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We present a new framework for vision-based estimation of calorific expenditure from RGB-D data - the first that is validated on physical gas exchange measurements and applied to daily living scenarios. Deriving a person’s energy expenditure from sensors is an important tool in tracking physical activity levels for health and lifestyle monitoring. Most existing methods use metabolic lookup tables (METs) for a manual estimate or systems with inertial sensors which ultimately require users to wear devices. In contrast, the proposed pose-invariant and individual-independent vision framework allows for a remote estimation of calorific expenditure. We introduce, and evaluate our approach on, a new dataset called SPHERE-calorie, for which visual estimates can be compared against simultaneously obtained, indirect calorimetry measures based on gas exchange. We conclude from our experiments that the proposed vision pipeline is suitable for home monitoring in a controlled environment, with calorific expenditure estimates above accuracy levels of commonly used manual estimations via METs. With the dataset released, our work establishes a baseline for future research for this little-explored area of computer vision.

Original languageEnglish
Title of host publicationComputer Vision - ACCV 2016 Workshops
Subtitle of host publicationACCV 2016 International Workshops, Revised Selected Papers
PublisherSpringer-Verlag Berlin
Number of pages13
ISBN (Print)9783319544069
Publication statusPublished - 15 Mar 2017
Event13th Asian Conference on Computer Vision 2016: Workshop on Assistive Vision - Taipei International Convention Center, Taipei, Taiwan
Duration: 20 Nov 201624 Nov 2016
Conference number: 13

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10116 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


Conference13th Asian Conference on Computer Vision 2016
Abbreviated titleACCV 16
Internet address

Structured keywords

  • Digital Health


  • Digital Health


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