An Efficient Machine Learning-based Elderly Fall Detection Algorithm
This addresses a critical safety issue for elderly individuals, though it appears incremental as it builds on existing accelerometer-based methods with improved accuracy.
The paper tackled the problem of accurately detecting falls in elderly people to prevent serious injuries, achieving 99.98% accuracy using a Support Vector Machine classifier on a publicly available dataset.
Falling is a commonly occurring mishap with elderly people, which may cause serious injuries. Thus, rapid fall detection is very important in order to mitigate the severe effects of fall among the elderly people. Many fall monitoring systems based on the accelerometer have been proposed for the fall detection. However, many of them mistakenly identify the daily life activities as fall or fall as daily life activity. To this aim, an efficient machine learning-based fall detection algorithm has been proposed in this paper. The proposed algorithm detects fall with efficient sensitivity, specificity, and accuracy as compared to the state-of-the-art techniques. A publicly available dataset with a very simple and computationally efficient set of features is used to accurately detect the fall incident. The proposed algorithm reports and accuracy of 99.98% with the Support Vector Machine(SVM) classifier.