Overall Summary:
This week, we delivered our design review presentation and focused on component selection, toolchain configuration, and prototyping our initial development hardware. We verified the functionality of our core dev boards (u-blox ANT-B10, Raspberry Pi Pico WH, and FPGA), established communication with the FPGA’s Linux core via SSH, and finalized key wearable components, such as selecting the low-power ST LSM6DSOX IMU on a breakout board to de-risk prototype assembly. We also set up our Nordic direction-finding software environment. Our project is on schedule for fabricating the prototype next week to gather data.
Key Progress
Design Presentation:
Haowen + Matthew: Assisted in writing the Design Presentation slide deck.
Jeffrey: Wrote and delivered the Design Presentation on Wednesday.
Signals:
Haowen: Unboxed and verified the u-blox ANT-B10 antenna array, mapped SWD and UART pin headers, and configured the nRF Connect SDK environment to prepare for flashing the direction-finding firmware and testing 20 Hz BLE Constant Tone Extension (CTE) packet broadcasts from the Pico WH.
Software:
Matthew: Maintained development trajectory for the web platform’s upcoming historical data portal, setting up infrastructure for serving performance insights to users.
Jeffrey: Established SSH access to the FPGA’s Linux environment and began implementing a C benchmark suite on the ARM core to evaluate processing latency against FPGA SystemVerilog logic.
Hardware:
Matthew: Evaluated multiple IMUs and selected the ST LSM6DSOX 6-axis chip for its ultra-low power consumption (~0.5 mA) and decently low output error.
Jeffrey: Validated basic power and GPIO operation across all received microcontroller boards.
New Risks + Mitigation: Dead Reckoning Drift vs Angle of Arrival Inaccuracy
Tracking swimmer position down the lane relies on combining wearable IMU dead reckoning with Bluetooth Angle of Arrival (AoA) direction finding at the poolside base station. However, both modalities face compounding error sources. Dead reckoning suffers from exponential integration drift within seconds of forward swimming, while AoA suffers from signals reflecting off the water’s surface, and periodic signal loss when the wearable is submerged
We plan to manage these issues through a combination of the two systems:
1. Swimmers should be physically restricted to linear lanes, and cannot deviate from a 1D line by more than 1.5m. Rather than solving for 2D/3D positions, we will project AoA angle rays directly onto the swimmer’s known 1D lane vector.
2. The continuous IMU dead reckoning model will be updated periodically whenever the wearable surfaces and transmits a valid AoA packet.
3. Zero-Velocity Reset: Every time the IMU detects an impulse from a swimmer pushing off the wall, their spatial position and velocity will be hard-reset to the known physical pool boundary, clearing all accumulated dead-reckoning drift every lap.
No significant changes to the schedule were made at this point in time.