CaloriNet: From silhouettes to calorie estimation in private environments

Alessandro Masullo; Tilo Burghardt; Dima Damen; Sion Hannuna; Victor Ponce Lopez; Majid Mirmehdi

CaloriNet: From silhouettes to calorie estimation in private environments

Alessandro Masullo, Tilo Burghardt, Dima Damen, Sion Hannuna, Victor Ponce Lopez, Majid Mirmehdi

Research output: Contribution to conference › Conference Paper › peer-review

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Abstract

We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes. Our fused convolutional neural network architecture is trainable end-to-end, to estimate calorie expenditure, using temporal foreground silhouettes alongside accelerometer data. The network is trained and cross-validated on a publicly available dataset, SPHERE_RGBD + Inertial_calorie. Results show state-of-the-art minimum error on the estimation of energy expenditure (calories per minute), outperforming alternative, standard and single-modal techniques.

Original language	English
Number of pages	14
Publication status	Published - 20 Jul 2018
Event	29th British Machine Vision Conference - Northumbria University, Newcastle upon Tyne, United Kingdom Duration: 3 Sept 2018 → 6 Sept 2018 http://bmvc2018.org/index.html

Conference

Conference	29th British Machine Vision Conference
Country/Territory	United Kingdom
City	Newcastle upon Tyne
Period	3/09/18 → 6/09/18
Internet address	http://bmvc2018.org/index.html

Research Groups and Themes

Digital Health
SPHERE

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Accepted author manuscript, 2.34 MB

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@conference{95c71c4e289942e18fc2c275baaa209b,

title = "CaloriNet: From silhouettes to calorie estimation in private environments",

abstract = "We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes. Our fused convolutional neural network architecture is trainable end-to-end, to estimate calorie expenditure, using temporal foreground silhouettes alongside accelerometer data. The network is trained and cross-validated on a publicly available dataset, SPHERE_RGBD + Inertial_calorie. Results show state-of-the-art minimum error on the estimation of energy expenditure (calories per minute), outperforming alternative, standard and single-modal techniques.",

author = "Alessandro Masullo and Tilo Burghardt and Dima Damen and Sion Hannuna and {Ponce Lopez}, Victor and Majid Mirmehdi",

year = "2018",

month = jul,

day = "20",

language = "English",

note = "29th British Machine Vision Conference ; Conference date: 03-09-2018 Through 06-09-2018",

url = "http://bmvc2018.org/index.html",

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TY - CONF

T1 - CaloriNet

T2 - 29th British Machine Vision Conference

AU - Masullo, Alessandro

AU - Burghardt, Tilo

AU - Damen, Dima

AU - Hannuna, Sion

AU - Ponce Lopez, Victor

AU - Mirmehdi, Majid

PY - 2018/7/20

Y1 - 2018/7/20

N2 - We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes. Our fused convolutional neural network architecture is trainable end-to-end, to estimate calorie expenditure, using temporal foreground silhouettes alongside accelerometer data. The network is trained and cross-validated on a publicly available dataset, SPHERE_RGBD + Inertial_calorie. Results show state-of-the-art minimum error on the estimation of energy expenditure (calories per minute), outperforming alternative, standard and single-modal techniques.

AB - We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes. Our fused convolutional neural network architecture is trainable end-to-end, to estimate calorie expenditure, using temporal foreground silhouettes alongside accelerometer data. The network is trained and cross-validated on a publicly available dataset, SPHERE_RGBD + Inertial_calorie. Results show state-of-the-art minimum error on the estimation of energy expenditure (calories per minute), outperforming alternative, standard and single-modal techniques.

UR - http://bmvc2018.org/contents/papers/0383.pdf

M3 - Conference Paper

Y2 - 3 September 2018 through 6 September 2018

ER -

CaloriNet: From silhouettes to calorie estimation in private environments

Abstract

Conference

Research Groups and Themes

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SPHERE (EPSRC IRC)

Cite this

CaloriNet: From silhouettes to calorie estimation in private environments

Abstract

Conference

Research Groups and Themes

Access to Document

Handle.net

Other files and links

Fingerprint

Projects

SPHERE (EPSRC IRC)

Cite this