Elderly Assistance Using Wearable Sensors by Detecting Fall and Recognizing Fall Patterns

Falling is a serious threat to the elderly people. One severe fall can cause hazardous problems like bone fracture or may lead to some permanent disability or even death. Thus, it has become the need of the hour to continuously monitor the activities of the elderly people so that in case of fall incident they may get rescued timely. For this purpose, many fall monitoring systems have been proposed for the ubiquitous personal assistance of the elderly people but most of those systems focus on the detection of fall incident only. However, if a fall monitoring system is made capable of recognizing the way in which the fall occurs, it can better assist people in preventing or reducing future falls. Therefore, in this study, we proposed a fall monitoring system that not only detects a fall but also recognizes the pattern of the fall for elderly assistance using supervised machine learning. The proposed system effectively distinguishes between falling and non-falling activities to recognize the fall pattern with a high accuracy.