Using Contextual Information for Recognising Human Behaviour

Using Contextual Information for Recognising Human Behaviour

Hans W. Guesgen (School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand) and Stephen Marsland (School of Engineering and Advanced Technology, Massey University, Palmerston North, New Zealand)
Copyright: © 2016 |Pages: 18
DOI: 10.4018/IJACI.2016010102
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Abstract

Identifying human behaviours in smart homes from sensor observations is an important research problem. The addition of contextual information about environmental circumstances and prior activities, as well as spatial and temporal data, can assist in both recognising particular behaviours and detecting abnormalities in these behaviours. In this paper, the authors describe a novel method of representing this data and discuss a wide variety of possible implementation strategies.
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An Ai Perspective On The Smart Home

The smart home can be separated into the sensory system and the ambient intelligence that works to interpret the sensor observations. In this paper, we do not explicitly consider what types of sensor are (or could be) available in the home, but assume that the sensory stream is available in the form of a sequence of ‘tokens’, i.e., there has been some preprocessing of the sensor readings (and possibly the fusion of different sensor data) into a sequence of observations that can be used by some form of ambient intelligence system. For our purposes, we consider that the task of the smart home is to segment this token stream into different behaviours, and to identify whether or not this behaviour is typical of the user and, if not, whether it is sufficiently abnormal to warrant any action (such as calling a carer, or interacting with the inhabitant, for example in the form of reminders: ‘did you remember to turn the gas off?’).

Our interpretation of the smart home problem from the point of view of artificial intelligence is that it requires solutions to at least some of the following problems:

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