These sensors are used to recordthe activities representing the behaviour of the occupant,
and allow the caregiver to observe any changes to patterns.
It can be concluded that using large number of hidden
neurons in ESN yielded a good results in terms of the error
and time required for training and testing.
Based on the results shown here, it appears that a home
equipped with some low-level sensors can provide important
information about the status of the occupant. The
proposed approach works better for elderly residents when
more routine activities are expected. We cannot suggest
whether this approach would work for a young or more
active occupant.
Our future work aims at multiple occupancy and prediction
of abnormal behaviour. The approach presented in
this paper would not be effective in the presence of visitors
or even when the elderly people have a pet which is true for
some cases. We are also aiming to continue our research in
semantic modelling of the behaviour where the predicted
values are communicated with the elderly and carer in
linguistic terms. For the work presented in this paper, only
a limited number of discrete sensors were used. However,
more research is required when a combination of discrete
sensors (occupancy, door entry point,…) and continuous
sensors (temperature, humidity,…) are used.