From IMUs to Smartwatches: Measuring Performance in Practical Shooting
Bibliographic record
Abstract
Human Activity Recognition (HAR) has seen significant advancements through wearable sensors and machine learning, particularly in sports like running and swimming. However, high-speed tactical disciplines such as practical shooting remain underexplored due to their complex, overlapping actions and the need for non-intrusive equipment. This study investigates the feasibility of using commercial smartwatches and smartphones to replace laboratory-grade inertial measurement units (IMUs) for performance monitoring in practical shooting. Twelve athletes participated in trials, with data collected using full-body IMUs and reduced sensor setups. Key actions—shooting, running, reloading, and extraction—were identified and classified using binary classifiers and multi-label strategies. Results showed that a combination of sensors on the dominant wrist and hip provided optimal accuracy, outperforming single-sensor setups and audio-based configurations. Additionally, training classifiers on complete exercise sessions yielded better performance than isolated actions. The findings demonstrate the potential of commercial devices for HAR in practical shooting, offering a cost-effective and energy-efficient solution for real-world deployment. This approach could be extended to other sports, balancing accuracy, cost, and usability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".