Prediction of mobility entropy in an ambient intelligent environment

Ambient Intelligent (AmI) technology can be used to help older adults to live longer and independent lives in their own homes. Information collected from AmI environment can be used to detect and understanding human behaviour, allowing personalized care. The behaviour pattern can also be used to detect changes in behaviour and predict future trends, so that preventive action can be taken. However, due to the large number of sensors in the environment, sensor data are often complex and difficult to interpret, especially to capture behaviour trends and to detect changes over the long-term. In this paper, a model to predict the indoor mobility using binary sensors is proposed. The model utilizes weekly routine to predict the future trend. The proposed method is validated using data collected from a real home environment, and the results show that using weekly pattern helps improve indoor mobility prediction. Also, a new measurement, Mobility Entropy (ME), to measure indoor mobility based on entropy concept is proposed. The results indicate ME can be used to distinguish elders with different mobility and to see decline in mobility. The proposed work would allow detection of changes in mobility, and to foresee the future mobility trend if the current behaviour continues

Analysis of security protocols using finite-state machines

This paper demonstrates a comprehensive analysis method using formal methods such as finite-state machine. First, we describe the modified version of our new protocol and briefly explain the encrypt-then-authenticate mechanism, which is regarded as more a secure mechanism than the one used in our protocol. Then, we use a finite-state verification to study the behaviour of each machine created for each phase of the protocol and examine their behaviour s together. Modelling with finite-state machines shows that the modified protocol can function correctly and behave properly even with invalid input or time delay

Modelling and simulation of a biometric identity-based cryptography

Government information is a vital asset that must be kept in a trusted environment and efficiently managed by authorised parties. Even though e-Government provides a number of advantages, it also introduces a range of new security risks. Sharing confidential and top-secret information in a secure manner among government sectors tend to be the main element that government agencies look for. Thus, developing an effective methodology is essential and it is a key factor for e-Government success. The proposed e-Government scheme in this paper is a combination of identity-based encryption and biometric technology. This new scheme can effectively improve the security in authentication systems, which provides a reliable identity with a high degree of assurance. In addition, this paper demonstrates the feasibility of using Finite-state machines as a formal method to analyse the proposed protocols

Behavioural pattern identification and prediction in intelligent environments

In this paper, the application of soft computing techniques in prediction of an occupant's behaviour in an inhabited intelligent environment is addressed. In this research, daily activities of elderly people who live in their own homes suffering from dementia are studied. Occupancy sensors are used to extract the movement patterns of the occupant. The occupancy data is then converted into temporal sequences of activities which are eventually used to predict the occupant behaviour. To build the prediction model, different dynamic recurrent neural networks are investigated. Recurrent neural networks have shown a great ability in finding the temporal relationships of input patterns. The experimental results show that non-linear autoregressive network with exogenous inputs model correctly extracts the long term prediction patterns of the occupant and outperformed the Elman network. The results presented here are validated using data generated from a simulator and real environments

Biometric identity-based cryptography for e-Government environment

Government information is a vital asset that must be kept in a trusted environment and efficiently managed by authorised parties. Even though e-Government provides a number of advantages, it also introduces a range of new security risks. Sharing confidential and top-secret information in a secure manner among government sectors tend to be the main element that government agencies look for. Thus, developing an effective methodology is essential and it is a key factor for e-Government success. The proposed e-Government scheme in this paper is a combination of identity-based encryption and biometric technology. This new scheme can effectively improve the security in authentication systems, which provides a reliable identity with a high degree of assurance. In addition, this paper demonstrates the feasibility of using Finite-state machines as a formal method to analyse the proposed protocols

Activities recognition and worker profiling in the intelligent office environment using a fuzzy finite state machine

Analysis of the office workers’ activities of daily working in an intelligent office environment can be used to optimize energy consumption and also office workers’ comfort. To achieve this end, it is essential to recognise office workers’ activities including short breaks, meetings and non-computer activities to allow an optimum control strategy to be implemented. In this paper, fuzzy finite state machines are used to model an office worker’s behaviour. The model will incorporate sensory data collected from the environment as the input and some pre-defined fuzzy states are used to develop the model. Experimental results are presented to illustrate the effectiveness of this approach. The activity models of different individual workers as inferred from the sensory devices can be distinguished. However, further investigation is required to create a more complete model

Single-occupancy simulator for ambient intelligent environment

In this paper, the simulation of an occupant’s behaviour in a single-occupant ambient intelligent environment is addressed. The algorithm of the simulator is designed flexible enough to accept different environmental profiles including the number of areas and the connections between them along with different occupant’s profiles including expected daily occupancy pattern of him/her and the uncertainty of his/her behaviour to follow this occupancy pattern. The generated occupancy signal by the simulator represents the occupancy of areas by assuming a signal level for the occupancy of each area in a single-occupant environment with the resolution of one minute in a whole day activity of the occupant in the environment. The validity of the simulator will be verified by tuning the simulator’s parameters to occupancy data collected by sensory agents from a real equivalent environment. By applying the generated data from this simulator to the data mining techniques, the ability of different techniques will be investigated

Semantic-based decision support for remote care of dementia patients

This paper investigates the challenges in developing a semantic-based Dementia Care Decision Support System based on the non-intrusive monitoring of the patient's behaviour. Semantic-based approaches are well suited for modelling context-aware scenarios similar to Dementia care systems, where the patient's dynamic behaviour observations (occupants movement, equipment use) need to be analysed against the semantic knowledge about the patient's condition (illness history, medical advice, known symptoms) in an integrated knowledgebase. However, our research findings establish that the ability of semantic technologies to reason upon the complex interrelated events emanating from the behaviour monitoring sensors to infer knowledge assisting medical advice represents a major challenge. We attempt to address this problem by introducing a new approach that relies on propositional calculus modelling to segregate complex events that are amenable for semantic reasoning from events that require pre-processing outside the semantic engine before they can be reasoned upon. The event pre-processing activity also controls the timing of triggering the reasoning process in order to further improve the efficiency of the inference process. Using regression analysis, we evaluate the response-time as the number of monitored patients increases and conclude that the incurred overhead on the response time of the prototype decision support systems remains tolerable

Echo state network for occupancy prediction and pattern mining in intelligent environment

Pattern analysis and prediction of sensory data is becoming an increasing scientific challenge and a massive economical interest supports the need for better pattern mining techniques. The aim of this paper is to investigate efficient mining of useful information from a sensor network representing an ambient intelligence environment. The goal is to extract and predict behavioral patterns of a person in his/her daily activities by analyzing the time series data representing the behaviour of the occupant, generated using occupancy sensors. There are various techniques available for analysis and prediction of a continuous time series signal. However, the occupancy signal is represented by a binary time series where only discrete values of a signal are available. To build the prediction model, recurrent neural networks are investigated. They are proven to be useful tools to solve the difficulties of the temporal relationships of inputs between observations at different time steps, by maintaining internal states that have memory. In this paper, a special form of recurrent neural network, the so-called Echo State Network (ESN) is used in which discrete values of time series can be well processed. Then, a model developed based on ESN is compared with the most popular recurrent neural net-works; namely Back Propagation Through Time (BPTT) and Real Time Recurrent Learning (RTRL). The results showed that ESN provides better prediction results compared with BPTT and RTRL. Using ESN, large datasets are learnt in only few minutes or even seconds. It can be concluded that ESN are efficient and valuable tools in binary time series prediction. The results presented in this paper are based on simulated data generated from a simulator representing a person in a 1 bedroom flat

User activities outliers detection; integration of statistical and computational intelligence techniques

In this paper, a hybrid technique for user activities outliers detection is introduced. The hybrid technique consists of a two-stage integration of Principal Component Analysis (PCA) and Fuzzy Rule-Based Systems (FRBS). In the first stage, the Hamming distance is used to measure the differences between different activities. PCA is then applied to the distance measures to find two indices of Hotelling's T2 and Squared Prediction Error. In the second stage of the process, the calculated indices are provided as inputs to the FRBSs to model them heuristically. The model is used to identify the outliers and classify them. The proposed system is tested in real home environments, equipped with appropriate sensory devices, to identify outliers in the activities of daily living of the user. Three case studies are reported to demonstrate the effectiveness of the proposed system. The proposed system successfully identifies the outliers in activities distinguishing between the normal and abnormal behavioural patterns