On the Validation of Multi-Level Personalised Health Condition Model

This paper presents a verification-based methodology to validate the model of personalised health conditions. The model identifies the values that may result in unsafe, unreachable, in-exhaustive, and overlapping states those can otherwise threaten the patient's life as a result of producing false alarms by accepting suspicious behaviour of the target health condition. Contemporary approaches to validating a model employ various testing, simulation and model checking techniques to recognise such vulnerabilities. However, these approaches are neither systematic nor exhaustive and thus fail to identify those false values or computations that estimate the health condition at run-time based on the sensor or input data received from various IoT medical devices. We have demonstrated our validation methodology by validating our example multi-level model that describes three different scenarios of Diabetes health conditions.