Calorie Burn Estimation in Community Parks Through DLICP: A Mathematical Modelling Approach
Fuente:
arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909385183199232 |
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| author | Sebastian, Abhishek A, Annis Fathima R, Pragna S, Madhan Kumar M, Jesher Joshua |
| author_facet | Sebastian, Abhishek A, Annis Fathima R, Pragna S, Madhan Kumar M, Jesher Joshua |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04986 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Calorie Burn Estimation in Community Parks Through DLICP: A Mathematical Modelling Approach Sebastian, Abhishek A, Annis Fathima R, Pragna S, Madhan Kumar M, Jesher Joshua Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | Calorie Burn Estimation in Community Parks Through DLICP: A Mathematical Modelling Approach |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2407.04986 |