Sensor Diagnosis System for Sleep

Abstract

The cloud-computing-dirven and fog-computing-dirven mode have further inspired the studies of preventive medicine. If the database of physicians’ recorded clinical cognitive diagnosis can be smartly utilized in the Internet of Things( IoT) technologies for AI monitoring of sleep quality and disease risks prediction, then the preliminary diagnostic data provided by such terminal sleeping diagnosis device might be used for prevention of diseases at early-stage and, therefore, to greatly reduce medical cost, to increase accessibility of reliable medical services and to further reduce medical risk. However, the diagnostic big data is almost missing in current e-medical system. Therefore, a wearable smart sensor diagnosis system for sleeping patients is proposed in this article. Within the diagnosis system, smart devices for brain, nose, heart, lung and legs are included. All detected data is uploaded to the user’s mobile APP for preliminary comprehensive diagnosis calculation, which is called fog calculation. The refined data provided by the APP of sleeping sensor system can cover a large proportion of physicians’ cognition of each patients’ real conditions. Refined data, which has been removed redundant data by fog calculation, is uploaded to big data cloud for accurate diagnosis and treatment. The sleeping diagnosis big data, uploaded by sensor system perhaps reflected the values of the well-linked text information produced by a combined database of online and offline medical records.