Reliable jump detection for snow sports with low-cost MEMS inertial sensors
Bibliographic record
Abstract
Body-mounted devices, incorporating low-cost micro-electromechanical systems (MEMS) Inertial Measurement Units (IMUs), for real-time sports performance feedback are commercially available. In sports such as skiing, snowboarding, and mountain biking, aerial jumps can be detected with these devices and performance variables including air time and jump drop can be calculated real-time. However, the performance of currently used real-time athletic jump detection algorithms using MEMS IMUs is unsatisfactory in terms of accuracy, power efficiency, and reliability. In this paper, a novel algorithm for jump detection with a head-mounted MEMS IMU is proposed. Two novel methods used in this algorithm, namely Windowed Mean Canceled Multiplication and Preceding and Following Acceleration Difference, are introduced. Field experiments are conducted and the results of the proposed algorithm are compared with those of algorithms used in two state-of-the-art sport performance measurement devices. Results demonstrate that the proposed jump detection algorithm comprehensively outperforms these commercial algorithms.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".