Occupancy monitoring and prediction in ambient intelligent environment

Occupancy monitoring and prediction as an influential factor in the extraction of occupants' behavioural patterns for the realisation of ambient intelligent environments is addressed in this research. The proposed occupancy monitoring technique uses occupancy detection sensors with unobtrusive features to monitor occupancy in the environment. Initially the occupancy detection is conducted for a purely single-occupant environment. Then, it is extended to the multipleoccupant environment and associated problems are investigated. Along with the occupancy monitoring, it is aimed to supply prediction techniques with a suitable occupancy signal as the input which can enhance efforts in developing ambient intelligent environments. By predicting the occupancy pattern of monitored occupants, safety, security, the convenience of occupants, and energy saving can be improved. Elderly care and supporting people with health problems like dementia and Alzheimer disease are amongst the applications of such an environment. In the research, environments are considered in different scenarios based on the complexity of the problem including single-occupant and multiple-occupant scenarios. Using simple sensory devices instead of visual equipment without any impact on privacy and her/his normal daily activity, an occupant is monitored in a living or working environment in the single-occupant scenario. ZigBee wireless communication technology is used to collect signals from sensory devices such as motion detection sensors and door contact sensors. All these technologies together including sensors, wireless communication, and tagging are integrated as a wireless sensory agent

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

Occupant behaviour prediction in ambient intelligence computing environment

In this paper, the application of ambient intelligence computing techniques in the prediction of occupant behaviours is addressed. It is aimed to deliver a wellbeing monitoring and assistive environment to support elderly lives independently, in control of their day to day activities. A wireless sensor network is constructed to collect the required occupancy data. Individual sensory data are combined to form an occupancy time series. In this paper different techniques in time series prediction are investigated. The prediction techniques include an Evolving Fuzzy Predictor (EFP) model along with Auto Regressive Moving Average (ARMA) model, Adaptive-Network-based Fuzzy Inference System (ANFIS), as well as Transductive Neuro-Fuzzy Inference model with Weighted data normalization (TWNFI). These prediction techniques are used to predict the occupancy time series representing anticipated occupancy of different areas of the environment, and the results are compared. Experimental results are presented based on a home environment with four separate areas and each area is equipped with a wireless passive infrared motion detector linked to a central processing unit. For wireless communication of the sensor network, ZigBee wireless modules are employed in the prototype ambient intelligence environment