Performance Analysis of Various Machine Learning Algorithm for Fall Detection A Survey
Improper activities appear nowadays for the human (i.e. falling without aware, and numerous techniques had been developed to reduce them. In this paper, critically analysis of the various proposed methodologies by comparing their strength and their weakness. Due to the diverseness and complexness action recognition become very challenge. The traditional (CCTV) system for monitoring patient activity it is inefficient and costly, then by using sensor based is also difficult due to drained battery life. So we need real time system for activity recognition with more efficiency and accuracy to avoid people from morality problems or it may lead to causes to major injuries. By comparing various algorithm Support vector machine (SVM) is a discriminative classifier belonging to supervised learning. Recurrent neural network (RNN) is one of the concepts in deep neural network. The main intention of the RNN is to minimize the preprocessing. This application will seem like visual imagery analysis. The convolutional neural network (CNN) it is more cost expensive compared wearable and ambience based. Diffusion Convolutional Neural Network (DCRNN) is the branch of artificial intelligence. The neural network was trained by DCRNN algorithm. The objective of the discussion is to be able to implement an automated anomalous human activity recognition system by using Diffusion Convolutional Recurrent Neural network and compression of the video in real times it proves to be more effective than other classification algorithm.
2020 International Conference on System, Computation, Automation and Networking (ICSCAN)