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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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6766792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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