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

Naseri, Mohammad Mehdi, Tabibian, Shima and Homayounvala, Elaheh (2022) Adaptive and personalized user behavior modeling in complex event processing platforms for remote health monitoring systems. Artificial Intelligence in Medicine, 134 (102421). pp. 1-26. ISSN 0933-3657


Taking care of people who need constant care is essential and its cost is rising every day. Many intelligent remote health monitoring systems have been developed from the past till now. Intelligent systems explainability has become a necessity after the worldwide adoption of such systems, especially in the health domain to explain and justify decisions made by intelligent systems. Rule-based techniques are among the best in terms of explainability. However, there are several challenges associated with remote health monitoring systems in general and rule-based techniques, specifically. In this research, an adaptive platform based on Complex Event Processing (CEP) has been proposed for user behavior modeling to provide adaptive and personalized remote health monitoring. This system can manage a massive amount of data in real-time utilizing the CEP engine. It can also avoid human errors in setting rules thresholds by extracting thresholds from previous data using JRip rule-based classifier. Moreover, a feature selection method is proposed to decrease the high number of features while maintaining accuracy. Additionally, a rule adaption method has been proposed to cope with changes over time. Additionally, a personalized rule adaption method is proposed to address the need for responsiveness of the system to the special requirements of each user. The experimental results on both hospital and activity data sets showed that the proposed rule adaption method improves the accuracy by about 15 % compared to non-adaptive systems. Additionally, the proposed personalized rule adaption method has an accuracy improvement of about 3 % to 6 % on both mentioned datasets.

E.Homayounvala 2022 Artificial Intelligence in Medicine Journal.pdf - Accepted Version
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