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Energy expenditure estimation using visual and inertial sensors

Lili Tao, Tilo Burghardt, Majid Mirmehdi, Dima Damen, Ashley Cooper, Massimo Camplani, Sion Hannuna, Adeline Paiement, Ian Craddock

IET Computer Vision

Swansea University Author: Adeline Paiement

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DOI (Published version): 10.1049/iet-cvi.2017.0112

Abstract

Deriving a person’s energy expenditure accurately forms the foundation for tracking physical activity levels across many health and lifestyle monitoring tasks. In this work, we present a method for estimating calorific expenditure from combined visual and accelerometer sensors by way of an RGB-Depth...

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Published in: IET Computer Vision
ISSN: 1751-9632 1751-9640
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa36266
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first_indexed 2017-10-25T19:11:58Z
last_indexed 2018-02-09T05:28:20Z
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spelling 2017-12-11T14:22:12.1122263 v2 36266 2017-10-25 Energy expenditure estimation using visual and inertial sensors f50adf4186d930e3a2a0f9a6d643cf53 Adeline Paiement Adeline Paiement true false 2017-10-25 FGHSS Deriving a person’s energy expenditure accurately forms the foundation for tracking physical activity levels across many health and lifestyle monitoring tasks. In this work, we present a method for estimating calorific expenditure from combined visual and accelerometer sensors by way of an RGB-Depth camera and a wearable inertial sensor. The proposed individual independent framework fuses information from both modalities which leads to improved estimates beyond the accuracy of single modality and manual metabolic lookup table (MET) based methods. For evaluation, we introduce a new dataset called SPHERE_RGBD+Inertial_calorie, for which visual and inertial data is simultaneously obtained with indirect calorimetry ground truth measurements based on gas exchange. Experiments show that the fusion of visual and inertial data reduces the estimation error by 8% and 18% compared to the use of visual only and inertial sensor only, respectively, and by 33% compared to a MET-based approach. We conclude from our results that the proposed approach is suitable for home monitoring in a controlled environment. Journal Article IET Computer Vision 1751-9632 1751-9640 28 9 2017 2017-09-28 10.1049/iet-cvi.2017.0112 http://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0112 COLLEGE NANME Humanities and Social Sciences - Faculty COLLEGE CODE FGHSS Swansea University 2017-12-11T14:22:12.1122263 2017-10-25T16:00:28.8717691 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Lili Tao 1 Tilo Burghardt 2 Majid Mirmehdi 3 Dima Damen 4 Ashley Cooper 5 Massimo Camplani 6 Sion Hannuna 7 Adeline Paiement 8 Ian Craddock 9 0036266-06122017174611.pdf IET-CVIv3.pdf 2017-12-06T17:46:11.1930000 Output 7045360 application/pdf Version of Record true 2017-12-06T00:00:00.0000000 CC-BY true eng
title Energy expenditure estimation using visual and inertial sensors
spellingShingle Energy expenditure estimation using visual and inertial sensors
Adeline Paiement
title_short Energy expenditure estimation using visual and inertial sensors
title_full Energy expenditure estimation using visual and inertial sensors
title_fullStr Energy expenditure estimation using visual and inertial sensors
title_full_unstemmed Energy expenditure estimation using visual and inertial sensors
title_sort Energy expenditure estimation using visual and inertial sensors
author_id_str_mv f50adf4186d930e3a2a0f9a6d643cf53
author_id_fullname_str_mv f50adf4186d930e3a2a0f9a6d643cf53_***_Adeline Paiement
author Adeline Paiement
author2 Lili Tao
Tilo Burghardt
Majid Mirmehdi
Dima Damen
Ashley Cooper
Massimo Camplani
Sion Hannuna
Adeline Paiement
Ian Craddock
format Journal article
container_title IET Computer Vision
publishDate 2017
institution Swansea University
issn 1751-9632
1751-9640
doi_str_mv 10.1049/iet-cvi.2017.0112
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0112
document_store_str 1
active_str 0
description Deriving a person’s energy expenditure accurately forms the foundation for tracking physical activity levels across many health and lifestyle monitoring tasks. In this work, we present a method for estimating calorific expenditure from combined visual and accelerometer sensors by way of an RGB-Depth camera and a wearable inertial sensor. The proposed individual independent framework fuses information from both modalities which leads to improved estimates beyond the accuracy of single modality and manual metabolic lookup table (MET) based methods. For evaluation, we introduce a new dataset called SPHERE_RGBD+Inertial_calorie, for which visual and inertial data is simultaneously obtained with indirect calorimetry ground truth measurements based on gas exchange. Experiments show that the fusion of visual and inertial data reduces the estimation error by 8% and 18% compared to the use of visual only and inertial sensor only, respectively, and by 33% compared to a MET-based approach. We conclude from our results that the proposed approach is suitable for home monitoring in a controlled environment.
published_date 2017-09-28T03:45:17Z
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score 11.036334

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