Routine Learning: from Reactive to Proactive Environments

• Main Author: Pirttikangas, S. (Susanna)
• Format: Doctoral Thesis
• Language: English
• Published: University of Oulu 2004
• Subjects: context-recognition; data mining; human-computer interaction; pattern recognition; pervasive computing; proactive computing; ubiquitous computing
• Online Access: http://urn.fi/urn:isbn:9514275659; http://nbn-resolving.de/urn:isbn:9514275659

Abstract Technological development and various information services becoming common has had the effect that data from everyday situations is available. Utilizing this technology and the data it produces in an efficient manner is called context-aware or ubiquitous computing. The research includes the specifications of each application, the requirements of the communication systems, issues of privacy, and human - computer interaction, for example. The environment should learn from the user's behaviour and communicate with the user. The communication should not be only reactive, but proactive as well. This thesis is divided into two parts, both representing methodology for enabling intelligence in our everyday surroundings. In part one, three different applications are defined for studying context-recognition and routine learning: a health monitoring system, a context-aware health club application, and automatic device configuration in an office space. The path for routine learning is straight forward and it is closely related to pattern recognition research. Sensory data is collected from users in various different situations, the signals are pre-processed, and the contexts recognized from this sensory data. Furthermore, routine learning is realized through association rules. The routine learning paradigm developed here can utilize already recognized contexts despite their meaning in the real world. The user makes the final decision on whether the routine is important or not, and has authority over every action of the system. The second part of the thesis is built on experiments on identifying a person walking on a pressure-sensitive floor. Resolving the characteristics of the special sensor producing the measurements, which lies under the normal flooring, is one of the tasks of this research. The identification is tested with Hidden Markov models and Learning Vector Quantization. The methodology developed in this thesis offers a step along the long road towards functional and calm intelligent environments.