Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation

Wearable technologies are known to improve our quality of life. Among the various wearable devices, shoes are non-intrusive, lightweight, and can be used for outdoor activities. In this study, we estimated the energy consumption and heart rate in an environment (i.e., running on a treadmill) using s...

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Autores principales:	Heesang Eom, Jongryun Roh, Yuli Sun Hariyani, Suwhan Baek, Sukho Lee, Sayup Kim, Cheolsoo Park
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	smart shoe energy expenditure heart rate channel wise attention DenseNet accelerometer Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/db6f0190421f436791a3469943a3776d
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spelling	oai:doaj.org-article:db6f0190421f436791a3469943a3776d2021-11-11T19:04:55ZDeep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation10.3390/s212170581424-8220https://doaj.org/article/db6f0190421f436791a3469943a3776d2021-10-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/21/7058https://doaj.org/toc/1424-8220Wearable technologies are known to improve our quality of life. Among the various wearable devices, shoes are non-intrusive, lightweight, and can be used for outdoor activities. In this study, we estimated the energy consumption and heart rate in an environment (i.e., running on a treadmill) using smart shoes equipped with triaxial acceleration, triaxial gyroscope, and four-point pressure sensors. The proposed model uses the latest deep learning architecture which does not require any separate preprocessing. Moreover, it is possible to select the optimal sensor using a channel-wise attention mechanism to weigh the sensors depending on their contributions to the estimation of energy expenditure (EE) and heart rate (HR). The performance of the proposed model was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>). Moreover, the RMSE was 1.05 ± 0.15, MAE 0.83 ± 0.12 and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> 0.922 ± 0.005 in EE estimation. On the other hand, and RMSE was 7.87 ± 1.12, MAE 6.21 ± 0.86, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> 0.897 ± 0.017 in HR estimation. In both estimations, the most effective sensor was the z axis of the accelerometer and gyroscope sensors. Through these results, it is demonstrated that the proposed model could contribute to the improvement of the performance of both EE and HR estimations by effectively selecting the optimal sensors during the active movements of participants.Heesang EomJongryun RohYuli Sun HariyaniSuwhan BaekSukho LeeSayup KimCheolsoo ParkMDPI AGarticlesmart shoeenergy expenditureheart ratechannel wise attentionDenseNetaccelerometerChemical technologyTP1-1185ENSensors, Vol 21, Iss 7058, p 7058 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	smart shoe energy expenditure heart rate channel wise attention DenseNet accelerometer Chemical technology TP1-1185
spellingShingle	smart shoe energy expenditure heart rate channel wise attention DenseNet accelerometer Chemical technology TP1-1185 Heesang Eom Jongryun Roh Yuli Sun Hariyani Suwhan Baek Sukho Lee Sayup Kim Cheolsoo Park Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation
description	Wearable technologies are known to improve our quality of life. Among the various wearable devices, shoes are non-intrusive, lightweight, and can be used for outdoor activities. In this study, we estimated the energy consumption and heart rate in an environment (i.e., running on a treadmill) using smart shoes equipped with triaxial acceleration, triaxial gyroscope, and four-point pressure sensors. The proposed model uses the latest deep learning architecture which does not require any separate preprocessing. Moreover, it is possible to select the optimal sensor using a channel-wise attention mechanism to weigh the sensors depending on their contributions to the estimation of energy expenditure (EE) and heart rate (HR). The performance of the proposed model was evaluated using the root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>). Moreover, the RMSE was 1.05 ± 0.15, MAE 0.83 ± 0.12 and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> 0.922 ± 0.005 in EE estimation. On the other hand, and RMSE was 7.87 ± 1.12, MAE 6.21 ± 0.86, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> 0.897 ± 0.017 in HR estimation. In both estimations, the most effective sensor was the z axis of the accelerometer and gyroscope sensors. Through these results, it is demonstrated that the proposed model could contribute to the improvement of the performance of both EE and HR estimations by effectively selecting the optimal sensors during the active movements of participants.
format	article
author	Heesang Eom Jongryun Roh Yuli Sun Hariyani Suwhan Baek Sukho Lee Sayup Kim Cheolsoo Park
author_facet	Heesang Eom Jongryun Roh Yuli Sun Hariyani Suwhan Baek Sukho Lee Sayup Kim Cheolsoo Park
author_sort	Heesang Eom
title	Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation
title_short	Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation
title_full	Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation
title_fullStr	Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation
title_full_unstemmed	Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation
title_sort	deep learning-based optimal smart shoes sensor selection for energy expenditure and heart rate estimation
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/db6f0190421f436791a3469943a3776d
work_keys_str_mv	AT heesangeom deeplearningbasedoptimalsmartshoessensorselectionforenergyexpenditureandheartrateestimation AT jongryunroh deeplearningbasedoptimalsmartshoessensorselectionforenergyexpenditureandheartrateestimation AT yulisunhariyani deeplearningbasedoptimalsmartshoessensorselectionforenergyexpenditureandheartrateestimation AT suwhanbaek deeplearningbasedoptimalsmartshoessensorselectionforenergyexpenditureandheartrateestimation AT sukholee deeplearningbasedoptimalsmartshoessensorselectionforenergyexpenditureandheartrateestimation AT sayupkim deeplearningbasedoptimalsmartshoessensorselectionforenergyexpenditureandheartrateestimation AT cheolsoopark deeplearningbasedoptimalsmartshoessensorselectionforenergyexpenditureandheartrateestimation
_version_	1718431675048787968

Deep Learning-Based Optimal Smart Shoes Sensor Selection for Energy Expenditure and Heart Rate Estimation

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