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

• URL: http://arxiv.org/abs/2407.04986v1
• Date: Sat, 6 Jul 2024 07:45:05 GMT
• Title: Calorie Burn Estimation in Community Parks Through DLICP: A Mathematical Modelling Approach
• Authors: Abhishek Sebastian, Annis Fathima A, Pragna R, Madhan Kumar S, Jesher Joshua M,
• Abstract summary: This study introduces DLICP (Deep Learning Integrated Community Parks), an innovative approach that combines deep learning techniques specifically, face recognition technology. The DLICP utilizes a camera with face recognition software to accurately identify and track park users. This study contributes significantly to the development of intelligent smart park systems, enabling real-time updates on burned calories and personalized fitness tracking.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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