Extracting personal user context with a three-axis sensor mounted on a freely carried cell phone

To realize ubiquitous services such as presence services and health care services, we propose an algorithm to extract "personal user context" such as user's behavior; it processes information gathered by a three-axis accelerometer mounted on a cell phone. Our algorithm has two main functions; one is to extract feature vectors by analyzing sensor data in detail by wavelet packet decomposition. The other is to flexibly cluster personal user context by combining a self-organizing algorithm with Bayesian theory. A prototype that implements the algorithm is constructed. Experiments on the prototype show that the algorithm can identify personal user contexts such as walking, running, going up/down stairs, and walking fast with an accuracy of about 88[%].