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Movement Retraining using Real-time Feedback of Performance

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Published on: January 17, 2013

A Random Forest-Based Accuracy Prediction Model for Augmented Biofeedback in a Precision Shooting Training System.

Junqi Guo^1,2, Lan Yang^1,3, Anton Umek⁴

¹School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China.

Sensors (Basel, Switzerland)

|August 19, 2020

Summary

This study introduces an augmented biofeedback system using machine learning to improve precision shooting training. The system accurately predicts shooting accuracy, reducing training time and costs.

Keywords:

accuracy prediction model augmented feedback precision shooting random forest

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Area of Science:

* Applied Machine Learning
* Human-Computer Interaction
* Sports Science

Background:

* Precision shooting training is crucial for military, law enforcement, and sports.
* Current training methods are costly due to extensive ammunition and trainer time.
* Need for efficient and cost-effective training solutions.

Purpose of the Study:

* To develop an augmented biofeedback system to reduce precision shooting training costs and duration.
* To detect and provide feedback on excessive hand movement, aiming, and triggering errors.
* To enhance shooting accuracy through data-driven insights.

Main Methods:

* Development of an augmented biofeedback system integrating machine learning.
* Innovative use of machine learning for detecting hand movement errors.
* Threshold approach for identifying aiming and triggering errors.
* Random Forest model for predicting precision shot accuracy.

Main Results:

* The Random Forest model achieved a prediction accuracy of 91.27%, outperforming other models.
* Demonstrated a strong correlation between hand movement and precision shooting accuracy.
* The system provides concurrent feedback on hand movement and terminal feedback on aiming/triggering errors.

Conclusions:

* The proposed augmented biofeedback system effectively reduces ammunition usage and training time.
* Machine learning, particularly Random Forest, is highly suitable for precision shooting analysis.
* The system offers a promising solution for optimizing shooting training protocols.