20.500.12556/RUL-134384 A Random Forest-based accuracy prediction model for augmented biofeedback in a precision shooting training system 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. precision shooting accuracy prediction model augmented feedback random forest natančno streljanje model napovedovanja točnosti poudarjena povratna informacija random forest true false true Angleški jezik Slovenski jezik Članek v reviji 2022-01-12 11:54:14 2022-01-12 11:54:15 2022-09-13 03:54:51 0000-00-00 00:00:00 2020 0 0 16 str. iss. 16, art. 4512 Vol. 20 Aug. 2020 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 004:799.3:621.39 1424-8220 10.3390/s20164512 25029123 RAZ_Guo_Junqi_2020.pdf RAZ_Guo_Junqi_2020.pdf 1 EC65264F3A4066F4090F764DD401D65B ba5f27ed5b934f8cd8dc0f15e6cbfd6c135f64c01fd1961b0fb44402c977b4af 1ddf6bcc-7396-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=152806 https://www.mdpi.com/1424-8220/20/16/4512 1 cdd29c28-7395-11ec-abdb-00155dcfd717 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=152805 Fakulteta za elektrotehniko 0 0 0