An Efficient Machine Learning-based Elderly Fall Detection Algorithm

by   Faisal Hussain, et al.

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


page 2

page 5


A fall alert system with prior-fall activity identification

Falling, especially in the elderly, is a critical issue to care for and ...

Providing Confidential Cloud-based Fall Detection from Remote Sensor Data Using Multi-Party Computation

Fall detection systems are concerned with rapidly detecting the occurren...

A Fourier Domain Feature Approach for Human Activity Recognition Fall Detection

Elder people consequence a variety of problems while living Activities o...

CareFall: Automatic Fall Detection through Wearable Devices and AI Methods

The aging population has led to a growing number of falls in our society...

TCP Prague Fall-back on Detection of a Classic ECN AQM

The IETF's Prague L4S Requirements expect an L4S congestion control to s...

Elderly fall risk prediction based on a physiological profile approach using artificial neural networks

Falls play a critical role in older people’s life as it is an important ...

Please sign up or login with your details

Forgot password? Click here to reset