Energy expenditure estimation using visual and inertial sensors

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

Abstract

© The Institution of Engineering and Technology 2017. 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.

• Journal Article Type: Article
• Acceptance Date: Sep 23, 2017
• Online Publication Date: Sep 29, 2017
• Publication Date: Feb 1, 2018
• Deposit Date: Nov 7, 2017
• Publicly Available Date: Nov 7, 2017
• Journal: IET Computer Vision
• Print ISSN: 1751-9632
• Electronic ISSN: 1751-9640
• Publisher: Institution of Engineering and Technology (IET)
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Issue: 1
• Pages: 36-47
• DOI: https://doi.org/10.1049/iet-cvi.2017.0112
• Keywords: computer vision, table lookup, calorimetry, medical image processing, accelerometers, image sensors, sensor fusion, biomedical engineering
• Public URL: https://uwe-repository.worktribe.com/output/860132
• Publisher URL: http://dx.doi.org/10.1049/iet-cvi.2017.0112
• Related Public URLs: http://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2017.0112
• Contract Date: Nov 7, 2017