Wireless Instrumented Curling Broom Phase III: Final Design Report
Bibliographic record
Abstract
Curling Canada has requested the design of a wireless instrumented curling broom to help coaches better understand the effectiveness of a curler when sweeping. The team of engineering students is tasked with designing a curling broom to record and transmit useful performance data wirelessly to a mobile device or computer. The goal of this project is to design a device that accurately measures the desired performance data which will eventually help coaches and curlers perform at a higher level while curling. This Final Design Report (FDR) contains a summary of the project background, objectives, customer needs, project constraints, and metrics. The main client needs include accurate measurement of both the force applied on the ice and the sweeping frequency, ability to differentiate between the push and the pull stroke, and ability to output the collected data wirelessly to a device to be viewed. Next, an overview is provided of the functional decomposition and synthesis approach taken during the concept generation and selection phase of the project. The final design is comprised of an Arduino Nano 33 BLE microcontroller with a built-in inertial measurement unit and Bluetooth capabilities alongside a Michigan Scientific Model TR3D-B-1K 3-axis load cell to meet required client needs. The final device may be broken down into two groups: the electronics package and the load cell package. The electronics package is secured to the broom handle by a clamp and is self contained in a plastic shell that contains the microcontroller, three HX711 load cell amplifiers, a LARGE low temperature 18650 battery and battery shield. The load cell package is made up of an upper and lower adaptor, allowing the load cell to be mounted inline between the broom head and the broom handle. The load cell is secured between the adaptors and plugged into the electronics package by a 12 pin connector. The final design meets or exceeds all metrics required by Curling Canada, with a total cost and weight of $2,866.64 and 247 grams, respectively. Further recommendations are provided to aid in the continuation of the project. These include recommendations for the manufacturing of the design, app development, device testing and device feedback.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".