Summary of Calorie Burn Estimation in Community Parks Through Dlicp: a Mathematical Modelling Approach, by Abhishek Sebastian et al.
Calorie Burn Estimation in Community Parks Through DLICP: A Mathematical Modelling Approach
by Abhishek Sebastian, Annis Fathima A, Pragna R, Madhan Kumar S, Jesher Joshua M
First submitted to arxiv on: 6 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces DLICP (Deep Learning Integrated Community Parks), a novel approach that combines deep learning techniques with face recognition technology and walking activity measurement algorithms to enhance user experience in community parks. The system utilizes a camera with face recognition software to accurately identify and track park users, while also calculating parameters such as average pace and calories burned based on individual attributes. Evaluations show 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 like the Apple Watch Series 6. This study contributes to the development of intelligent smart park systems, enabling real-time updates on burned calories and personalized fitness tracking. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new system called DLICP that helps people track their physical activity in community parks. It uses special cameras with face recognition technology to identify and follow people as they walk or exercise. The system also calculates how many calories each person burns based on their individual attributes, like pace and speed. Tests show that this system is very accurate, almost as good as popular fitness trackers like the Apple Watch. This research can help create smarter parks that give people real-time feedback on their physical activity and encourage them to be more active. |
Keywords
» Artificial intelligence » Deep learning » Face recognition » Mae » Precision » Tracking
