Evidence map›Paper›PMID 41755100›Full record

ArticleSensors (Basel, Switzerland)2026

Landmine Press Kinematics Measured with an Enhanced YOLOv8 Model and Mathematical Modeling.

Rui Zhao, Rong Cong, Ruijie Zhou, Kelong Lin, Jianke Yang, Tongchun Kui, Jiajin Zhang, Ran Wang, Rou Dong

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Rui ZhaoCollege of Physical Education, Yunnan Agricultural University, Kunming 650201, China.ORCID 0009-0006-6232-7481
Rong CongCollege of Physical Education, Yunnan Agricultural University, Kunming 650201, China.ORCID 0000-0001-8243-2170
Ruijie ZhouCollege of Physical Education, Yunnan Agricultural University, Kunming 650201, China.ORCID 0009-0008-4431-7030
Kelong LinCollege of Physical Education, Yunnan Agricultural University, Kunming 650201, China.ORCID 0009-0000-2723-1456
Jianke YangCollege of Physical Education, Yunnan Agricultural University, Kunming 650201, China.ORCID 0009-0002-0955-466X
Tongchun KuiCollege of Physical Education, Yunnan Agricultural University, Kunming 650201, China.ORCID 0009-0000-7623-6448
Jiajin ZhangCenter for Sports Intelligence Innovation and Application, Yunnan Agricultural University, Kunming 650201, China.
Ran WangSchool of Athletic Performance, Shanghai University of Sport, Shanghai 200438, China.ORCID 0000-0002-7148-0764
Rou DongCollege of Physical Education, Yunnan Agricultural University, Kunming 650201, China.

Funding

the open project of Yunnan Key Laboratory of Biodiversity Information BIKF2024-06
6 · The paper itself

Abstract

The landmine press is a reliable and valid test for assessing upper-body push strength. However, its application is constrained by the limitations of current mainstream monitoring technologies, such as linear position transducers (LPTs). These devices require physical attachment to the barbell, they rely on proprietary software, and their measurement accuracy can degrade under high-load conditions due to sensor drift and electromechanical noise. To address these limitations, this study developed a markerless, non-contact, and vision-based system using an enhanced YOLOv8-OBB model and a mathematical modeling framework to measure four kinematic indicators during the concentric phase of the landmine press. By integrating a polarized self-attention mechanism, an improved C3k2 module, and an optimized SPPF structure, the system significantly enhanced detection accuracy and robustness for the small targets at both ends of the barbell, achieving an mAP@0.5 of 0.995 on the test set. A method comparison study was conducted against a widely used LPT device (GymAware) across four loads (20-35 kg) in 247 trials. The results showed strong correlations (r > 0.85) for peak velocity, mean velocity, peak power, and mean power. Although the vision-based method systematically overestimated velocity metrics, the bias was predictable. Notably, under the highest load (35 kg), where LPT limitations are pronounced, the vision system demonstrated comparative stability, suggesting its potential advantage in mitigating sensor-related errors. The findings demonstrate that this vision-based system offers a reliable and practical alternative for monitoring landmine press kinematics, suitable for both training and scientific research.

Indexed as

kinematicslandmine pressmathematical modelingYOLOv8

Identifiers

PMID41755100
PMCPMC12944738

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.