International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 11 | Nov 2024
p-ISSN: 2395-0072
www.irjet.net
IOT Enhanced Fitness Tracker with ML based
Recommendation
1Madhura Kulkarni, 2Sanjeevani Anuse, 3Pooja Vasekar, 4Prajkta Gaikwad, 5Kalyani Kumbhar, 6S.A. Shegdar 1 ,2,3,4,5 UG Students, Department of Computer Science and Engineering, SVERI’s College of Engineering,
Pandharpur, Maharashtra, India
1madhurarkulkarni@coep.sveri.ac.in
6 Assistant Professor, Department of Computer Science and Engineering, SVERI’s College of Engineering,
Pandharpur, Maharashtra, India --------------------------------------------------------------------------***----------------------------------------------------------------------IoT sensors to continuously monitor various health ABSTRACT: The IoT-enhanced fitness tracker with
metrics, such as heart rate, step count, and activity levels. The data collected is processed through machine learning algorithms that analyze user patterns and generate tailored fitness advice to enhance user engagement and outcomes [3]. These devices utilize various sensors to track metrics such as heart rate, steps, sleep patterns, and calories burned, offering a comprehensive view of a user’s fitness journey.
machine learning (ML) recommendations is an innovative solution designed to optimize fitness and health management. By integrating advanced IoT sensors, such as heart rate monitors (HW827) and accelerometers (MPU6050), with sophisticated ML algorithms, the system provides personalized fitness recommendations based on real-time data. The tracker continuously monitors key health metrics, including heart rate, step count, and activity levels, to deliver tailored workout plans and detailed progress tracking. The ML component analyzes historical and current user data to refine recommendations, ensuring fitness plans are customized, adaptive, and effective.
In this project, we present an IoT-enhanced fitness tracker website integrated with machine learning (ML)based recommendation systems. The system employs the ESP32 microcontroller paired with sensors like the MPU6050 accelerometer and the hw827 heart rate sensor to capture real-time data on user movements and vital signs. This data is transmitted to the web platform via the ESP32 module, allowing users to access their fitness metrics remotely through a user-friendly interface. What sets this project apart is the integration of machine learning algorithms to deliver personalized fitness recommendations. By analyzing the collected data, the ML component identifies patterns and trends in user behavior, enabling the system to provide tailored suggestions on exercise routines, activity adjustments, and health tips. The goal is to provide personalized fitness recommendations by processing and interpreting sensor data such as heart rate and step count in real time [8].
This approach enhances user engagement through interactive features, empowers users to make datadriven health decisions, and supports habit formation. The system's scalability and remote access capabilities further extend its utility, allowing for seamless integration with web-based dashboards and future expansions. Overall, this project represents a significant advancement in personalized fitness technology, combining IoT and ML to promote healthier, more with sensors like the MPU6050 accelerometer and the hw827 heart rate sensor to capture real-time data on user movements and vital signs. This data is transmitted to the web platform via the ESP32 module, allowing users to access their fitness metrics remotely through a userfriendly interface. What sets this project apart is the integration of machine learning algorithms recommendations. By analyzing the collected data, the ML component.
Machine learning techniques to develop an IoT-enhanced fitness tracker capable of recognizing and analyzing user activity patterns from sensor data. By implementing robust pattern recognition algorithms, the tracker can accurately interpret heart rate, step count, and other fitness metrics, enabling it to provide personalized and data-driven fitness recommendations [11].
Keywords: Data Analytics., Health Monitoring, IoT, Fitness Tracker, Machine Learning, Wearable Technology.
II. LITERATURE SURVEY
I. INTRODUCTION
2.1 Existing model
The rapid advancement of Internet of Things (IoT) technologies has revolutionized the way we monitor and manage personal health and fitness. The integration of
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