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  • Düzce Üniversitesi Bilim ve Teknoloji Dergisi
  • Cilt: 13 Sayı: 3
  • Integration of Sensor and Biometric Data in Shooting Training: An Efficient and Goal-Oriented Approa...

Integration of Sensor and Biometric Data in Shooting Training: An Efficient and Goal-Oriented Approach through an Intelligent Decision Support System

Authors : Enver Küçükkülahlı
Pages : 1385-1405
Doi:10.29130/dubited.1716947
View : 91 | Download : 111
Publication Date : 2025-07-31
Article Type : Research Paper
Abstract :Shooting training presents significant challenges in terms of efficiency due to high costs, time constraints, and the limitations of manual assessment processes. Furthermore, objectively evaluating trainees’ performance is often difficult, which in turn slows down the learning process. In this study, a sensor-based system integrated into both the firearm and the target was developed to enhance training efficiency and reduce costs. Accelerometer (ACC) and gyroscope (GYRO) sensors precisely measure the dynamic movements of the firearm, capturing critical data such as recoil, vibration, directional changes, and angular velocity in real time. Additionally, the sensor-equipped target system instantly detects the accuracy of each shot and provides immediate feedback regarding hits or misses. The proposed system not only monitors firearm movements but also incorporates biometric data to deliver a more comprehensive performance analysis. Heart rate, a key biometric factor that directly influences shooting performance, is monitored and analyzed in real time. This allows instructors to provide more informed and effective feedback by considering not only mechanical errors but also the psychological and physiological states of the trainees. Moreover, the importance of features extracted from the collected data was evaluated using the Random Forest algorithm. It was observed that heart rate accounts for approximately 28% of the variance in the dataset. Finally, a predictive model was developed using the Support Vector Machines (SVM) algorithm, achieving an accuracy rate of 74% in shot prediction.
Keywords : Atış eğitimi, biyometrik veriler, karar destek sistemleri, makine öğrenimi, destek vektör makineleri

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