Ai-powered Personalized Fitness Planner For Students

• Author(s):

  Dr.S.R.Patil | Yadav Karan Balasaheb | Sanket Bharat Gore | Junnare Prathamesh Suhas | Sapkal Vishwajeet Hanmant

• Keywords:

  Artificial Intelligence, Personalized Fitness, Deep Learning, Student Health, Exercise Recommendation, Pose Estimation, MediaPipe, BMI, Machine Learning

• 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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Other Details

• Paper id:

  IJSARTV12I5105486

• Published in:

  Volume: 12 Issue: 5 May 2026

• Publication Date:

  2026-05-24

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