Düşme algılama sistemlerinin geliştirilmesinde PySpark ve Scikit-Learn kütüphanelerinin performansının araştırılması

Year 2024, , 582 - 592, 15.04.2024

Erhan Kavuncuoglu

https://doi.org/10.28948/ngumuh.1388789

Abstract

Düşmeler, genellikle ciddi yaralanmalara ve yaşlı nüfusun yaşam kalitesinin azalmasına neden olan önemli bir risk oluşturur. Doğru ve etkili düşme tespit sistemleri, bu riskleri azaltmada önemli bir rol oynayabilir. Bu çalışma, düşme tespit modellerinin geliştirilmesinde PySpark ve Scikit-Learn kütüphanelerinin performansını karşılaştırmalı bir analiz sunmaktadır. Her iki kütüphane de kullanılarak, lojistik regresyon, gradyan arttırma sınıflandırıcısı, rastgele orman, destek vektör makinesi ve karar ağacı dahil olmak üzere beş popüler makine öğrenme algoritması kullanılarak düşme tespit modelleri oluşturuldu. Modeller, kapsamlı metrikler (doğruluk, duyarlılık, özgüllük, karışıklık matrisi) kullanılarak değerlendirildi. Çalışmada düşme ve günlük yaşam aktivite verilerinden oluşan Sisfall veri setinden 26 farklı özellik beş ana kategoride çıkarıldı: temel istatistiksel özellikler, frekans alanı özellikleri, zaman serisi özellikleri, hareket özellikleri ve ilişkisel özellikler. Bu özellikler, düşme tespit modellerine düşmeleri tanıma yeteneklerini artırmak için dahil edildi. Bulgular, hem PySpark hem de Scikit-Learn'ün düşme tespitinde güçlü ve etkili sonuçlar sunduğunu göstermektedir. Her iki kütüphane de en yüksek performans oranlarına lojistik regresyon ile ulaşılmıştır. Ayrıca, PySpark, testte daha iyi performans sergileyen Scikit-Learn'e göre biraz daha uzun eğitim süreleri sergilemiştir. Sonuç olarak, bu çalışma, yaşlıların güvenliğini ve refahını artırmak için düşme tespit sistemlerinin geliştirilmesine katkıda bulunduğu gibi yeni bir özellik çıkarma yöntemi sunarakta literatüre katkıda bulunuyor.

Keywords

Düşme Algılama, Yapay Zeka, Makine Öğrenmesi, PySpark, Scikit-Learn

Exploring the performance of PySpark and Scikit-Learn libraries in developing fall detection systems

Abstract

Falls pose a significant risk, often resulting in serious injuries and reduced quality of life for the elderly population. Accurate and effective fall detection systems can play an important role in reducing these risks. This study presents a comparative analysis of the performance of PySpark and Scikit-Learn libraries in the development of fall detection models. Using both libraries, fall detection models were built using five popular machine learning algorithms, including logistic regression, gradient boosting classifier, random forest, support vector machine and decision tree. The models were evaluated using comprehensive metrics (accuracy, sensitivity, specificity, confusion matrix). In the study, 26 different features were extracted from the Sisfall dataset consisting of falls and activities of daily living data in five main categories: basic statistical features, frequency domain features, time series features, motion features and relational features. These features were incorporated into the fall detection models to increase their ability to recognise falls. The findings show that both PySpark and Scikit-Learn offer powerful and effective results in fall detection. The highest performance rates of both libraries were achieved by logistic regression. Furthermore, PySpark exhibited slightly longer training times than Scikit-Learn, which performed better in the test. In conclusion, this study contributes to the development of fall detection systems to improve the safety and well-being of the elderly and contributes to the literature by providing a new feature extraction method.

Keywords

Fall Detection, Artificial Intelligence, Machine Learning, PySpark, Scikit-Learn

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