Ai-powered Personalized Fitness Planner For Students

Abstract

Student Health And Physical Fitness Are Increasingly Neglected Due To Academic Workload, Irregular Schedules, And Lack Of Personalized Guidance. This Paper Presents An AI- Powered Personalized Fitness Planning System Tailored Specifically For Students, Integrating Machine Learning, Deep Learning, And Computer Vision To Generate Adaptive Workout Recommendations. The Proposed System Collects Student-specific Parameters Such As Body Mass Index (BMI), Fitness Goals, Academic Schedule, Activity History, And Dietary Preferences To Build A Comprehensive User Profile. A Hybrid Model Combining A 1D-Convolutional Neural Network (1D-CNN) With A Gradient-boosted Classifier Generates Individualized Exercise Prescriptions, While A Real-time Pose Estimation Module Using MediaPipe Provides Form Feedback During Workouts. Experimental Evaluation On A Dataset Of 500 Undergraduate Students Demonstrates That The Proposed System Achieves 93.6% Accuracy In Fitness Level Classification And Yields Measurable Improvements In Student Physical Activity Adherence Over An Eight-week Trial Period. The System Represents A Practical, Low-cost Solution Deployable On Standard Smartphones, Making Personalized Fitness Coaching Accessible To The Student Popula- Tion Without Requiring Expensive Gym Memberships Or Personal Trainers.

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