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

Enver Küçükkülahlı

Makale detayları

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

Yazarlar:

Enver Küçükkülahlı

Yayıncı:

Duzce University

Ana Başlıklar:

Sınıflandırma algoritmaları | Gömülü Sistemler | Silah Sistemleri

Dergi:

Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.13 Issue 3, pp.1385-1405

Belge Tipi:

Makale

Yayın Tarihi:

7/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001355751

pISSN:

2148-2446

Anahtar kelimeler:

Özet:

 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.

 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.

 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.

 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 28ini oluşturduğu gözlemlenmiştir. Son olarak, Destek Vektör Makineleri SVM algoritması kullanılarak atış tahmininde 74lük bir doğruluk oranına ulaşan bir tahmin modeli geliştirilmiştir.

Makale detayları

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

Yazarlar:

Enver Küçükkülahlı

Yayıncı:

Duzce University

Ana Başlıklar:

Sınıflandırma algoritmaları | Gömülü Sistemler | Silah Sistemleri

Dergi:

Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.13 Issue 3, pp.1385-1405

Belge Tipi:

Makale

Yayın Tarihi:

7/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001355751

pISSN:

2148-2446

Shooting training

biometric data

decision support systems

machine learning

support vector machinesAtış eğitimi

biyometrik veriler

karar destek sistemleri

makine öğrenimi

destek vektör makineleri

Sayı

Makale detayları

Yazarlar: Enver Küçükkülahlı

Yayıncı: Duzce University

Ana Başlıklar: Sınıflandırma algoritmaları | Gömülü Sistemler | Silah Sistemleri

Dergi: Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.13 Issue 3, pp.1385-1405

Belge Tipi: Makale

Yayın Tarihi: 7/2025

Belge Dili: İngilizce

Document ID (Org No.): 2001355751

pISSN: 2148-2446

Sayı

Yazarlar:

Enver Küçükkülahlı

Yayıncı:

Duzce University

Ana Başlıklar:

Sınıflandırma algoritmaları | Gömülü Sistemler | Silah Sistemleri

Dergi:

Düzce Üniversitesi Bilim ve Teknoloji Dergisi Vol.13 Issue 3, pp.1385-1405

Belge Tipi:

Makale

Yayın Tarihi:

7/2025

Belge Dili:

İngilizce

Document ID (Org No.):

2001355751

pISSN:

2148-2446