The Understand Of User Based On Sensors Build-in Smart Phones

With the coming of Mobile-Internet and rapid developing of related technologies, the use of mobile phones, which are the representatives of the mobile terminals was more widely, its portability and ease of use makes it gradually integrate into people’s daily life. With the advances in technology, mobile phones were no longer just a simple communication tool, what users need is a more intelligent, more humane equipment, in such conditions, the smart phone’s built-in sensors have emerged, such as accelerometers for screen switch, gyro support stabilization for camera, GPS and electronic compass for positioning and navigation, the use of these sensors can not only provide users with a better experience, but also bring out the emerging of large numbers of applications and systems social networking, health monitoring, route planning and traffic management. This article start the work based on sensors and carry out as follows:(1) We contrast sensors support of three mainly smartphone operation system, at the same time, we also compare the background running and power saving mechanism. And then we develop a user-sensor data acquisition system named LiveData based on Android platform, and the power consumption problem which users are most concerned about is studied and improved.(2) We do some detail analyses of LiveData; first we point out that some data were missing and find out the reason, then we propose a repair algorithm for missing data. Then we introduce the Timeline based on LiveData, and point out the problems to be addressed, such as the identification of the user motion state.(3) We also use the sensor data to understand the behavior of the user; first, we use acceleration sensor data to identify the location of mobile phone and draw a conclusion:acceleration sensor can probably reflect different characteristics of the motion of the placement. Then gyro data are added to characterize the movement more fully, and improve the accuracy of recognition. Finally, we use the acceleration sensor and GPS data to identify the user’s state, successfully identified walking, cycling and taking bus.