Edinburgh Napier University

  In today’s world, we are living in busy metropolitan cities and want our homes to be ambient intelligent enough towards our cognitive requirements for assisted living in smart space environment and an excellent smart home control system should not rely on the users' instructions. Cognitive IoE is a new state-of-art computing paradigm for interconnecting and controlling network objects in context-aware perception-action cycle for our cognitive needs. The interconnected objects (sensors, RFID, network objects etc.) behave as agents to learn, think and adapt situations according to dynamic contextual environment with no or minimum human intervention. One most important recent research problem is “how to recognize inhabitant activity patterns from the observed sensors data”. In this paper, we proposed a two level classification model named as ACM (Ambient Cognition Model) for inhabitant’s activities pattern recognition, using Hidden Markov Model based probabilistic model and subtractive clustering classification method. While subtractive clustering separates similar activity states from non-similar activity states, a HMM works as the top layer to train systems for temporal-sequential activities to learn and predict inhabitant activity pattern proactively. The proposed ACM framework plays a significant role to identify user activity intention in more proactive manner such as routine, location, social activity intentions in smart home scenario. The experimental results have been performed on Matlab simulation to evaluate the efficiency and accuracy of proposed ACM model.