Recognizing User Context Using Mobile Handsets with Acceleration Sensors

Abstract

User context recognition is one of the important technologies for realizing context aware services. Conventional multi sensor based approach has advantages in that it can generate variety of contexts with less computation resources by using many different sensors. However, such systems tend to be complex and cumbersome and, thus, do not fit in well with mobile environment. In this sense, a single sensor based approach is suitable for mobile environments. In this paper, we show a context inference scheme that realizes a user posture inference with only one acceleration sensor embedded in a mobile handset. Our system automatically detects the sensor position on the user's body and selects the most relevant inference method dynamically. Our experimental results show that the system can infer a user's posture (sitting, standing, walking, and running) with an accuracy of more than 96%.