Patient Healthcare Monitoring system for Emergency Situations

Abstract

System for real time patient monitoring, have attracted noteworthy consideration in the most recent two decades. An enormous number of economical forms of patient checking systems are accessible, which were being used by approved health care experts. Notwithstanding this there is a solid requirement for online patient observing system, when the patient isn't in the emergency clinic. The principle target of this paper is to structure and execute an economical, convenient powerful patient checking observing system that can transmit the essential indications of a patient in crisis /crucial circumstances constantly through a remote communication network. Different sensors, for example, pulse, temperature, blood pressure and fingerprint are inter-faced through the microcontroller for estimating the significant physical parameters of the patient. For remote transmission, these sensors are associated with a sensor node through GSM module. Raspberry-Pi is utilized as a sensor node as it has better highlights contrasted with different controllers. Subsequent to giving system access to the Raspberry Pix board, it goes about as a server. At that point the server routinely sends estimated information to the web-server, which are additionally checked by employing web page which can be recovered from anyplace on the planet using workstations, laptops, android advanced cell and so on. The deliberate constant parameters are refreshed at regular intervals. The information procured is first stored, analyzed and projected on a webserver. The framework is executed so that, when the deliberate physiological information surpasses the threshold value, the overseer is alarmed through SMS and a voice call. Also using the proposed system, real-time remote monitoring is also carried out on the patient. Additionally, positional data of the individual can be obtained from GPS receiver as shown on the digital map and sent to the related units. Thus, the system proposed in this paper makes the individual’s day-to-day life relaxed and more comfortable.

The Prediction of Diabetes

“…The selected algorithms with the highest level of accuracy are discussed, and their results are compared (Perveen et al, 2019). Currently, designing monitoring models for actual data of patients is getting attraction, and many models are available for use in the health monitoring system (Chauhan et al, 2019).…”

“…Further, there is a great need for the online availability of these models. However, a robust design of such a model, a GSM and IoT based structure, is described in (Chauhan et al, 2019). Moreover, the role of controlling glucose levels that help to prevent complications in diabetes is handled in (Vehí et al, 2019); the risks are described, and the application of machine learning models is provided.…”