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A Sensor Platform for Athletes’ Training Supervision: A Proof of Concept Study

One of the basic needs of professional athletes is the real-time and non-invasive monitoring of their activities. The use of these kind of data is necessary to develop strategies for specific tailored training in order to improve performances. The sensor system presented in this work has the aim to...

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Autores principales: Zompanti, Alessandro, Sabatini, Anna, Santonico, Marco, Grasso, Simone, Gianfelici, Antonio, Donatucci, Bruno, Di Castro, Andrea, Pennazza, Giorgio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6766792/
https://www.ncbi.nlm.nih.gov/pubmed/31547403
http://dx.doi.org/10.3390/s19183948
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author Zompanti, Alessandro
Sabatini, Anna
Santonico, Marco
Grasso, Simone
Gianfelici, Antonio
Donatucci, Bruno
Di Castro, Andrea
Pennazza, Giorgio
author_facet Zompanti, Alessandro
Sabatini, Anna
Santonico, Marco
Grasso, Simone
Gianfelici, Antonio
Donatucci, Bruno
Di Castro, Andrea
Pennazza, Giorgio
author_sort Zompanti, Alessandro
collection PubMed
description One of the basic needs of professional athletes is the real-time and non-invasive monitoring of their activities. The use of these kind of data is necessary to develop strategies for specific tailored training in order to improve performances. The sensor system presented in this work has the aim to adopt a novel approach for the monitoring of physiological parameters, and athletes’ performances, during their training. The anaerobic threshold is herein identified with the monitoring of the lactate concentration and the respiratory parameters. The data collected by the sensor are used to build a model using a supervised method (based on the partial least squares method, PLS) to predict the values of the parameters of interest. The sensor is able to measure the lactate concentration from a sample of saliva and it can estimate a respiratory parameter, such as maximal oxygen consumption, maximal carbon dioxide production and respiratory rate from a sample of exhaled breath. The main advantages of the device are the low power; the wireless communication; and the non-invasive sampling method, which allow its use in a real context of sport practice.
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spelling pubmed-67667922019-10-02 A Sensor Platform for Athletes’ Training Supervision: A Proof of Concept Study Zompanti, Alessandro Sabatini, Anna Santonico, Marco Grasso, Simone Gianfelici, Antonio Donatucci, Bruno Di Castro, Andrea Pennazza, Giorgio Sensors (Basel) Article One of the basic needs of professional athletes is the real-time and non-invasive monitoring of their activities. The use of these kind of data is necessary to develop strategies for specific tailored training in order to improve performances. The sensor system presented in this work has the aim to adopt a novel approach for the monitoring of physiological parameters, and athletes’ performances, during their training. The anaerobic threshold is herein identified with the monitoring of the lactate concentration and the respiratory parameters. The data collected by the sensor are used to build a model using a supervised method (based on the partial least squares method, PLS) to predict the values of the parameters of interest. The sensor is able to measure the lactate concentration from a sample of saliva and it can estimate a respiratory parameter, such as maximal oxygen consumption, maximal carbon dioxide production and respiratory rate from a sample of exhaled breath. The main advantages of the device are the low power; the wireless communication; and the non-invasive sampling method, which allow its use in a real context of sport practice. MDPI 2019-09-12 /pmc/articles/PMC6766792/ /pubmed/31547403 http://dx.doi.org/10.3390/s19183948 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zompanti, Alessandro
Sabatini, Anna
Santonico, Marco
Grasso, Simone
Gianfelici, Antonio
Donatucci, Bruno
Di Castro, Andrea
Pennazza, Giorgio
A Sensor Platform for Athletes’ Training Supervision: A Proof of Concept Study
title A Sensor Platform for Athletes’ Training Supervision: A Proof of Concept Study
title_full A Sensor Platform for Athletes’ Training Supervision: A Proof of Concept Study
title_fullStr A Sensor Platform for Athletes’ Training Supervision: A Proof of Concept Study
title_full_unstemmed A Sensor Platform for Athletes’ Training Supervision: A Proof of Concept Study
title_short A Sensor Platform for Athletes’ Training Supervision: A Proof of Concept Study
title_sort sensor platform for athletes’ training supervision: a proof of concept study
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6766792/
https://www.ncbi.nlm.nih.gov/pubmed/31547403
http://dx.doi.org/10.3390/s19183948
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