Evidence of Work
In Step 4, the main question we needed to answer was whether PlayVision actually works well enough to use in a real football game. We had a working prototype but no real data to back it up. So we designed two experiments to test our two biggest unknowns: how fast a play transmits from the coach app to the helmet display, and how long the battery lasts during a game. Before running any tests we wrote out a full plan for each experiment, including all the variables, materials, and step-by-step procedures. We also started working on technical drawings of the helmet mount in Fusion 360 and used SimScale to model how the 3D printed enclosure would hold up under impact.Our experiments confirmed that PlayVision meets both of its performance goals. The average transmission time across ten trials was 0.716 seconds, under our one-second goal. The battery lasted an average of 2.32 hours across three trials, which beats our two-hour target. We put both experiments into a formal lab report with a cover page, abstract, methods, results, and discussion. We also completed a Life Cycle Assessment of PlayVision, researching the environmental impact of every component from raw materials to disposal, and made an infographic to go with it.
Content
Experimental Design and Variables
Experimental design is how you set up a test so the results are reliable. Every experiment has three types of variables: the independent variable is what you change, the dependent variable is what you measure, and controlled variables are things you keep the same so they don't affect the outcome. In our latency experiment, the independent variable was which play was selected, the dependent variable was how long it took to show up on the display, and controlled variables included the Wi-Fi network, the distance between devices, and the hardware. Keeping variables controlled is important because if too many things change at once you can't figure out what actually caused the result.
Latency and Signal Transmission
Latency is the delay between sending a signal and receiving it, measured in seconds or milliseconds. In our system, data is sent using WebSockets, which keep a live open connection between the coach app and the helmet so a new connection doesn't have to open every time. This keeps the delay low and consistent. Our average latency was 0.716 seconds, which we calculated using: Latency = Time play appears − Time coach taps. Running on a local ESP32 hotspot with no outside network traffic is what kept the times so consistent across trials.
Power and Energy
Power is how fast energy is used, measured in watts. Energy is power times time, measured in watt-hours. Our 1000mAh LiPo battery stores about 3.7 watt-hours, calculated using: Energy = Voltage × Capacity = 3.7V × 1.0Ah = 3.7 Wh. Our battery test showed the system runs for about 2.32 hours on one charge, meaning the average power draw is around 1.6 watts. That's a very small number, which is why such a small battery can last through a whole game.
Life Cycle Assessment
A Life Cycle Assessment tracks the total environmental impact of a product from the time raw materials are pulled out of the ground all the way through disposal. For PlayVision, the biggest environmental impacts are cobalt and lithium mining for the LiPo battery and the energy and chemicals used in chip manufacturing. The actual impact during use is tiny since the system only uses 3.7 watt-hours per charge. This connects to environmental science because it helps designers make smarter choices about materials and how things are made.
Simulation and Modeling
Simulation software like SimScale lets you test how an object will behave in the real world using a computer model, without having to physically build and break every version. We used it to model stress on our 3D printed helmet mount to check if it could survive helmet impacts. This connects to physics because it uses concepts like force, stress, and material strength to predict whether a design will hold up before it's finalized.
Reflection
Two things I did well in Step 4 were attention to detail and follow-through. Writing the experiment plan made me slow down and think through every variable and step carefully. I caught things I would have missed otherwise, like needing to fully recharge the battery before every trial and keeping the device distance consistent each time. That kind of careful planning made our results more reliable. I also followed through on all the deliverables for this step including the experiment plan, lab report, LCA report, and infographic. Getting all of it done on time is something I'm proud of.
Two things I still need to improve are measurement precision and starting new tools earlier. Our latency measurement used slow motion video, which worked but still had some human error in reading the exact frame. A better method would have been logging timestamps directly in the Arduino serial monitor so the ESP32 records the exact send and receive times automatically. I knew that was possible but went with the easier option instead. Going forward I want to use the more technically correct method even if it takes more setup. I also waited too long to learn SimScale, which meant I couldn't get as far into the simulation as I wanted before the deadline. I need to start learning new tools earlier so I actually have time to use them well.