This paper deals with the information extraction of daily life log measured by smart phone sensors. Two types of neural computing are applied for estimating the human activities based on the time series of the measured data. Acceleration, angular velocity, and movement distance are measured by the smart phone sensors and stored as the entries of the daily life log together with the activity information and timestamp. First, growing neural gas performs clustering on the data. Then, spiking neural network is applied to estimate the activity. Experiments are performed for verifying the effectiveness of the proposed method. © 2013 The Authors.

Recently, the rate of elderly people rises in the super-aging society. Human-friendly robots can be used to support the mental and physical care for elderly people and to assist the care of caregivers to elderly people. Robotic conversation can activate the brain of such elderly people and improve their concentration and memory abilities. However, it is difficult for a robot to converse appropriately with a person even if many contents of the conversation are designed in advance because the performance of voice recognition is not enough in the daily conversation. Recognition of human gestures is also important in order to perform smooth communication. This paper deals with human gestures recognition using spiking neural network and classification learning. The proposed method is able to handle the cultural differences in the human communication. © 2012 IEEE.