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The nutrition app market is expected to reach a revenue of 5.4 billion dollars in 2024 all over the world with India hosting over 670 health apps, a few notable ones being HealthifyMe, cult.fit and 1 mg. Despite this, many individuals struggle to accurately document their food intake and monitor their fitness due to accurate manual tracking and insufficient integration of dietary and exercise data. Therefore, the research work introduces a utilized system which uses object detection to detect the food item which allows auto-calorie estimation, Web technologies to provide a user interface for the user to interact with. The system further provides a workout plan and ultimately, inculcates computer vision to track live feed and estimate the number of calories burnt. Putting all this together, the research aims at simplifying the process by using the live video analysis to recognize the food and pose estimation for workout detection. In turn, this empowers the user to reach their health and fitness goals more efficiently.

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AI Driven Food Recognition and Real-Time Pose Estimation for Health Monitoring

  • 会议论文

  • 发表日期：

    2025年

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  • Siddhanth Nilesh Jagtap,
  • Patil Krishna Reddy,
  • Moghal Hazash Baig,
  • B. V. Gokulnath,
  • Divya Meena Sundaram

摘要

The nutrition app market is expected to reach a revenue of 5.4 billion dollars in 2024 all over the world with India hosting over 670 health apps, a few notable ones being HealthifyMe, cult.fit and 1 mg. Despite this, many individuals struggle to accurately document their food intake and monitor their fitness due to accurate manual tracking and insufficient integration of dietary and exercise data. Therefore, the research work introduces a utilized system which uses object detection to detect the food item which allows auto-calorie estimation, Web technologies to provide a user interface for the user to interact with. The system further provides a workout plan and ultimately, inculcates computer vision to track live feed and estimate the number of calories burnt. Putting all this together, the research aims at simplifying the process by using the live video analysis to recognize the food and pose estimation for workout detection. In turn, this empowers the user to reach their health and fitness goals more efficiently.