A neural network agent based approach to activity detection in AmI environments
Fernando Rivera-Illingworth · 2005
Many countries are facing the problem of caring economically for their ageing population. One approach to this is the development of environments which possess ambient intelligence that will provide care for older people while assisting them with their everyday life activities. One function that is required in care provision is spotting abnormal behaviours as this might be an indicator of a problem requiring attention from a carer. Agent technology can be employed to detect abnormalities by first learning a normal set of personal behaviours and then detecting deviations from these. This paper presents a novel connectionist embedded agent architecture that combines the use of unobtrusive and relatively simple sensors and employs a constructive algorithm with temporal capabilities which is able to recognize different high level activities (such as “sleeping”, “working at computer”, “eating”), and identify abnormal behaviours. The network is trained in an online mode, and is able to adapt and expand itself as new data are made available over time or it can add new output nodes to represent new classes or accommodate the abnormal instances. The developed connectionist approach is not computationally demanding and hence it can be integrated into the limited processor-power embedded computing platforms used in intelligent domestic environments.