Komal Singh
Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.
Akshay Rajput
Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.
Sachin Sharma
Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India.
DOI https://doi.org/10.33889/IJMEMS.2020.5.1.014
Abstract
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.
Keywords- Hidden Markov model, Gaussian distribution, Multilayer perceptron, Fuzzy rule, Deep learning.
Citation
Singh, K., Rajput, A., & Sharma, S. (2020). Human Fall Detection Using Machine Learning Methods: A Survey. International Journal of Mathematical, Engineering and Management Sciences, 5(1), 161-180. https://doi.org/10.33889/IJMEMS.2020.5.1.014.