Atış Eğitiminde Sensörlerin ve Biometrik Verilerin Entegrasyonu: Akıllı Bir Karar Destek Sistemi

Abstract

Atış eğitimi, yüksek maliyetler, zaman kısıtlamaları ve manuel değerlendirme süreçlerinin sınırlılıkları nedeniyle verimlilik açısından önemli zorluklar sunar. Dahası, kursiyerlerin performansını objektif olarak değerlendirmek genellikle zordur, bu da öğrenme sürecini yavaşlatır. Bu çalışmada, eğitim verimliliğini artırmak ve maliyetleri düşürmek amacıyla hem ateşli silah hem de hedefe entegre edilmiş sensör tabanlı bir sistem geliştirilmiştir. İvmeölçer (ACC) ve jiroskop (GYRO) sensörleri, ateşli silahın dinamik hareketlerini hassas bir şekilde ölçerek geri tepme, titreşim, yön değişiklikleri ve açısal hız gibi kritik verileri gerçek zamanlı olarak yakalar. Ek olarak, sensör donanımlı hedef sistemi her atışın doğruluğunu anında tespit eder ve vuruş veya ıskalar hakkında anında geri bildirim sağlar. Önerilen sistem, sadece ateşli silah hareketlerini izlemekle kalmaz, aynı zamanda daha kapsamlı bir performans analizi sunmak için biyometrik verileri de içerir. Atış performansını doğrudan etkileyen önemli bir biyometrik faktör olan kalp atış hızı, gerçek zamanlı olarak izlenir ve analiz edilir. Bu, eğitmenlerin sadece mekanik hataları değil, aynı zamanda kursiyerlerin psikolojik ve fizyolojik durumlarını da dikkate alarak daha bilinçli ve etkili geri bildirimler sunmalarını sağlar. Ayrıca, toplanan verilerden çıkarılan özelliklerin önemi Random Forest algoritması kullanılarak değerlendirilmiştir. Kalp atış hızının veri kümesindeki varyansın yaklaşık %28'ini oluşturduğu gözlemlenmiştir. Son olarak, Destek Vektör Makineleri (SVM) algoritması kullanılarak atış tahmininde %74'lük bir doğruluk oranına ulaşan bir tahmin modeli geliştirilmiştir.

Keywords

• Atış eğitimi
• biyometrik veriler
• karar destek sistemleri
• makine öğrenimi
• destek vektör makineleri

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

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

• Shooting training
• biometric data
• decision support systems
• machine learning
• support vector machines

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