Reliable jump detection for snow sports with low-cost MEMS inertial sensors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 it