  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.
