Authors:
Gopal Jamnal
and
Xiaodong Liu
Affiliation:
Edinburgh Napier University, United Kingdom
Keyword(s):
Intelligent Inhabited Environment, Ambient Intelligent Smart Home, Activity Pattern Recognition, Cognitive IoTs, and Cyber Physical System.
Related
Ontology
Subjects/Areas/Topics:
Data Communication Networking
;
Enterprise Information Systems
;
Internet of Things
;
Sensor Networks
;
Software Agents and Internet Computing
;
Software and Architectures
;
Telecommunications
Abstract:
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 sub
tractive clustering classification method. While subtractive
clustering separates similar activity states from non-similar activity state, 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 play, 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.
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