{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/calorie-counter-rgb-depth-visual-estimation","title":"Calorie Counter: RGB-Depth Visual Estimation of Energy Expenditure at Home","arxiv_id":"1607.08196","date":"2016-07-27","proceeding":null,"authors":["Lili Tao","Tilo Burghardt","Majid Mirmehdi","Dima Damen","Ashley Cooper","Sion Hannuna","Massimo Camplani","Adeline Paiement","Ian Craddock"],"abstract":"We present a new framework for vision-based estimation of calorific\nexpenditure from RGB-D data - the first that is validated on physical gas\nexchange measurements and applied to daily living scenarios. Deriving a\nperson's energy expenditure from sensors is an important tool in tracking\nphysical activity levels for health and lifestyle monitoring. Most existing\nmethods use metabolic lookup tables (METs) for a manual estimate or systems\nwith inertial sensors which ultimately require users to wear devices. In\ncontrast, the proposed pose-invariant and individual-independent vision\nframework allows for a remote estimation of calorific expenditure. We\nintroduce, and evaluate our approach on, a new dataset called SPHERE-calorie,\nfor which visual estimates can be compared against simultaneously obtained,\nindirect calorimetry measures based on gas exchange. % based on per breath gas\nexchange. We conclude from our experiments that the proposed vision pipeline is\nsuitable for home monitoring in a controlled environment, with calorific\nexpenditure estimates above accuracy levels of commonly used manual estimations\nvia METs. With the dataset released, our work establishes a baseline for future\nresearch for this little-explored area of computer vision.","url_abs":"http://arxiv.org/abs/1607.08196v1","url_pdf":"http://arxiv.org/pdf/1607.08196v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"sphere-calorie","name":"SPHERE-calorie","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}