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Real-Time Calorie Burn Estimation Using IoT Sensors and RF ML Algorithm

  • Unique Paper ID: 178292
  • Volume: 11
  • Issue: 12
  • PageNo: 3966-3971
  • Abstract:
  • Accurately estimating calorie expenditure during physical activity is crucial for fitness tracking, weight management, and personalized health monitoring. This paper presents a real-time calorie burn prediction system leveraging the synergy of Internet of Things (IoT) sensors and machine learning techniques. The system employs non-invasive physiological sensors, specifically the MAX30102 pulse oximeter for heart rate monitoring and the MLX90614 infrared thermometer for body temperature measurement. These sensors are seamlessly integrated with ESP8266 microcontrollers to facilitate real-time data acquisition and transmission. In addition to the dynamic physiological inputs, the system incorporates static user-specific parameters, including age, gender, height, and weight, along with the duration of the activity. This comprehensive dataset forms the basis for training a robust machine learning model. We have implemented a Random Forest Regression algorithm, chosen for its inherent ability to handle non-linear relationships and provide high prediction accuracy in regression tasks. The trained model effectively learns the complex interplay between physiological responses, user characteristics, and activity duration to estimate calorie expenditure. To provide users with immediate feedback, a user-friendly Streamlit web application has been developed, featuring an aesthetically pleasing dark theme. This intuitive interface displays the real-time calorie burn predictions, enabling users to monitor their energy expenditure during workouts or daily activities. Furthermore, all collected sensor data, user details, and the corresponding calorie burn predictions are persistently stored in a MySQL database. This facilitates data analysis, historical tracking of calorie expenditure, and potential future enhancements to the prediction model. The developed system demonstrates an effective and practical application of IoT sensor technology, machine learning algorithms, and efficient data management for real-time calorie burn estimation, offering a valuable tool for health and fitness enthusiasts.
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Cite This Article

  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 3966-3971

Real-Time Calorie Burn Estimation Using IoT Sensors and RF ML Algorithm

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8.01 (Year 2024)
UGC Approved
Journal no 47859

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