| _version_ | 1866901957861441536 |
|---|---|
| author | Mr. Bitra Ram Prasad,Mohd Yahiya, Kodipad Saifur Rahaman, Adla Bhavani |
| author_facet | Mr. Bitra Ram Prasad,Mohd Yahiya, Kodipad Saifur Rahaman, Adla Bhavani |
| contents | <p>Cal Police is a full-stack health informatics application designed to combat sedentary lifestyle risks and<br>nutritional unawareness through integrated, AI-enhanced wellness tracking. The system combines a responsive<br>React frontend with a Node.js backend and a dedicated Python AI microservice to provide real-time calorie<br>monitoring, meal logging, and hydration tracking. A proprietary machine learning integration utilizing the Groq<br>API processes user biometric data and dietary logs to generate personalized food and exercise recommendations.<br>In response to user input or daily goals, the system instantly calculates macronutrient breakdowns and visualizes<br>progress through dynamic Recharts dashboards. Additional features such as JWT-secured authentication and a<br>social achievement feed foster accountability and long-term user engagement. Deployed across scalable<br>microservices, the application ensures continuous, cross-platform access with high data fidelity. Compact in its<br>modular architecture yet robust in analytical capability, this project is suitable for individual fitness enthusiasts<br>and clinical wellness programs, enhancing personal health management and supporting data-driven nutritional<br>science.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19510233 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | CALORIE INTAKE TRACKER Mr. Bitra Ram Prasad,Mohd Yahiya, Kodipad Saifur Rahaman, Adla Bhavani <p>Cal Police is a full-stack health informatics application designed to combat sedentary lifestyle risks and<br>nutritional unawareness through integrated, AI-enhanced wellness tracking. The system combines a responsive<br>React frontend with a Node.js backend and a dedicated Python AI microservice to provide real-time calorie<br>monitoring, meal logging, and hydration tracking. A proprietary machine learning integration utilizing the Groq<br>API processes user biometric data and dietary logs to generate personalized food and exercise recommendations.<br>In response to user input or daily goals, the system instantly calculates macronutrient breakdowns and visualizes<br>progress through dynamic Recharts dashboards. Additional features such as JWT-secured authentication and a<br>social achievement feed foster accountability and long-term user engagement. Deployed across scalable<br>microservices, the application ensures continuous, cross-platform access with high data fidelity. Compact in its<br>modular architecture yet robust in analytical capability, this project is suitable for individual fitness enthusiasts<br>and clinical wellness programs, enhancing personal health management and supporting data-driven nutritional<br>science.</p> |
| title | CALORIE INTAKE TRACKER |
| url | https://doi.org/10.5281/zenodo.19510233 |