Sean Leishman developed a foundational data processing feature for the Hyp-ed/hyped-2025 repository, focusing on accelerometer data. He implemented the AccelerometerPreprocessor module in Rust, which computes acceleration magnitude, filters outliers using quartile analysis, and assesses sensor data reliability. This approach improved the quality and reliability of sensor telemetry, supporting downstream analytics and machine learning workflows. Sean also updated project dependencies and removed unused test code, streamlining maintenance and reducing technical debt. His work demonstrated proficiency in embedded systems, data filtering, and sensor data processing, delivering an end-to-end solution that enhanced the robustness of the codebase within a month.