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This paper presents a novel approach for automatic detection of abnormal behaviours in daily routine of people living alone in their homes, without any manual labelling of the training dataset. Regularity and frequency of activities are monitored by estimating the status of specific electrical appliances via their power signatures identified from the composite power signal of the house. A novel unsupervised...
In this paper, an approach is presented for the detection of abnormal behaviours in the activities of people living alone in their homes. The proposed approach takes input from Kinect cameras and extracts a number of visual attributes that represent the occupant's location and orientation. A set of fuzzy logic parameters is first learnt from the training data. Next the proposed approach learns epochs...
The increasing elderly population presents a challenge on the resources of carers and assisted living communities. In this paper, we present an algorithm based around the Microsoft Kinect for monitoring activities of daily living. The system analyses the behaviour of occupants to provide carers with valuable observational data, and has the capacity to detect abnormal events in the home.
Australia is geographically large and sparsely populated. Thus, its population is subject to inequalities in healthcare based on geographic location. This is particularly true for the management of pregnancies at risk, which require regular monitoring. We discuss a solution in the form of a framework based on inexpensive monitoring devices coupled with a mobile phone and PC systems to enable enhanced...
It has been shown that the rate of preterm birth in remote and rural populations in Australia is more than twice of those in the metropolitan population, and that increased access to quality antenatal care in such communities leads to a significant improvement. This paper describes a system that supports remote fetal heart rate monitoring, thus providing a technique that addresses this need for better...
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