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Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions
The progressive loss of functional capacity due to aging is a serious problem that can compromise human locomotion capacity, requiring the help of an assistant and reducing independence. The NanoStim project aims to develop a system capable of performing treatment with electrostimulation at the pati...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572597/ https://www.ncbi.nlm.nih.gov/pubmed/36236708 http://dx.doi.org/10.3390/s22197605 |
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author | Franco, Tiago Sestrem, Leonardo Henriques, Pedro Rangel Alves, Paulo Varanda Pereira, Maria João Brandão, Diego Leitão, Paulo Silva, Alfredo |
author_facet | Franco, Tiago Sestrem, Leonardo Henriques, Pedro Rangel Alves, Paulo Varanda Pereira, Maria João Brandão, Diego Leitão, Paulo Silva, Alfredo |
author_sort | Franco, Tiago |
collection | PubMed |
description | The progressive loss of functional capacity due to aging is a serious problem that can compromise human locomotion capacity, requiring the help of an assistant and reducing independence. The NanoStim project aims to develop a system capable of performing treatment with electrostimulation at the patient’s home, reducing the number of consultations. The knee angle is one of the essential attributes in this context, helping understand the patient’s movement during the treatment session. This article presents a wearable system that recognizes the knee angle through IMU sensors. The hardware chosen for the wearables are low cost, including an ESP32 microcontroller and an MPU-6050 sensor. However, this hardware impairs signal accuracy in the multitasking environment expected in rehabilitation treatment. Three optimization filters with algorithmic complexity [Formula: see text] were tested to improve the signal’s noise. The complementary filter obtained the best result, presenting an average error of 0.6 degrees and an improvement of 77% in MSE. Furthermore, an interface in the mobile app was developed to respond immediately to the recognized movement. The systems were tested with volunteers in a real environment and could successfully measure the movement performed. In the future, it is planned to use the recognized angle with the electromyography sensor. |
format | Online Article Text |
id | pubmed-9572597 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-95725972022-10-17 Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions Franco, Tiago Sestrem, Leonardo Henriques, Pedro Rangel Alves, Paulo Varanda Pereira, Maria João Brandão, Diego Leitão, Paulo Silva, Alfredo Sensors (Basel) Article The progressive loss of functional capacity due to aging is a serious problem that can compromise human locomotion capacity, requiring the help of an assistant and reducing independence. The NanoStim project aims to develop a system capable of performing treatment with electrostimulation at the patient’s home, reducing the number of consultations. The knee angle is one of the essential attributes in this context, helping understand the patient’s movement during the treatment session. This article presents a wearable system that recognizes the knee angle through IMU sensors. The hardware chosen for the wearables are low cost, including an ESP32 microcontroller and an MPU-6050 sensor. However, this hardware impairs signal accuracy in the multitasking environment expected in rehabilitation treatment. Three optimization filters with algorithmic complexity [Formula: see text] were tested to improve the signal’s noise. The complementary filter obtained the best result, presenting an average error of 0.6 degrees and an improvement of 77% in MSE. Furthermore, an interface in the mobile app was developed to respond immediately to the recognized movement. The systems were tested with volunteers in a real environment and could successfully measure the movement performed. In the future, it is planned to use the recognized angle with the electromyography sensor. MDPI 2022-10-07 /pmc/articles/PMC9572597/ /pubmed/36236708 http://dx.doi.org/10.3390/s22197605 Text en © 2022 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 Franco, Tiago Sestrem, Leonardo Henriques, Pedro Rangel Alves, Paulo Varanda Pereira, Maria João Brandão, Diego Leitão, Paulo Silva, Alfredo Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions |
title | Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions |
title_full | Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions |
title_fullStr | Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions |
title_full_unstemmed | Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions |
title_short | Motion Sensors for Knee Angle Recognition in Muscle Rehabilitation Solutions |
title_sort | motion sensors for knee angle recognition in muscle rehabilitation solutions |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572597/ https://www.ncbi.nlm.nih.gov/pubmed/36236708 http://dx.doi.org/10.3390/s22197605 |
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