A Low Cost Multi-Sensors Navigation Solution for Sport Performance Assessment
Bibliographic record
Abstract
In the past decade the advanced micro-chip technology has made sensors become feasible and approachable in various applications. The cost, power consumption, weight and size properties of micro-electro-mechanical system (MEMS) inertial measurements units (IMU) have motivated the use of miniature IMUs for a variety of applications such as personal digital assistants, smart phones, gaming and monitoring devices. A new application enhances athlete training systems in different sports by either real-time monitoring or off-line analysis and assessment of their performance. This application would use multiple low-cost sensors including an accelerometer triad, a gyroscope triad, a 3D magnetometer, and a barometer. These MEMS-based sensors are the most commonly used micro-chips installed in portable devices. Quantitative analysis of motion is important for athletes' to analyze a maneuver while training. To do so, the proposed solution is to provide high rate position, velocity (from which speed can be derived), and attitude (pitch, roll, and heading), corrected accelerations and angular rates of the athlete. Outputs of this solution are easily interpreted by the athlete and therefore can be helpful in self-assessment. The proposed system is capable of providing position, velocity and attitude solutions for the athletes under consideration, thus giving the athletes themselves the ability to assess their own performances after each training session with the proposed system. Two sports are presented in this paper: skiing and cycling. The proposed system yields an accurate, portable and inexpensive sensor system to support athletes training in real-time coaching and off-line assessment. The developed system can be used not only to track the motion but also to monitor the instantaneous corrected acceleration and turning rate of the body’s behavior.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".