Extraction of Daily Life Log Measured by Smart Phone Sensors Using Neural Computing

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.