Deep Learning-enabled Personal Fitness Coaching With Motion Feedback And Goal Optimization

• Author(s):

  V.Tejaswi | K.Akshitha | M.Phani Akshaya | P.Raghu Vamshi | V. Veeresh

• Keywords:

  Artificial Intelligence, Deep Learning, Personal Fitness Coach, YOLOv11, Exercise Recognition, CNN, Posture Correction.

• Abstract:

  The Increasing Adoption Of Artificial Intelligence In Healthcare And Fitness Has Opened New Opportunities For Intelligent Personal Coaching Systems. This Paper Presents A Deep Learning-enabled Personal Fitness Coaching System That Recognizes Exercise Activities And Provides Personalized Workout Recommendations. The Proposed System Uses A Custom-trained YOLOv11 Convolutional Neural Network (CNN) Model To Detect And Classify 36 Different Exercise Postures From User-uploaded Images And Videos. Based On The Detected Activity And User-provided Age, The System Dynamically Generates Personalized Workout Plans And Posture Correction Guidance. The Application Includes Modules Such As User Login, Deep Learning Model Training, Training Graph Visualization, Activity Recognition, And Plan Recommendation. Experimental Results Show That The Proposed System Provides Accurate Activity Recognition And Helps Users Improve Exercise Performance, Reduce Injury Risk, And Achieve Better Fitness Outcome.

---

Other Details

• Paper id:

  IJSARTV12I4104970

• Published in:

  Volume: 12 Issue: 4 April 2026

• Publication Date:

  2026-04-11

Download Article