Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.629277
Title: Identification and prediction of abnormal behaviour activities of daily living in intelligent environments
Author: Mahmoud, S. M.
Awarding Body: Nottingham Trent University
Current Institution: Nottingham Trent University
Date of Award: 2012
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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. Mostpatients would prefer to use non-intrusive technology to help them tomaintain 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 flexible 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.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.629277  DOI: Not available
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