🎓 Final-year Electronics & Communication Engineering student (VTU) | 📍 Bangalore, India
🧠 Focused on Applied Machine Learning, Computer Vision & Intelligent Systems
⚙️ Building real-time systems that combine ML + sensors + hardware integration
🚀 I design systems that don’t just predict - they work in real-world conditions with partial hardware availability
- 🧠 Applied Machine Learning - Feature engineering, classification systems, real-time inference
- 👁️ Computer Vision - Pose estimation and visual validation pipelines
- 🔌 Embedded Systems & IoT - ESP32, IMU sensors, real-time data streaming
- ⚡ System Integration - Designing end-to-end systems combining ML, hardware, and UI
- Built OCR-based document processing pipelines
- Improved validation workflows by identifying and handling model inconsistencies
- Contributed to ML system integration and deployment pipelines
- Programming: Python, C, C++
- Machine Learning: Random Forest, Classification Models, Feature Engineering, Model Evaluation
- Libraries: NumPy, Pandas, Scikit-learn
- Computer Vision: MediaPipe (pose estimation), OpenCV
- Systems: ESP32, IoT Architectures, Real-Time Systems
- Tools: Git, Jupyter Notebook, VS Code
🔗 https://github.com/DadeJahnavi/physioguide-ai
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Built a multi-modal ML system (IMU + vision fusion) for rehabilitation exercise analysis
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Achieved ~98% correctness, ~97% exercise classification, ~94% pose accuracy
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Designed a resilient pipeline with fallback modes:
- Vision-only feedback when IMU data is unavailable
- IMU-only haptic feedback when camera input is absent
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Developed feature-engineered ML pipeline with real-time inference
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Integrated ESP32-based sensing with vision-based validation
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Built dashboard for real-time feedback and monitoring
🔗 https://github.com/DadeJahnavi/ESP32-6DOF-Robotic-Arm
- Built a WiFi-controlled robotic arm with real-time browser-based calibration interface
- Implemented a calibration-driven pick-and-place execution pipeline
- Solved real hardware challenges:
- Servo jitter and motion instability
- Power supply and voltage drop issues
- I2C communication failures
- Designed system to operate independently after calibration without continuous input
- Demonstrated stable and repeatable operation across multiple execution cycles
🔗 https://github.com/DadeJahnavi/Smart-Medication-Reminder
- Built IoT-based automated pill dispensing system
- Implemented RTC-based scheduling and adherence tracking
- Developed monitoring interface for tracking medication intake
🔗 https://github.com/DadeJahnavi/Air-Pollution-Monitoring-System
- Built real-time AQI monitoring system using environmental sensors
- Displayed live pollutant data via embedded display interface
- 🥈 2nd Prize - National Level Project Expo (46 teams)
- 👩💼 Lead Coordinator - IEEE Technovate 2K25 (700+ participants)
- 🎯 Coordinator - ISTE Technisum (500+ attendees)
💡 Building intelligent systems that bridge machine learning with real-world deployment constraints.