Human Fall Detection Using Machine Learning Methods: A Survey

International Journal of Mathematical, Engineering and Management Sciences

Komal Singh¹  Sachin Sharma¹  Akshay Rajput¹ 

[1] Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India;

关键词: hidden markov model;    gaussian distribution;    multilayer perceptron;    fuzzy rule;    deep learning.;   

DOI  :  10.33889/IJMEMS.2020.5.1.014

来源: DOAJ

【 摘 要 】

Human fall due to an accident can cause heavy injuries which may lead to a major medical issue for elderly people. With the introduction of new advanced technologies in the healthcare sector, an alarm system can be developed to detect a human fall. This paper summarizes various human fall detection methods and techniques, through observing people’s daily routine activities. A human fall detection system can be designed using one of these technologies: wearable based device, context-aware based and vision based methods. In this paper, we discuss different machine learning models designed to detect human fall using these techniques. These models have already been designed to discriminate fall from activities of daily living (ADL) like walking, moving, sitting, standing, lying and bending. This paper is aimed at analyzing the effectiveness of these machine learning algorithms for the detection of human fall.

【 授权许可】

Unknown