Fall Detection for Elderly People using Machine Learning
Sejal Badgujar, Anju S. Pillai
Abstract
Health is the major worrisome point whose impalpability increases with increase in the age. Thus, taking care of elders is very important responsibility. In such scenario, technology is helping people by providing living assistance. One of the major causes of health degradation or death of elders is `fall'. In this paper, a fall detection system is proposed based on machine learning. The system detects falls by classifying different activities into fall and non-fall actions and alert the relative or care taker of the elderly person in case of emergency. The dataset SisFall with variety of activities of multiple participants is used to calculate features. Machine learning algorithms SVM and decision tree are used to detect the falls on the basis of calculated features. The system acquires accuracy up to 96% by using decision tree algorithm.