An Efficient Machine Learning-based Elderly Fall Detection Algorithm

TL;DR

This paper presents a machine learning-based fall detection algorithm for the elderly that achieves high accuracy using simple features and a publicly available dataset, improving reliability over existing methods.

Contribution

The paper introduces a computationally efficient fall detection algorithm using SVM that outperforms state-of-the-art techniques in accuracy and simplicity.

Findings

Achieved 99.98% accuracy in fall detection

Uses a simple feature set for computational efficiency

Outperforms existing fall detection methods

Abstract

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.