Personalized Fitness Recommendation and Tracking System

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Nambiaaruran S; Dr. J. Angel Ida Chellam; Raghunath C G; Sriraam K N

Personalized Fitness Recommendation and Tracking System

Authors: Nambiaaruran S, Dr. J. Angel Ida Chellam, Raghunath C G, Sriraam K N

Unique Paper ID: 177547
Volume: 11
Issue: 12
PageNo: 1652-1655

Keywords: Fitness Tracking Personalized Recommendation NodeMCU MPU6050 Machine Learning Flutter Barbell Exercises

Abstract:
This paper presents a comprehensive smart fitness system that merges embedded hardware and mobile software to deliver personalized workout planning and real-time strength training tracking. Utilizing the NodeMCU ESP8266 microcontroller and MPU6050 inertial measurement unit (IMU), the system captures and transmits motion data during barbell exercises. The Flutter-based mobile application employs machine learning models to classify exercises and accurately count repetitions and sets. Personalized workout and diet recommendations are generated based on user profiles including age, gender, medical conditions, and fitness goals. The system eliminates reliance on external servers by embedding models directly in the app, ensuring efficient and low-latency performance. Experimental evaluation indicates high accuracy in exercise classification and user satisfaction in recommendation quality.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 1652-1655

Personalized Fitness Recommendation and Tracking System

Available:https://ijirt.org/article?manuscript=177547

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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