Calorie Burn Estimation in Community Parks Through DLICP: A Mathematical Modelling Approach
2024 · Springer · Abhishek Sebastian, Annis Fathima A, Pragna R, Madhan Kumar S, Jesher Joshua M
Deep-learning park fitness tracking that estimates calorie burn without expensive wearables.
DLICP (Deep Learning Integrated Community Parks) combines face recognition with a walking-activity model. It explores whether shared park infrastructure can provide fitness feedback without requiring each visitor to own a wearable.
A camera tracks users; the algorithm estimates pace and calories tailored to individual attributes. Against Apple Watch Series 6, the system landed a Mean Absolute Error of 5.64 cal and a Mean Percentage Error of 1.96%.
These results measure agreement with a consumer wearable, rather than direct measurement of metabolism. The engineering problem joins identity association, walking measurements, and a personalised estimate. The manuscript also identifies its connection to Indian application 202441050325.
A walk becomes a fitness estimate.
DLICP uses face recognition to associate observations with a park user.
Conceptual workflow based on the DLICP study. No identity recognition or health measurement runs on this page.
- Computer vision
- Public health
- Smart cities