Calorie Burn Estimation in Community Parks Through DLICP: A Mathematical Modelling Approach

TL;DR

This paper presents DLICP, a novel system combining deep learning and activity measurement algorithms to accurately estimate calories burned in community parks, enhancing personalized fitness tracking and user experience.

Contribution

It introduces a new integrated approach using face recognition and activity measurement algorithms for precise calorie estimation in community parks.

Findings

Achieved a MAE of 5.64 calories in calorie estimation.

Attained an MPE of 1.96%, demonstrating high accuracy.

Validated against Apple Watch Series 6 measurements.

Abstract

Community parks play a crucial role in promoting physical activity and overall well-being. This study introduces DLICP (Deep Learning Integrated Community Parks), an innovative approach that combines deep learning techniques specifically, face recognition technology with a novel walking activity measurement algorithm to enhance user experience in community parks. The DLICP utilizes a camera with face recognition software to accurately identify and track park users. Simultaneously, a walking activity measurement algorithm calculates parameters such as the average pace and calories burned, tailored to individual attributes. Extensive evaluations confirm the precision of DLICP, with a Mean Absolute Error (MAE) of 5.64 calories and a Mean Percentage Error (MPE) of 1.96%, benchmarked against widely available fitness measurement devices, such as the Apple Watch Series 6. This study contributes significantly to the development of intelligent smart park systems, enabling real-time updates on burned calories and personalized fitness tracking.