Executive Summary
This week, we locked down our hardware choices, set up the web dashboard UI, and validated our BLE 5.1 Angle of Arrival (AoA) pipeline. Offloading RF processing to Nordic silicon avoids complex FPGA timing work. Next week, we’ll focus on assembling the wearable prototype to collect real telemetry data.
Key Progress
- Signal (Haowen): Confirmed the nRF5340 Network Core handles BLE CTE sampling in hardware, outputting clean angle scalars over UART (115200 baud). Designed a 2-stage filter (Median + Moving Average) to handle water surface multipath. Unboxed the nRF5340-DK, ANT-B10, and Pico WH, and prepped the nRF SDK in VS Code.
- Software (Matthew): Set up the web app repo and built a poolside-friendly UI featuring an Outdoor High-Contrast mode, a 2-second touch-lock screen, and Wake-Lock API integration. Added a stream-level Jitter Buffer for out-of-order packets and built a 3-tap tag pairing menu.
- Hardware (Jeffrey): Established a two-phase wearable strategy (off-the-shelf prototype -> custom PCB). Selected Raspberry Pi Pico + Pimoroni MCP73831 for the prototype and XIAO RP2040 Plus + LiFePO4 battery for the final PCB. Created architecture diagrams for the design review.
Risks & Mitigations
- Shipping & Power Supply Delays: FPGA power supply is delayed -> Mitigated by using off-the-shelf Pico WH and nRF5340-DK boards to assemble the wearable prototype first.
- Water Surface Reflections: Splashes cause signal jitter -> Mitigated via Median filtering on compute and a Jitter Buffer on the web app.
Considerations
Part A was written by Jeffrey Liu:
We don’t expect our product to have too much of a direct impact on public health, safety, and welfare. One could make the argument that since exercise is good for health, by making a device that improves the swimming experience and improves coaches’ ability to train swimmers, that there will be a broad benefit to public health by encouraging more people to exercise and stay healthy. Swimming is also an important survival skill in certain areas, so encouraging swimming may help public health in that regard. With regards to safety, our main concern with our project is that there is no safety hazard to the users. In our case, the primary safety consideration would be that the wearable device needs to have a battery. With that in mind, our battery choice of a LiFePo battery is among the safest lithium-ion battery chemistries that is available and we will keep battery safety in mind when designing the surround circuitry. Additionally, the material we will make sure is skin-safe in water, as it will be worn for prolonged times. Our project is probably least applicable to welfare, as swimming training/guidance is not a basic provision that people need to survive.
Part B is written by Matthew Kwee:
Our platform addresses technological and socioeconomic disparities across competitive swimming. Swimming has a well-documented resource divide: elite collegiate programs and affluent private clubs can afford multi-thousand-dollar camera rigs, underwater timing gates, and dedicated biomechanics staff, while municipal clubs and public high schools rely solely on hand-held stopwatches and visual estimates. By delivering accessible, low-cost tracking, our project democratizes sports analytics. This lowers the barrier to data-driven coaching for underfunded teams, fostering greater competitive equity across different socioeconomic communities.
Our system also meaningfully alters coach-athlete dynamics. In a typical practice with 20 to 30 swimmers circling in crowded lanes, a coach can realistically only focus their stopwatch and feedback on one or two athletes at a time. This naturally leads to unintentional disparities in attention and may result in perceived bias. By providing simultaneous, automated metrics across all lanes, coaches can distribute attention and constructive feedback much more equitably, building a fairer and more supportive team training environment.
We must also consider the social and psychological impact of continuous performance surveillance on young athletes. In high-pressure sports environments, constant metric tracking and public leaderboards can sometimes foster unhealthy peer comparison, burnout, or anxiety. To protect team culture and swimmer well-being, we will design the software around configurable privacy controls. Coaches can decide whether performance data is shared openly on a team board to foster healthy camaraderie, or kept private between the individual swimmer and the coaches to focus purely on personal development.
Part C is written by Haowen Huang:
For our project, economic considerations directly shaped our choice of hardware, aiming to deliver high-precision tracking without the prohibitive price tag of existing solutions. By using commercial off-the-shelf components (Nordic nRF5340, u-blox ANT-B10 array, and Raspberry Pi Pico), we avoided the heavy development and production costs of custom ASICs or sub-nanosecond FPGA hardware. This keeps both our prototyping expenses and eventual unit manufacturing costs low. Our system also fills a major cost gap in sports analytics. Existing swimmer-tracking tech usually relies on optical camera rigs or underwater timing systems that cost thousands of dollars, putting them out of reach for most high school programs or local swim clubs. Using BLE 5.1/5.2 Angle of Arrival lets us deliver real-time spatial data at a fraction of the hardware cost, making data-driven coaching accessible to smaller teams with tighter budgets. Finally, long-term maintenance costs are kept low through a modular design. The wearable tag uses an energy-efficient microcontroller and a rechargeable battery to extend hardware lifespan. Because individual tags are cheap and easy to swap out, a damaged or depleted tag can be replaced on the spot without replacing or reconfiguring the poolside base station.
Next Week’s Goals
- Signal (Haowen): Flash the nRF5340 SDK Rx sample, verify raw angle output over UART, and wire it to the Pico WH.
- Software (Matthew): Pause web development temporarily to assist Jeffrey with physical wearable assembly and data logging setup.
- Hardware (Jeffrey): Place orders for verified prototype/PCB parts and assemble the initial wearable prototype once parts arrive.
