Optimized low computational algorithm for elderly fall detection based on machine learning techniques

Falls are a common and often devastating problem among elder people, causing a tremendous amount of morbidity, mortality. So mechanisms to detect and avoid falls are necessary to improve common living of aged people. By developing preventing mechanisms it serves as an important factor to reduce injuries, hospitalization morbidity and mortality among older people. They are several algorithms established in fall detection (FD) and fall prevention (FP) process using feature extraction and classification algorithms. In this paper a fall detection technique is developed based on the data sets of electromyography (EMG) and electrocardiogram (ECG). The objective of this paper is to detect falls by processing with several feature extraction and classification technique for normal as well as sick people. It also focuses on the improvement of the accuracy in detecting falls. The feature extraction algorithm chosen is capable of sensing, processing and communicating the fall event under real life conditions. A hybrid classification algorithm is also proposed to achieve accuracy in detection by tracking objects and having the ability to handle the causes. The combination of numerical data is used in order to detect fall with high accuracy and reliability. The advantages of this proposed method is less computation complexity and improves efficiency of fall detection compared to existing machine learning algorithms