Abstract
In the military, police, security companies, and shooting sports, precision shooting training is of the outmost importance. In order to achieve high shooting accuracy, a lot of training is needed. As a result, trainees use a large number of cartridges and a considerable amount of time of professional trainers, which can cost a lot. Our motivation is to reduce costs and shorten training time by introducing an augmented biofeedback system based on machine learning techniques. We are designing a system that can detect and provide feedback on three types of errors that regularly occur during a precision shooting practice: excessive hand movement error, aiming error and triggering error. The system is designed to provide concurrent feedback on the hand movement error and terminal feedback on the other two errors. Machine learning techniques are used innovatively to identify hand movement errors; the other two errors are identified by the threshold approach. To correct the excessive hand movement error, a precision shot accuracy prediction model based on Random Forest has proven to be the most suitable. The experimental results show that: (1) the proposed Random Forest (RF) model achieves the prediction accuracy of 91.27%, higher than any of the other reference models, and (2) hand movement is strongly related to the accuracy of precision shooting. Appropriate use of the proposed augmented biofeedback system will result in a lower number of rounds used and shorten the precision shooting training process.
Highlights
Accuracy is the most important parameter in precision shooting
We have developed a shot accuracy prediction model of precision shooting based on the Random Forest (RF) [18] classification algorithm
(2) A shot accuracy prediction model for precision shooting based on a random forest (RF) classification algorithm that achieves a higher prediction accuracy than other existing reference models; the proposed model can be considered as a promising candidate solution for shooting accuracy prediction by using kinematic sensors, (3) some reliable results that clearly show that hand movement is negatively correlated with shooting accuracy, and (4) a designed augmented biofeedback system based on machine learning techniques that meets the demand for accuracy prediction in precision shooting training scenarios and the system that can be used in concurrent and terminal feedback scenarios
Summary
Accuracy is the most important parameter in precision shooting. To achieve the desired level of shooting accuracy, trainees have to practice a lot, which takes a lot of rounds (cartridges) and time. Some work has been found in [16,17] on the use of machine learning algorithms to predict shooting results, these use only static data, such as the basic information of the athletes (gender, age, status and precision shooting mode), and not the motion data collected by the sensors They cannot implement an augmented real-time biofeedback system that provides concurrent feedback to users. (2) A shot accuracy prediction model for precision shooting based on a RF classification algorithm that achieves a higher prediction accuracy than other existing reference models; the proposed model can be considered as a promising candidate solution for shooting accuracy prediction by using kinematic sensors, (3) some reliable results that clearly show that hand movement is negatively correlated with shooting accuracy, and (4) a designed augmented biofeedback system based on machine learning techniques that meets the demand for accuracy prediction in precision shooting training scenarios and the system that can be used in concurrent and terminal feedback scenarios.
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