Enabling Edge Cloud Intelligence for Activity Learning in Smart Home
This work addresses activity learning for smart home users, but it is incremental as it builds on existing methods by incorporating temporal features.
The authors tackled activity recognition and prediction in smart homes by proposing an Edge Cloud framework that leverages temporal features like activity order, validated with real data.
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.