Adaptive and personalized user behavior modeling in complex event processing platforms for remote health monitoring systems

作者：

Highlights：

• An adaptive and personalized cCEP Platform for remote health monitoring is presented.

• A machine learning method has been used to extract the rules and rules' thresholds of a CEP engine from existing data.

• A combinational feature selection method is proposed.

• The possibility of updating the rules due to rule adaption has been provided.

摘要

•An adaptive and personalized cCEP Platform for remote health monitoring is presented.•A machine learning method has been used to extract the rules and rules' thresholds of a CEP engine from existing data.•A combinational feature selection method is proposed.•The possibility of updating the rules due to rule adaption has been provided.

论文关键词：Remote health monitoring,User-behavior modeling,Complex event processing,Rule-based learning,Explainability,Personalized rule adaption

论文评审过程：Received 8 July 2021, Revised 28 July 2022, Accepted 2 October 2022, Available online 7 October 2022, Version of Record 19 October 2022.

论文官网地址：https://doi.org/10.1016/j.artmed.2022.102421