Cargando…

Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP)

As an alternative to high-cost shoe insole pressure sensors that measure the insole pressure distribution and calculate the center of pressure (CoP), researchers developed a foot sensor with FSR sensors on the bottom of the insole. However, the calculations for the center of pressure and ground reac...

Descripción completa

Detalles Bibliográficos
Autores principales: Choi, Ho Seon, Lee, Chang Hee, Shim, Myounghoon, Han, Jong In, Baek, Yoon Su
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308711/
https://www.ncbi.nlm.nih.gov/pubmed/30544652
http://dx.doi.org/10.3390/s18124349
_version_ 1783383253117304832
author Choi, Ho Seon
Lee, Chang Hee
Shim, Myounghoon
Han, Jong In
Baek, Yoon Su
author_facet Choi, Ho Seon
Lee, Chang Hee
Shim, Myounghoon
Han, Jong In
Baek, Yoon Su
author_sort Choi, Ho Seon
collection PubMed
description As an alternative to high-cost shoe insole pressure sensors that measure the insole pressure distribution and calculate the center of pressure (CoP), researchers developed a foot sensor with FSR sensors on the bottom of the insole. However, the calculations for the center of pressure and ground reaction force (GRF) were not sufficiently accurate because of the fundamental limitations, fixed coordinates and narrow sensing areas, which cannot cover the whole insole. To address these issues, in this paper, we describe an algorithm of virtual forces and corresponding coordinates with an artificial neural network (ANN) for low-cost flexible insole pressure measurement sensors. The proposed algorithm estimates the magnitude of the GRF and the location of the foot plantar CoP. To compose the algorithm, we divided the insole area into six areas and created six virtual forces and the corresponding coordinates. We used the ANN algorithm with the input of magnitudes of FSR sensors, 1st and 2nd derivatives of them to estimate the virtual forces and coordinates. Eight healthy males were selected for data acquisition. They performed an experiment composed of the following motions: standing with weight shifting, walking with 1 km/h and 2 km/h, squatting and getting up from a sitting position to a standing position. The ANN for estimating virtual forces and corresponding coordinates was fitted according to those data, converted to c script, and downloaded to a microcontroller for validation experiments in real time. The results showed an average RMSE the whole experiment of 31.154 N for GRF estimation and 8.07 mm for CoP calibration. The correlation coefficients of the algorithm were 0.94 for GRF, 0.92 and 0.76 for the X and Y coordinate respectively.
format Online
Article
Text
id pubmed-6308711
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-63087112019-01-04 Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP) Choi, Ho Seon Lee, Chang Hee Shim, Myounghoon Han, Jong In Baek, Yoon Su Sensors (Basel) Article As an alternative to high-cost shoe insole pressure sensors that measure the insole pressure distribution and calculate the center of pressure (CoP), researchers developed a foot sensor with FSR sensors on the bottom of the insole. However, the calculations for the center of pressure and ground reaction force (GRF) were not sufficiently accurate because of the fundamental limitations, fixed coordinates and narrow sensing areas, which cannot cover the whole insole. To address these issues, in this paper, we describe an algorithm of virtual forces and corresponding coordinates with an artificial neural network (ANN) for low-cost flexible insole pressure measurement sensors. The proposed algorithm estimates the magnitude of the GRF and the location of the foot plantar CoP. To compose the algorithm, we divided the insole area into six areas and created six virtual forces and the corresponding coordinates. We used the ANN algorithm with the input of magnitudes of FSR sensors, 1st and 2nd derivatives of them to estimate the virtual forces and coordinates. Eight healthy males were selected for data acquisition. They performed an experiment composed of the following motions: standing with weight shifting, walking with 1 km/h and 2 km/h, squatting and getting up from a sitting position to a standing position. The ANN for estimating virtual forces and corresponding coordinates was fitted according to those data, converted to c script, and downloaded to a microcontroller for validation experiments in real time. The results showed an average RMSE the whole experiment of 31.154 N for GRF estimation and 8.07 mm for CoP calibration. The correlation coefficients of the algorithm were 0.94 for GRF, 0.92 and 0.76 for the X and Y coordinate respectively. MDPI 2018-12-10 /pmc/articles/PMC6308711/ /pubmed/30544652 http://dx.doi.org/10.3390/s18124349 Text en © 2018 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
Choi, Ho Seon
Lee, Chang Hee
Shim, Myounghoon
Han, Jong In
Baek, Yoon Su
Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP)
title Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP)
title_full Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP)
title_fullStr Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP)
title_full_unstemmed Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP)
title_short Design of an Artificial Neural Network Algorithm for a Low-Cost Insole Sensor to Estimate the Ground Reaction Force (GRF) and Calibrate the Center of Pressure (CoP)
title_sort design of an artificial neural network algorithm for a low-cost insole sensor to estimate the ground reaction force (grf) and calibrate the center of pressure (cop)
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6308711/
https://www.ncbi.nlm.nih.gov/pubmed/30544652
http://dx.doi.org/10.3390/s18124349
work_keys_str_mv AT choihoseon designofanartificialneuralnetworkalgorithmforalowcostinsolesensortoestimatethegroundreactionforcegrfandcalibratethecenterofpressurecop
AT leechanghee designofanartificialneuralnetworkalgorithmforalowcostinsolesensortoestimatethegroundreactionforcegrfandcalibratethecenterofpressurecop
AT shimmyounghoon designofanartificialneuralnetworkalgorithmforalowcostinsolesensortoestimatethegroundreactionforcegrfandcalibratethecenterofpressurecop
AT hanjongin designofanartificialneuralnetworkalgorithmforalowcostinsolesensortoestimatethegroundreactionforcegrfandcalibratethecenterofpressurecop
AT baekyoonsu designofanartificialneuralnetworkalgorithmforalowcostinsolesensortoestimatethegroundreactionforcegrfandcalibratethecenterofpressurecop