A General-Purpose mHealth System Relying on Knowledge Acquisition through Artificial Intelligence

Remote monitoring of patients' vital parameters and ensuring mobility of both patient and doctor can greatly profit from real-time tele-monitoring technology. Here a description is given of a multi-purpose and multi-parametric tele-monitoring system. It can take advantage of the extraction, carried out offline and automatically on a desktop, of knowledge from databases containing measurements of patient's parameters. This knowledge is represented under the form of a set of IF...THEN rules that are provided to a rule-based mobile Decision Support System embedded in the system here presented. Then, wearable sensors collect in real time patient's vital parameters that are sent to a mobile device, where they are processed in real time by an app. If, as a consequence of the measured parameters, one of the above rules is activated, an alarm is automatically generated by the system for a well-timed medical intervention. Moreover all the monitored parameters are stored in EDF files for possible further analysis. This paper presents two practical applications of the system to two significant healthcare issues, i.e. apnea monitoring and fall detection. For these use cases, comparison with other well-known classifiers is carried out to evaluate the quality of the extracted knowledge.