Recently, various types of wireless sensor network systems have been developed to realize daily care for elderly people living alone. Furthermore, visualization methods of life logs have been presented. However, it is important to integrate different types of data measured by each sensor node to estimate human states and behaviors. Therefore, we have proposed the concept of informationally structured space (ISS). This paper proposes a methodology to deal with data measured by sensor nodes in wireless sensor networks on ISS. First, we explain how to use ISS for wireless sensor networks. Next, we apply the proposed method to elderly care. We propose four different components such as (1) human localization by spiking neurons, (2) human movement transition probability, (3) redundant monitoring by simultaneous firing of sensor nodes, and (4) temporal life pattern extraction by Gaussian membership functions. Finally, we show several simulation results and discuss the effectiveness of the proposed method.