This paper presents a Fuzzy approach to monitor the health of a patient in pervasive computing environments. A decision model considers three classes of variables that represent the context information being collected: environmental, physiological and behavioral. A case study of blood pressure monitoring was developed to identify critical situations based on medical knowledge. The solution maintains the interpretability of a set of defined rules, even after a learning phase that proposes adjustments to them. In this phase, the Fuzzy C-Means clustering was chosen to adjust membership functions, using the cluster centers. A medical team evaluated data from 24-hour monitoring of 30 patients and the rating was compared with the results of the system. The proposed approach proved to be individualized, identifying critical events in patients with different levels of blood pressure with an accuracy of 90% and low rate of false negatives.
context inference; fuzzy logic; pervasive healthcare
