We propose a method to detect the model, location and activity of a conventional home electric appliance. Waveforms of current consumed by appliances vary according to their configurations and activity. We define feature parameters for detecting the status of appliances. A current detector, microcomputer and transmitter are equipped in a power outlet in order to measure consumed current, calculate the feature parameters, and transmit the results to a home server. Feature parameters of appliances in the home are learned and stored in a home server in advance. The home server compares the feature parameters of known appliances with the received feature parameters to detect an appliance's model and activity. User could control appliances from out of the house via the Internet.

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Flick input is usual input method on touch panels of smart phones. Inputted characters are decided by a touched place on the screen and by a moving direction from the place. There is a research in which users are identified with characteristics of flick input of them. However, the characteristics are not stable since users usually change their postures when they hold their smart phones. The accuracy of personal identification depends on postures of users in the research. Therefore, in this paper, we propose a new method which increases the accuracy of personal identification by obtaining postures of users from acceleration sensors on smart phones.