Personal project · 2021
BK-Shoot
Low-cost IoT device that detects basketball makes/misses in real time using IR + vibration sensor fusion.
Honourable Mention - 12th Planter de Sondeigs i Experiments
Awarded by UPC, UAB, UB, and Idescat for "combining statistics, Big Data, AI, and programming with sports".
ICFO Young Photonics Congress
Selected to present research on sensor fusion and optical detection to industry experts.
C++ · IoT · Arduino · Android
Challenge
Basketball analytics rely on expensive proprietary systems, making real-time shooting metrics inaccessible to amateur players.
Approach
Engineered a <€25 IoT device fusing IR and vibration sensors to classify makes, misses, and swishes at ~95% accuracy. Stats stream via Bluetooth to a custom Android app.
What it does
- Sensor Fusion Algorithm. Custom C++ algorithm correlates IR triggers and vibration spikes within a 1000ms window.
- Cost-Effective Hardware. Built with Arduino Uno, IR sensors, and vibration modules for under €25.
- Full-Stack System. Designed circuitry, embedded firmware, Bluetooth protocol, and Android app end-to-end.
- Field-Validated. Tested with 20+ participants and ~2,000 shots achieving statistical significance.
Gallery
