FitSplit is a smart gym attendance and personalized workout recommendation system that combines RFID/NFC technology and Machine Learning to streamline user entry logging and deliver data-driven workout planning.
Manual gym check-ins are inefficient and don't leverage data for user insights. FitSplit automates entry logging using RFID/NFC and clusters gym-goers based on attendance behavior to recommend optimal workout plans.
- ✅ Automatically log user entries via RFID/NFC
- ✅ Track attendance patterns
- ✅ Recommend personalized workout splits using ML (K-Means clustering)
- ✅ Identify user types: Beginner, Intermediate, Advanced
- ✅ Encourage consistency through gamified features
- RC522 RFID Module
- Arduino UNO
- RFID/NFC Tags
Each gym-goer taps their RFID card to log entry. The UID is sent to the PC via Serial.
The Python script listens to the serial port and logs:
- UID
- Timestamp
- Day, Week, Month
Stored in RFID_logs.csv.
- Parses attendance patterns from CSV
- Extracts features like:
- Visit frequency
- Consistency (gap std deviation)
- Time-of-day preference
- Day-of-week behavior
- Uses K-Means Clustering to categorize users
- Stores trained model for future predictions
| Domain | Tools & Libraries |
|---|---|
| Hardware | Arduino UNO, RC522 RFID |
| Programming | C++, Python |
| ML & Data Processing | Pandas, Scikit-learn, KMeans, Joblib |
| Visualization & Logging | CSV, Serial Monitor |
| RC522 Pin | Arduino Pin |
|---|---|
| VCC | 3.3V |
| GND | GND |
| RST | 9 |
| MISO | 12 |
| MOSI | 11 |
| SCK | 13 |
| SDA (SS) | 10 |
- 🛂 Contactless Check-In: No manual logging
- 🧠 Smart Recommendations: Suggest best time/day to work out
- 🧩 User Classification: Tailored workout plans
- 🕹️ Gamification: Badges, streaks, milestones
- 📊 Manager Insights: Detect churn, plan staffing
- 🤖 Deep Learning for user goals (fat loss, hypertrophy)
- 📱 Mobile App with real-time analytics
- 🏆 Social Features: Leaderboards, group workouts
- 🏋️ Integration with smart equipment
- 🌐 Multi-branch gym support
The system successfully automates gym attendance and delivers meaningful workout insights based on real usage patterns. It is scalable, cost-effective, and a stepping stone towards AI-powered fitness personalization.