Health conscious is the main subject of interest, its testability increasing with age. Thus, taking care of the elderly people is a very important responsibility. In such a scenario, technology helps people by providing life support. One of the main causes of poor health or old age is 'falling'. In this paper, a fall detection system is suggested based on machine learning. The system detects falls by classifying different activities into falling and non-falling actions and alerts parents or caregivers of senior students in an emergency. The SisFall dataset with diverse activities from many participants is used to compare the characteristics. SVM and decision tree machine learning algorithms are used to detect droplets based on computed features. The system obtains up to 96% accuracy by using decision tree algorithm.

Health, dataset, falling, accuracy, detect, Algorithms.