Recently, there are high demand on healthy life and wellness due to the increased lifespan. Wellness is generally used to mean a healthy balance of the mind, body and spirit that results in an overall feeling of well-being. To monitor the physical and mental wellness of object, we developed an inspection service middleware for monitoring physical and mental health condition by analyzing EEG (electroencephalography), ECG (electrocardiography), respiration rate, SpO2 and EMG (Electrocardiogram) waveforms from multi-modal biosensors under the coverage of a wireless sensor network (WSN). We also use ontologies are an adequate methodology to model sensors and their capabilities. For the inspection service middleware, we propose a prediction model based on risk ratio Expectation Maximization (EM) by monitoring bio-sensor data in real-time. We also used ontology model which enables reasoning, classification of datasets from sensor network and wellness contents recommendation. This inspection middleware for monitoring healthcare condition and support recommendation of wellness contents such as customized exercise, proper diet, and hospital checkup. In this paper, there are the five modules as follows: (1) The measurement of biometrics such as body temperature, EEG, ECG, respiration rates and EMG, (2) Object assessment from measurement wavelength, (3) Situation assessment using GPS in smart device, (4) Maximized health condition using risk ratio EM, (5) decision making and recommendation of wellness contents.
Jung, Y., & Yoon, Y. (2015). Ontology model for wellness contents recommendation based on risk ratio em. In Procedia Computer Science (Vol. 52, pp. 1179–1185). Elsevier B.V. https://doi.org/10.1016/j.procs.2015.05.155