Fall Detection for Elderly People Using Machine Learning

Abstract

Abstract: Falling down is among the most common causes of medical attention required by the elderly people . Elderly people often injure themselves from falling down more especially when they are living alone. After a fall occurs, medical attention needs to be provided promptly in order to reduce the risk of victim . Several technologies have been developed which utilize webcams to monitor the activities of elderly people. However, the cost of operation and installation is expensive and only applicable for. Fall is one the major cause of death for older people. Detecting the fall plays a major role in saving lives. There are three different types of fall detection commonly used , such as wearable, ambient sensor and vision-based methods.If elderly people falls then it will put severe effect on their health and technology is helping humans in every aspect of their life and in this paper author is using machine learning algorithm to predict FALL scenarios by analysing their movements. In propose paper author has used SVM and Decision Tree algorithms to train SISFALL dataset and this trained model can be used to predict fall scenarios from new test data. Sensors will be embedded with elderly people’s body and this sensor will record their movement such as Heart Rate, EEG and circulation and then give this input to ML model and ML model will predict current scenario or posture and alert to elderly people