A Random Forest-based accuracy prediction model for augmented biofeedback in a precision shooting training system

Guo, Junqi (author), Yang, Lan (author), Umek, Anton (author), Bie, Rongfang (author), Tomažič, Sašo (author), Kos, Anton (author)

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.

• Language: English
• Keywords: precision shooting, accuracy prediction model, augmented feedback, random forest
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: FE - Faculty of Electrical Engineering
• Publication status: Published
• Publication version: Version of Record
• Year: 2020
• Number of pages: 16 str.
• Numbering: Vol. 20, iss. 16, art. 4512
• PID: 20.500.12556/RUL-134384
• UDC: 004:799.3:621.39
• ISSN on article: 1424-8220
• DOI: 10.3390/s20164512
• COBISS.SI-ID: 25029123
• Publication date in RUL: 12.01.2022

Record is a part of a journal

• Title: Sensors
• Shortened title: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• COBISS.SI-ID: 10176278

Licences

• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
• Description: This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
• Licensing start date: 12.08.2020

Secondary language

• Language: Slovenian
• Keywords: natančno streljanje, model napovedovanja točnosti, poudarjena povratna informacija, random forest