论文标题
启用边缘云智能，用于在智能家中学习活动
Enabling Edge Cloud Intelligence for Activity Learning in Smart Home
论文作者
论文摘要
我们提出了一个基于边缘云体系结构的新型活动学习框架，以识别和预测人类活动。尽管许多研究人员已经对活动认识进行了大量研究，但构成一种活动的时间特征（可以为活动模型提供有用的见解）并未通过挖掘算法来充分利用其全部潜力。在本文中，我们利用时间功能在单个智能家庭环境中进行活动识别和预测。我们发现活动模式和时间关系，例如实际数据的活动顺序以开发提示系统。对从智能家居收集的实际数据进行分析用于验证所提出的方法。
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.
