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Wearable health-monitoring systems (WHMS) promise to revolutionize health care by providing real-time unobtrusive monitoring of patientspsila physiological parameters through the deployment of several on-body and even intra-body biosensors. Although several technological issues regarding WHMS still need to be resolved, in order for them to become more applicable in real-life scenarios, it is expected that continuous ambulatory monitoring of vital signs will enable pro-active personal health management and better treatment of patients suffering from chronic diseases, of the elderly population and of emergency situations. In this paper a novel formal language based model for multi-sensor data fusion and early-detection of various conditions is presented. Patterns or even signal states indicating pathological symptoms that are presented in the signals, which can be collected from on-body distributed biosensors, are modeled as symbols of the Prognosis context-free formal language, whose grammar and production rules define the prognosis-words. The proposed approach is based on a described generic WHMS model and on a simple but at the same time efficient method for characterizing body-signalpsilas patterns and/or states. Finally, we provide several illustrative examples for better comprehension of the proposed model.