Fuzzified neural network based human condition monitoring using a small flexible monitoring device
Manabu Nii, Yoshihiro Kakiuchi, Kazunobu Takahama, Takafumi Matsuda, Yuki Matsumoto, Kazusuke Maenaka · 2014
For maintaining our daily healthcare, we need to understand our own physical condition. In understanding such data, additional information such as what the subject is doing at that time is needed. For example, let us assume that we have a record of a certain heart rate 90. If such value is observed when the subject person was sleeping, that value is high and the subject may have some trouble on his/her health. On the other hand, when the subject was running, the subject has no problem on his/her health. In this paper, we propose a combined system for maintaining our healthcare. Our proposed system consists of both systems; (1) a fuzzified neural network based unusual condition detection and (2) a standard neural network based action estimation. The proposed system can handle multiple kinds of sensors' data. In this paper, the following three kinds of sensors were handled; (1) three-axis acceleration data, (2) heart rate, and (3) breathing rate. From experimental results, the effectiveness of our proposed system is shown for understanding our conditions.