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Integrated Pedal System for Data Driven Rehabilitation
We present a system capable of providing visual feedback for ergometer training, allowing detailed analysis and gamification. The presented solution can easily upgrade any existing ergometer device. The system consists of a set of pedals with embedded sensors, readout electronics and wireless commun...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8662464/ https://www.ncbi.nlm.nih.gov/pubmed/34884118 http://dx.doi.org/10.3390/s21238115 |
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author | Schaer, Alessandro Helander, Oskar Buffa, Francesco Müller, Alexis Schneider, Kevin Maurenbrecher, Henrik Becsek, Barna Chatzipirpiridis, George Ergeneman, Olgac Pané, Salvador Nelson, Bradley J. Schaffert, Nina |
author_facet | Schaer, Alessandro Helander, Oskar Buffa, Francesco Müller, Alexis Schneider, Kevin Maurenbrecher, Henrik Becsek, Barna Chatzipirpiridis, George Ergeneman, Olgac Pané, Salvador Nelson, Bradley J. Schaffert, Nina |
author_sort | Schaer, Alessandro |
collection | PubMed |
description | We present a system capable of providing visual feedback for ergometer training, allowing detailed analysis and gamification. The presented solution can easily upgrade any existing ergometer device. The system consists of a set of pedals with embedded sensors, readout electronics and wireless communication modules and a tablet device for interaction with the users, which can be mounted on any ergometer, transforming it into a full analytical assessment tool with interactive training capabilities. The methods to capture the forces and moments applied to the pedal, as well as the pedal’s angular position, were validated using reference sensors and high-speed video capture systems. The mean-absolute error (MAE) for load is found to be 18.82 N, 25.35 N, 0.153 Nm for [Formula: see text] , [Formula: see text] and [Formula: see text] respectively and the MAE for the pedal angle is 13.2°. A fully gamified experience of ergometer training has been demonstrated with the presented system to enhance the rehabilitation experience with audio visual feedback, based on measured cycling parameters. |
format | Online Article Text |
id | pubmed-8662464 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86624642021-12-11 Integrated Pedal System for Data Driven Rehabilitation Schaer, Alessandro Helander, Oskar Buffa, Francesco Müller, Alexis Schneider, Kevin Maurenbrecher, Henrik Becsek, Barna Chatzipirpiridis, George Ergeneman, Olgac Pané, Salvador Nelson, Bradley J. Schaffert, Nina Sensors (Basel) Article We present a system capable of providing visual feedback for ergometer training, allowing detailed analysis and gamification. The presented solution can easily upgrade any existing ergometer device. The system consists of a set of pedals with embedded sensors, readout electronics and wireless communication modules and a tablet device for interaction with the users, which can be mounted on any ergometer, transforming it into a full analytical assessment tool with interactive training capabilities. The methods to capture the forces and moments applied to the pedal, as well as the pedal’s angular position, were validated using reference sensors and high-speed video capture systems. The mean-absolute error (MAE) for load is found to be 18.82 N, 25.35 N, 0.153 Nm for [Formula: see text] , [Formula: see text] and [Formula: see text] respectively and the MAE for the pedal angle is 13.2°. A fully gamified experience of ergometer training has been demonstrated with the presented system to enhance the rehabilitation experience with audio visual feedback, based on measured cycling parameters. MDPI 2021-12-04 /pmc/articles/PMC8662464/ /pubmed/34884118 http://dx.doi.org/10.3390/s21238115 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Schaer, Alessandro Helander, Oskar Buffa, Francesco Müller, Alexis Schneider, Kevin Maurenbrecher, Henrik Becsek, Barna Chatzipirpiridis, George Ergeneman, Olgac Pané, Salvador Nelson, Bradley J. Schaffert, Nina Integrated Pedal System for Data Driven Rehabilitation |
title | Integrated Pedal System for Data Driven Rehabilitation |
title_full | Integrated Pedal System for Data Driven Rehabilitation |
title_fullStr | Integrated Pedal System for Data Driven Rehabilitation |
title_full_unstemmed | Integrated Pedal System for Data Driven Rehabilitation |
title_short | Integrated Pedal System for Data Driven Rehabilitation |
title_sort | integrated pedal system for data driven rehabilitation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8662464/ https://www.ncbi.nlm.nih.gov/pubmed/34884118 http://dx.doi.org/10.3390/s21238115 |
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