Identification and Prediction of
Abnormal Behaviour Activities
of Daily Living in Intelligent
Environments
By
SAWSAN MOUSA MAHMOUD
A thesis submitted in partial fulfilment of the requirements of
Nottingham Trent University for the degree of
Doctor of Philosophy
May 2012
Abstract
The aim of this research is to investigate efficient mining of useful information from a
sensor network forming an Ambient Intelligence (AmI) environment. In this thesis, we
investigate methods for supporting independent living of the elderly (and specifically
patients who are suffering from dementia) by means of equipping their home
with a simple sensor network to monitor their behaviour and identify their Activities of
Daily Living (ADL). Dementia is considered to be one of the most important causes of
disability in the elderly. Most patients would prefer to use non-intrusive technology to
help them to maintain their independence. Such monitoring and prediction would allow
the caregiver to see any trend in the behaviour of the elderly person and to be informed of
any abnormal behaviour.
Employing a sensor network system allows us to extract daily behavioural patterns of the
occupant in an Intelligent Inhabited Environment (IIE). This information is then used to
build a behavioural model of the occupant which ultimately is applied to predict the
future values representing the expected occupancy in the monitored environment.
Challenges of employing wired and wireless sensor network have been widely
researched. However, pattern analysis and prediction of sensory data is becoming an
increasing scientific challenge and this research investigates appropriate means of pattern
mining and prediction within the IIE.
Door entry and occupancy sensors are used to extract the movement patterns of the
occupant. These sensors produce long sequences of data as binary time series, indicating
presence or absence of the occupant in different areas. It is essential to convert these
binary series into a more fexible and efficient format before they are processed
for any further analysis and prediction. Different ways of representing and visualizing the
large sensor data sets in a format suitable for predicting and identifying the behaviour
patterns are investigated.
A two-stage integration of Principal Component Analysis (PCA) and Fuzzy Rule-Based
System (FRBS) is proposed to identify important information regarding outliers or
abnormal behaviours in ADLs. In the first stage, binary dissimilarities or distance
measures are used to measure the distances between the activities. PCA is then applied to
find two indices of Hotelling's T2 and Squared Prediction Error (SPE).
In the second stage of the process, the calculated indices are provided as inputs to FRBSs
to model them heuristically. They are used to identify outliers and classify them. The
proposed system identifies user activities and helps in distinguishing between the normal
and abnormal behavioural patterns of the ADLs.
Data provided for this investigation was from real environments and from a previously
developed simulator. The simulator was modified to include trending behaviour in the
activities of daily living. Therefore, in the occupancy signal generated by the simulator,
both seasonality and trend are included in occupant's movements. Prediction models
are built through Recurrent Neural Networks (RNN) after converting the occupancy
binary time series. RNN have shown a great ability in finding the temporal relationships
of input patterns. In this thesis, RNN are compared to evaluate their abilities to accurately
predict the behaviour patterns. The experimental results show that Echo
State Network (ESN) and Non-linear Autoregressive netwoRk with eXogenous (NARX)
inputs correctly extract the long term prediction patterns of the occupant and
outperformed the classical Elman network.