About
Designed and implemented a state machine-driven IoT system for behavioral research in progressive overload training paradigms. The system integrates SENT linear induction sensors and beam-break detection to quantify behavioral responses. Features include fault-tolerant data architecture with SQLite buffering, automated rsync synchronization from distributed Raspberry Pi nodes, and automated hardware control systems for pellet dispensing and sensor calibration.
Projects
MSPD
Distributed, IoT-driven behavioral research platform integrating multi-sensor inputs, real-time event processing, and automated hardware control.
- Enabled high-throughput, reproducible behavioral studies via fault-tolerant edge data pipelines and automated closed-loop control.
- Achieved 100% data fidelity with local SQLite buffering, automated rsync synchronization, and JSON event streaming.
- Automated pellet dispensing, sensor calibration, and chamber ops via Python event handling, reducing manual intervention by 75%.
Linear Sensor Unit Test
Reverse engineered Microchip LXK3302A serial protocol and implemented Python tooling; integrated NEMA17 motion to map velocities using TMC2209 and Arduino.
Pellet Dispenser Unit Test
ESP32 + A4988 stepper control to dispense pellets with precise 45° steps; event manager with daemon threads for unit-test orchestration.