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A Grip Strength Estimation Method Using a Novel Flexible Sensor under Different Wrist Angles
It is a considerable challenge to realize the accurate, continuous detection of handgrip strength due to its complexity and uncertainty. To address this issue, a novel grip strength estimation method oriented toward the multi-wrist angle based on the development of a flexible deformation sensor is p...
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/PMC8914750/ https://www.ncbi.nlm.nih.gov/pubmed/35271152 http://dx.doi.org/10.3390/s22052002 |
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author | Wang, Yina Zheng, Liwei Yang, Junyou Wang, Shuoyu |
author_facet | Wang, Yina Zheng, Liwei Yang, Junyou Wang, Shuoyu |
author_sort | Wang, Yina |
collection | PubMed |
description | It is a considerable challenge to realize the accurate, continuous detection of handgrip strength due to its complexity and uncertainty. To address this issue, a novel grip strength estimation method oriented toward the multi-wrist angle based on the development of a flexible deformation sensor is proposed. The flexible deformation sensor consists of a foaming sponge, a Hall sensor, an LED, and photoresistors (PRs), which can measure the deformation of muscles with grip strength. When the external deformation squeezes the foaming sponge, its density and light intensity change, which is detected by a light-sensitive resistor. The light-sensitive resistor extended to the internal foaming sponge with illuminance complies with the extrusion of muscle deformation to enable relative muscle deformation measurement. Furthermore, to achieve the speed, accuracy, and continuous detection of grip strength with different wrist angles, a new grip strength-arm muscle model is adopted and a one-dimensional convolutional neural network based on the dynamic window is proposed to recognize wrist joints. Finally, all the experimental results demonstrate that our proposed flexible deformation sensor can accurately detect the muscle deformation of the arm, and the designed muscle model and convolutional neural network can continuously predict hand grip at different wrist angles in real-time. |
format | Online Article Text |
id | pubmed-8914750 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-89147502022-03-12 A Grip Strength Estimation Method Using a Novel Flexible Sensor under Different Wrist Angles Wang, Yina Zheng, Liwei Yang, Junyou Wang, Shuoyu Sensors (Basel) Article It is a considerable challenge to realize the accurate, continuous detection of handgrip strength due to its complexity and uncertainty. To address this issue, a novel grip strength estimation method oriented toward the multi-wrist angle based on the development of a flexible deformation sensor is proposed. The flexible deformation sensor consists of a foaming sponge, a Hall sensor, an LED, and photoresistors (PRs), which can measure the deformation of muscles with grip strength. When the external deformation squeezes the foaming sponge, its density and light intensity change, which is detected by a light-sensitive resistor. The light-sensitive resistor extended to the internal foaming sponge with illuminance complies with the extrusion of muscle deformation to enable relative muscle deformation measurement. Furthermore, to achieve the speed, accuracy, and continuous detection of grip strength with different wrist angles, a new grip strength-arm muscle model is adopted and a one-dimensional convolutional neural network based on the dynamic window is proposed to recognize wrist joints. Finally, all the experimental results demonstrate that our proposed flexible deformation sensor can accurately detect the muscle deformation of the arm, and the designed muscle model and convolutional neural network can continuously predict hand grip at different wrist angles in real-time. MDPI 2022-03-04 /pmc/articles/PMC8914750/ /pubmed/35271152 http://dx.doi.org/10.3390/s22052002 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 Wang, Yina Zheng, Liwei Yang, Junyou Wang, Shuoyu A Grip Strength Estimation Method Using a Novel Flexible Sensor under Different Wrist Angles |
title | A Grip Strength Estimation Method Using a Novel Flexible Sensor under Different Wrist Angles |
title_full | A Grip Strength Estimation Method Using a Novel Flexible Sensor under Different Wrist Angles |
title_fullStr | A Grip Strength Estimation Method Using a Novel Flexible Sensor under Different Wrist Angles |
title_full_unstemmed | A Grip Strength Estimation Method Using a Novel Flexible Sensor under Different Wrist Angles |
title_short | A Grip Strength Estimation Method Using a Novel Flexible Sensor under Different Wrist Angles |
title_sort | grip strength estimation method using a novel flexible sensor under different wrist angles |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8914750/ https://www.ncbi.nlm.nih.gov/pubmed/35271152 http://dx.doi.org/10.3390/s22052002 |
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