Active Living
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9947475/
https://www.ncbi.nlm.nih.gov/pubmed/36845828
http://dx.doi.org/10.3389/fspor.2023.1112739
work_keys_str_mv AT masciaguido machinelearningaidedjumpheightestimatedemocratizationthroughsmartphonemeasures
AT delazzaribeatrice machinelearningaidedjumpheightestimatedemocratizationthroughsmartphonemeasures
AT camomillavalentina machinelearningaidedjumpheightestimatedemocratizationthroughsmartphonemeasures