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Behaviour recognition in smart homes : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Manawatu, New Zealand
In the context of this thesis, behaviour recognition aims to infer the particular
behaviours of the inhabitant of a smart home from a series of sensor readings from
around the house. This thesis views the behaviour recognition problem as a task
of mapping the sensory outputs to a sequence of activities performed by the inhabitant.
The main focus is the development of machine learning methods to find an
approximation to the mapping between sensor outputs and behaviours.
While there have been many supervised machine learning methods for identifying
behaviours from a sensor stream, they generally assume that the behaviours are either
segmented or perform segmentation and behaviour recognition separately. In order
to be used in the real world, segmentation and behaviour recognition should not
be treated separately. This thesis addresses this problem based on a set of hidden
Markov models (HMM) and a variable window length.
As the majority of the methods reported in the literature are based on supervised
learning approach, they generally rely on a labelled dataset, where the behaviours of
the inhabitant have to be manually labelled. This is often not practical in the real
world. Most current unsupervised methods are not suitable for behaviour recognition
as they are based on inputs of xed dimensionality. In the smart home, the behaviours
that are to be recognised are variable in length. This thesis introduces an unsupervised
learning method that addresses this problem, which is based on compression and the
edit distance between words. This includes both the segmentation of the sensor
stream into suitable patterns and identi cation of patterns that correspond to human
behaviours. This thesis also shows that the resulting method can be used to provide
labels to training data for a supervised method.
However, training a learning algorithm on sensors that are irrelevant and/or
redundant becomes crucial as they may a ect the recognition performance. This
thesis addresses the sensor selection problem for behaviour recognition through an information-theoretic approach, which is based on information gain, modelled in the
form of a decision tree. The main idea is to identify the set of informative sensors
that are highly correlated with the behaviours. This thesis also presents solutions
to address the `generalisation' issues of the informative sensors identified across the
To evaluate the e ectiveness of our proposed methods, we use a real smart home
dataset obtained from the MIT PlaceLab and compare the labels produced by our
methods with the labels assigned by a human to the activities in the sensor stream.
We also validated our methods on other benchmark datasets and learning algorithms.