- Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi
- Cilt: 13 Sayı: 2
- Exploring the performance of PySpark and Scikit-Learn libraries in developing fall detection systems
Exploring the performance of PySpark and Scikit-Learn libraries in developing fall detection systems
Authors : Erhan Kavuncuoglu
Pages : 582-592
Doi:10.28948/ngumuh.1388789
View : 34 | Download : 51
Publication Date : 2024-04-15
Article Type : Research
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 : Düşme Algılama, Yapay Zeka, Makine Öğrenmesi, PySpark, Scikit-Learn