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

## TL;DR

A vision-based system using an enhanced YOLOv8 model accurately tracks landmine press movements, offering a reliable alternative to traditional sensors.

## Contribution

The novel integration of an enhanced YOLOv8-OBB model with a mathematical framework provides a markerless, non-contact method for measuring landmine press kinematics.

## Key findings

- The vision system showed strong agreement with commercial sensors for velocity and power metrics.
- It maintained stability under high loads where traditional sensors experience drift and noise.
- The system achieved high detection accuracy (mAP@0.5 of 0.995) for small targets on the barbell.

## Abstract

What are the main findings?
An enhanced YOLOv8-OBB vision system provides a markerless approach for tracking landmine press kinematics, demonstrating strong agreement with a commercial linear transducer across velocity and power metrics.The system shows consistent measurement stability under high-load conditions, where sensor-based devices are susceptible to electromechanical noise and drift, despite exhibiting a predictable overestimation in velocity.

An enhanced YOLOv8-OBB vision system provides a markerless approach for tracking landmine press kinematics, demonstrating strong agreement with a commercial linear transducer across velocity and power metrics.

The system shows consistent measurement stability under high-load conditions, where sensor-based devices are susceptible to electromechanical noise and drift, despite exhibiting a predictable overestimation in velocity.

What are the implications of the main findings?
This computer vision approach offers a practical and non-invasive alternative to attached sensors for monitoring strength training, suitable for both field and laboratory environments.The study illustrates how advanced object detection models can be adapted to develop specialized “vision sensors” for biomechanical applications, bridging computer vision and sports science.

This computer vision approach offers a practical and non-invasive alternative to attached sensors for monitoring strength training, suitable for both field and laboratory environments.

The study illustrates how advanced object detection models can be adapted to develop specialized “vision sensors” for biomechanical applications, bridging computer vision and sports science.

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.