Abstract
In an Assistive Eenvironment (AE), explicit/obtrusive interfaces for human/computer interaction can demand exclusive user attention and, often, replacement of them with implicit ones embedded into real-world artifacts for intuitive and unobtrusive use is desirable. As a part of solution, Context Aware can be utilized to recognize current context situation from a combination of low-level sensed contexts. Assuming the current context recognized, this paper tackles the next logical step of "the prediction of future contexts". This information allows the system to know patterns and their interrelations in user behaviour, which are not apparent at the lower levels of raw sensor data. The present paper analyzes prerequisites for user-centred prediction of future context and presents an algorithm for autonomous context recognition and prediction, based on our proposed Fuzzy-State Q- Learning technique as well as on some established methods for data-based prediction.
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Feki, M.A., Lee, S.W., Bien, Z., Mokhtari, M. (2007). Context Aware Life Pattern Prediction Using Fuzzy-State Q-Learning. In: Okadome, T., Yamazaki, T., Makhtari, M. (eds) Pervasive Computing for Quality of Life Enhancement. ICOST 2007. Lecture Notes in Computer Science, vol 4541. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73035-4_20
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DOI: https://doi.org/10.1007/978-3-540-73035-4_20
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