Enabling Edge Cloud Intelligence for Activity Learning in Smart Home [article]

Bing Huang, Athman Bouguettaya, Hai Dong
2020 arXiv   pre-print
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
more » ... me 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.
arXiv:2005.06885v1 fatcat:m42f33c7nnbxhks3griv27xuii