CaloriNet: From silhouettes to calorie estimation in private environments

Alessandro Masullo, Tilo Burghardt, Dima Damen, Sion Hannuna, Victor Ponce Lopez, Majid Mirmehdi

Research output: Contribution to conferenceConference Paperpeer-review

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We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes. Our fused convolutional neural network architecture is trainable end-to-end, to estimate calorie expenditure, using temporal foreground silhouettes alongside accelerometer data. The network is trained and cross-validated on a publicly available dataset, SPHERE_RGBD + Inertial_calorie. Results show state-of-the-art minimum error on the estimation of energy expenditure (calories per minute), outperforming alternative, standard and single-modal techniques.
Original languageEnglish
Number of pages14
Publication statusPublished - 20 Jul 2018
Event29th British Machine Vision Conference - Northumbria University, Newcastle upon Tyne, United Kingdom
Duration: 3 Sept 20186 Sept 2018


Conference29th British Machine Vision Conference
Country/TerritoryUnited Kingdom
CityNewcastle upon Tyne
Internet address

Structured keywords

  • Digital Health


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