Aims/ objectives: To interpret the trends of Activities of Daily Living (ADL) and Activities of Daily Working (ADW) of people who are occupying Ambient Intelligence (AmI) environments and predict the next activities’ time values. This research has two main contributions; A novel proposed technique called Activity Prediction Moving Average (APMA) based on Exponentially Weighted Moving Average (EWMA) and propose a new framework to be used in our research based on the Adaptive-Network based Fuzzy Inference System (ANFIS).Study Design: Cross-sectional study.Place and Duration of Study: Department of Computer science, Institute of Science and Technology, between August 2018 and November 2018.Methodology: Three datasets are included in this research of people who are occupying smart environments. These datasets are examined using APMA and ANFIS techniques.Results: The results of the applied techniques show a good indicator of using them in human behaviour forecasting.Conclusion: we investigated prediction techniques that can be applied to the human behaviours’ data. The proposed solutions demonstrate the feasibility of interpreting this kind of data. These techniques will support the supervisor to get clear information about the situation of the participant who occupying a smart environment.

Activities of daily living (ADL) or activities of daily working (ADW) may be affected by changes in a person's health or well-being. Measuring progressive changes in one activity or multiple activities is representative of behavioural variations. By inspecting the trends in multiple activities, it is possible to identify and predict human behavioural changes. We refer to the trends in people's behaviour as behavioural evolution. In this paper, we propose a novel indicator to measure the progressive changes representing a participant's behavioural evolution. The proposed indicator presents activities as a holistic measure, which first combine multi-activities and then measure the progressive changes in the combined activities for each single day. Real data sets were collected from a wireless sensor network and used to examine our proposed technique. As part of this process, we were able to quantify progressive changes for individual and aggregated activities. Our experimental results demonstrated that: (1) the proposed approach can identify and distinguish normal and abnormal behaviours; (2) large data sets gathered from sensors in an intelligent environment represented in various time series can be visualised in a simple and more understandable format; (3) identifying trends in ADLs or ADWs is a relevant means of sharing information with carers or supervisors. (C) 2018 Elsevier Ltd. All rights reserved.

Analysis of human behaviour changes is a subject of interest for many researchers. This could be obtained considering either short-term or long-term changes. The aim of this study is to find long-term changes (behaviour evolution) in Activities of Daily Living (ADL) or Activities of Daily Working (ADW) of users in an Ambient Intelligence (AmI) environment. Analysis is based on introduction of a novel Human Behaviour Momentum Indicator (HBMI). Extensive experiments are conducted to investigate the effectiveness of the studied techniques on real-world datasets collected from home and office environments. To show the effectiveness of the proposed approach, results are compared with Relative Strength Index (RSI). The results show that trends in ADL or ADW can be detected and the direction of the activity's trend are predicted. In addition, the results show that our proposed technique gives a better response to changes in data more than the other technique.

Analysing changes of the behaviour of an occupant who lives in an Ambient Intelligence (AmI) environment is addressed in this paper. Changes in Activities of Daily Living (ADL) are indicators of the social and health status of the occupant. This research therefore aims to identify trends in ADL and interpret them in a suitable form for carers. It is essential for this purpose to have access to relatively long-term monitoring data of the occupant using appropriate sensory devices. Different trend analysis techniques are investigated and compared. These techniques include; Seasonal Kendall Test (SKT), Simple Moving Mean Average (SMA), and Exponentially Weighted Moving Average (EWMA), which are used to detect trends in the time-series data representing occupancy duration in different areas of a home environment for an elderly person living independently.

The availability of datasets for monitoring the activities of daily living is limited by difficulties associated with the collection of such data. There have been many suggested software solutions to overcome this issue. In this paper, a new technique to generate realistic data is proposed. The new method provides virtual data to the researchers with the ability to rapidly generate a large simulated dataset with different factors that could be used to represent different behaviour of a user. This paper describes the use of Hidden Markov Model (HMM) and Direct Simulation Monte Carlo (DSMC) to generate data for Activities of Daily Living (ADL) representing an older adult's behaviour. The combination of HMM and DSMC facilitates the generation of datasets capturing behaviour in terms of occupancy and movement activity performance in the environment. Simulated data is validated against data collected from a real environment.

Human behaviour can be difficult to interpret even with the sophistication of modern smart homes, yet an understanding of the way people conduct their activities of daily living is essential for any attempts to detect problems. We discuss the key indicators for various activities that can be relatively robustly measured, and how these indicators can lead to a holistic measure for the activity. Combining indicators to give a metric for an activity evolution such as sleep can assist in extracting trends which may indicate some change in well-being.

This paper highlights the results of applied techniques, which optimize the design of a pressure vessel for hollow cylinder and hemi-spherical head in terms of the cost of the material and manufacturing. The optimise design problem is tackled in two different stages by creating two different models. The first model will use the simulated annealing technique and the second model uses the tabu search technique. Both of them will use the same minimise cost function equation and the variable vector of this function. That will gain results which could be used in a comparative study for the applied techniques. The study will explain the best solution of these techniques in such work like this. There are two C++ programs were done for the techniques as well.