Behaviour recognition aims to infer the particular behaviours of the inhabitant in a smart home from a series of sensor

readings from around the house.  There are many reasons to recognise human behaviours; one being to monitor the el-

derly or cognitively impaired and detect potentially danger-ous behaviours. We view the behaviour recognition problem

as the task of mapping the sensory outputs to a sequence of recognised activities. This research focuses on the develop-

ment of machine learning methods to find an approximation to the mapping between sensor outputs and behaviours. How-

ever, learning the mapping raises an important issue, which is that the training data is not necessarily annotated with exem-

plar behaviours of the inhabitant. This doctoral study takes several steps towards addressing the problem of finding an

approximation to this mapping, beginning with separate in-vestigations on current methods proposed in the literature,

identifying useful sensory outputs for behaviour recognition,and concluding by proposing two directions: one using su-

pervised learning on annotated sensory stream and one using unsupervised learning on unannotated ones.