# CaloriNet: From silhouettes to calorie estimation in private environments

arXiv: https://arxiv.org/abs/1806.08152 · HTML: https://mlsanity.com/paper/1806.08152/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2018.

## Built on by (most cited first, 4 of 4)

- [Should I take a walk? Estimating Energy Expenditure from Video Data](https://mlsanity.com/paper/2202.00712/) (2022)
- [Inertial Hallucinations -- When Wearable Inertial Devices Start Seeing Things](https://mlsanity.com/paper/2207.06789/) (2022)
- [Multimodal Indoor Localisation for Measuring Mobility in Parkinson's Disease using Transformers](https://mlsanity.com/paper/2205.06142/) (2022)
- [Deep Learning for Metabolic Rate Estimation from Biosignals: A Comparative Study of Architectures and Signal Selection](https://mlsanity.com/paper/2511.09276/) (2025)

## Abstract (arXiv)

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.