Improvement of Fuzzy Neural Network Based Human Activity Estimation System
Manabu Nii, Takuya Iwamoto, Yuichi Ishibashi, Daiki Komori · 2015
In our past works, a standard three-layer feed forward neural network based human activity estimation method has been proposed. The proposed method aims to record the subject activity automatically. The recorded data by MEMS based monitoring devices include raw accelerometer data of his/her activity. From these data, we need to determine what the subject person was doing. In our conventional methods, some numerical datasets of accelerometer which are measured for every subject person were needed to train neural networks. In this paper, we propose an estimation method of subject behavior using fuzzy neural networks. The proposed fuzzy neural network based method can be trained by using fuzzy if-then rules which represent action primitives instead of numerical datasets from subject person.