Design and Implementation of an IMU Sensor System to Estimate a Hockey Puck’s Peak Velocity
Bibliographic record
Abstract
The rapid advancement in sensor technology can revolutionize how sports dynamics are understood and analyzed. This thesis focuses on designing and implementing an Inertial Measurement Unit (IMU) sensor system to be deployed within a hockey puck to estimate its peak velocity. \n \nThe research involved the intricate design of a sensor system comprising an accelerometer, two gyroscopes, and a magnetometer. Moreover, puck preparation was carried out to secure the sensor and battery within the puck to ensure functionality and durability. Furthermore, a data acquisition system is developed to receive, save, and plot data transmitted via Bluetooth Low Energy (BLE) protocol. \n \nThree distinct methods for estimating the puck's peak velocity from the sensor data are compared. It is discovered that the method based on an extended Kalman filter and utilizing data from all three sensor types exhibits superior accuracy. This method is subsequently validated under various hockey shot conditions, reinforcing its practical applicability. Moreover, the relationship between velocity estimation error versus true velocity is investigated. \n \nPrimarily designed for research studies, this work offers a foundational understanding of hockey puck dynamics, despite the sensor system not being tailored for real-game scenarios. The insights gained have substantial implications for further sports analytics and player training. Furthermore, the results outline a promising pathway for future sports engineering and wearable technology investigations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".