Human fall detection using neuro-fuzzy models based on ensemble learning

Human falling may be due to a violent act, a heart attack or perhaps physical illness. Every year, many old people are being treated for injuries or even die in hospitals which caused by falling. From there, there is a long-standing need for a timely and inexpensive system that automatically identifying a falling person and then alert, which reduces the death rate and increases the likelihood of survival. Due to the low accuracy and a lot of faults in the recognition of the systems released in past years, this paper presented human falling detection by using neuro-fuzzy models and ensemble learning algorithms which presented to solve these problems. This showed the influence of the ensemble learning on performance of neuro-fuzzy models. However, it should be noted that feature selection and extraction methods in processing the dataset have their own impact. In this case, five kinds of feature selection/extraction algorithms are used. Five neuro-fuzzy models are used in this research: normalized radial basis function (NRBF) network, radial basis function (RBF) network, adaptive neuro-fuzzy inference system (ANFIS), local linear model trees (LOLIMOT) and generalized regression neural networks (GRNN). LOLIMOT model, by using correlation based features selection algorithm, reached the highest answer with the accuracy of 0.796. The results of these models are entered into the two ensemble learning algorithms, single majority vote and weighted majority vote, which weighted majority vote algorithm reached an accuracy of 0.87917 by using principal component analysis algorithm, and was the highest answer among of all the models used in this paper.