Name
Abstract | ||
|---|---|---|
We proposed a standard three-layer feedforward neural network based human activity estimation method. The purpose of the proposed method is to record the subject activity automatically. Here, the recorded activity includes not only actual accelerometer data but also rough description of his/her activity. In order to train the neural networks, we needed to prepare numerical datasets of accelerometer which are measured for every subject person. In this paper, we propose a fuzzy neural network based method for recording the subject activity. The proposed fuzzy neural network can handle both real and fuzzy numbers as inputs and outputs. Since the proposed method can handle fuzzy numbers, the training dataset can contain some general rules, for example, “If x and y axis accelerometer outputs are almost zero and z axis accelerometer output is equal to acceleration of gravity then the subject person is standing.” |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/RIISS.2014.7009174 | Robotic Intelligence In Informationally Structured Space |
Keywords | DocType | Citations |
accelerometers,data analysis,estimation theory,feedforward neural nets,fuzzy neural nets,accelerometer data,feedforward neural network,fuzzy neural network,human activity estimation,human daily activity | Conference | 0 |
PageRank | References | Authors |
0.34 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manabu Nii | 1 | 20 | 3.75 |
| Takahama, K. | 2 | 0 | 0.34 |
| Takeshi Iwamoto | 3 | 68 | 10.66 |
| Tetsuya Matsuda | 4 | 26 | 8.88 |