Wellness contents recommendation based on human emotional and health status using EM

In smart healthcare age, there are a high demand on physical and mental wellness for improving the quality of life. Wellness generally means a healthy balance of the mind, body and spirit that results in an overall feeling of well-being. For wellness service, we monitor the physical and mental status of object for developing an inspection service middleware using multimodal biosensors. This multimodal sensor can measure EEG (electroencephalography), ECG (electrocardiography), respiration rate, SpO2 and skin temperature. From the measurements, we generated bio-emotional index. We also use ontology model which is enables reasoning, classification of datasets from sensor network and wellness contents recommendation. For this analysis we propose an Expectation Maximization (EM) based object health status prediction model for maximization of the risk rates of object status.