Skip to main content Jan 2025 - now Jan 2025 - now AMBER Affordable Multimodal Sensor-Based Environmental Risk Detector AI/ML ›
- Engineered a wearable industrial helmet, with 6 multimodal sensors & edge computing to detect environmental hazards
- Developed a custom data pipeline and TLV-based sensor communication protocol in python, optimizing sensor fusion
- Soldered and wired electronic components, ensuring durable sensor connectivity and efficient signal processing
Jan 2025 - May 2026 Jan 2025 - May 2026 Undergraduate Researcher McComish Dept of Electrical Engineering and Computer Science, SDSU AI/ML ›
- Awarded $5,000 Future Innovators grant to develop AMBER: Python-based real-time hazard detection system with edge computing
- Researching sensor fusion and ML-based risk assessment models using Python, OpenCV for hardware prototyping and testing
- Investigating low-latency risk detection integrating Raspberry Pi 5 edge devices with multimodal sensor fusion
May 2026 - May 2026 May 2026 - May 2026 IMPACT Band BLE impact-detection wearable built on the Seeed Studio XIAO nRF52840 Sense Hardware ›
- Sampled the onboard LSM6DS3TR-C 6-axis IMU at 104 Hz and aggregated acceleration and detected impacts into 5-second window summaries
- Implemented Nordic UART Service transport for newline-framed JSON plus a Python/Bleak receiver for packet reassembly and JSONL logging
- Built 17 native firmware tests covering packet construction, window aggregation, impact detection and long-packet BLE transport behavior
- Validated physical taps up to 8.989g dynamic acceleration and recovered an intermittently failing battery-prepared board using a replacement control board