Projects per year
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.
|Title of host publication||Computer Vision - ACCV 2016 Workshops|
|Subtitle of host publication||ACCV 2016 International Workshops, Revised Selected Papers|
|Number of pages||13|
|Publication status||Published - 15 Mar 2017|
|Event||13th Asian Conference on Computer Vision 2016: Workshop on Assistive Vision - Taipei International Convention Center, Taipei, Taiwan|
Duration: 20 Nov 2016 → 24 Nov 2016
Conference number: 13
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||13th Asian Conference on Computer Vision 2016|
|Abbreviated title||ACCV 16|
|Period||20/11/16 → 24/11/16|
- Digital Health
- Digital Health
FingerprintDive into the research topics of 'Calorie counter: RGB-depth visual estimation of energy expenditure at home'. Together they form a unique fingerprint.
- 1 Finished
Craddock, I. J., Coyle, D. T., Flach, P. A., Kaleshi, D., Mirmehdi, M., Piechocki, R. J., Stark, B. H., Ascione, R., Ashburn, A. M., Burnett, M. E., Damen, D., Gooberman-Hill, R. J. S., Harwin, W. S., Hilton, G., Holderbaum, W., Holley, A. P., Manchester, V. A., Meller, B. J., Stack, E. & Gilchrist, I. D.
1/10/13 → 30/09/18
Project: Research, Parent