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Energy expenditure estimation using visual and inertial sensors.

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Bibliographic Details
Title:	Energy expenditure estimation using visual and inertial sensors.
Authors:	Tao, Lili^1,2, Burghardt, Tilo¹, Mirmehdi, Majid¹ M.Mirmehdi@bristol.ac.uk, Damen, Dima¹, Cooper, Ashley¹, Camplani, Massimo¹, Hannuna, Sion¹, Paiement, Adeline¹, Craddock, Ian¹
Source:	IET Computer Vision (Wiley-Blackwell). 2018, Vol. 12 Issue 1, p36-47. 12p.
Subjects:	Energy consumption, Data fusion (Statistics), Regression analysis, Physical activity measurement, Accelerometry
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 study, the authors 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 equivalents of task (MET) lookup table based methods. For evaluation, the authors introduce a new dataset called SPHERE_RGBD + Inertial_calorie, for which visual and inertial data are 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 with the use of visual only and inertial sensor only, respectively, and by 33% compared with a MET-based approach. The authors conclude from their results that the proposed approach is suitable for home monitoring in a controlled environment. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
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 study, the authors 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 equivalents of task (MET) lookup table based methods. For evaluation, the authors introduce a new dataset called SPHERE_RGBD + Inertial_calorie, for which visual and inertial data are 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 with the use of visual only and inertial sensor only, respectively, and by 33% compared with a MET-based approach. The authors conclude from their results that the proposed approach is suitable for home monitoring in a controlled environment. [ABSTRACT FROM AUTHOR]
ISSN:	17519632
DOI:	10.1049/iet-cvi.2017.0112