Calorie Burn Estimation in Community Parks Through DLICP: A Mathematical Modelling Approach
This provides real-time, personalized fitness tracking for park users, but it is incremental as it applies existing deep learning and measurement techniques to a specific domain.
The study tackled calorie burn estimation in community parks by introducing DLICP, which combines face recognition and a walking activity algorithm, achieving a Mean Absolute Error of 5.64 calories and a Mean Percentage Error of 1.96% compared to fitness devices.
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.