● Contributed to multiple iterations of the Software Development Life Cycle (SDLC) for Crash Detection through development and validation.
● Performed Software Quality checks through weekly manual regression tests for Fall Detection, Movement Disorder API, and Crash Detection on future iOS/WatchOS builds. Tests required significant understanding of sensor fusion, algorithm processes and situational events in order to properly debug and ensure features remain fully functional.
● Reported bugs/issues through Radar, providing clear and concise documentation and instructions on reproducibility.
● Ran and maintained automation tests for algorithm verification, regression and updated/improved checks to validate motion data files.[Python, Pandas]
● Curated collections of motion data for Crash Detection and potential false positives at the rate of ~1250 files per week. Major attention to detail needed for sensors such as accelerometers, GPS speed, barometers, and microphones to appropriately and correctly categorize.
● Led investigation into sensor data abnormalities from field data collections.
● Set up devices for data collection, and ensured they were properly configured with the correct builds and scripts before deployment.
● Provided feedback and suggestions on internal tools that would later be implemented, causing increase of curation efficiency.
Using object oriented best practices and design patterns, created python scripts to automate certain systems and processes. Various applications and tools include configuring and producing binary files for GPS receivers, parsing spreadsheet info, performing checksum validation, and verification of configuration parameters on receivers.
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Developed GUI interface for production to perform JTAG image loads on receiver hardware.
