  We propose a novel activity learning framework based on Edge Cloud
architecture for the purpose of recognizing and predicting human activities.
Although activity recognition has been vastly studied by many researchers, the
temporal features that constitute an activity, which can provide useful
insights for activity models, have not been exploited to their full potentials
by mining algorithms. In this paper, we utilize temporal features for activity
recognition and prediction in a single smart home setting. We discover activity
patterns and temporal relations such as the order of activities from real data
to develop a prompting system. Analysis of real data collected from smart homes
was used to validate the proposed method.
