Fall Detection Analysis Using Various Machine Learning Techniques
DOI:
https://doi.org/10.56532/mjsat.v6i3.674Keywords:
Support Vector Machines, Fall Detection System, Machine LearningAbstract
Falls represent a critical health hazard for the elderly demographic, often inducing severe physical trauma and a subsequent decline in psychological well-being that severely compromises their quality of life. To mitigate these risks and foster independent living, the development of a highly dependable and accurate automated fall detection system is of paramount importance. This paper introduces a machine learning-driven fall detection system that utilizes accelerometer data to categorize various activities. The system was trained and evaluated using three machine learning algorithms: Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF), employing the publically accessible SisFall dataset. The method employs a low-pass filter in the data preprocessing phase to eliminate noise from sensor readings, subsequently utilizing an overlapping sliding window technique for feature extraction. The findings indicate that all three classifiers attained outstanding performance, with accuracy, sensitivity, and specificity surpassing 90.0%. Of the three algorithms evaluated, Random Forest exhibited the highest performance, rendering it the optimal selection for fall detection systems intended for elderly individuals.References
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