CaloriNet: From silhouettes to calorie estimation in private environments
This work addresses privacy-preserving health monitoring for individuals in home settings, but it is incremental as it builds on existing datasets and methods.
The authors tackled the problem of estimating calorie expenditure in private environments by using silhouettes instead of RGB data, achieving state-of-the-art minimum error in calories per minute.
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.