With the fast development of cloud computing and computer science technology, the combination of the IOT and clod computing in the medical-assisted environment is urgently needed. The prior research focus more on individual development of the single technique, quite a less research on the field of medical monitoring and managing service application have been conducted. Therefore, in this paper, we study and analyze the application of cloud computing and the Internet of Things on the field of medical environment. We are trying to make the combination of the two kinds of technology monitoring and management information system in hospital. Remote monitoring cloud platform architecture model (RMCPHI) set up medical information in the first place. Then the RMCPHI architecture was analyzed. Eventually, the last effective PSOSAA algorithm proposed the hospital medical information service cloud system monitoring and management application. Experimental simulation illustrates that the proposed algorithm outperforms the other state-of-the-art algorithms. Further potential research areas are discussed.

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Abstract

This paper designs an internet of things-based temperature & humidity monitoring system in the key areas of the hospital to address the problem that the temperature and humidity monitoring systems are independent from each other in the hospital. The establishment of a hospital-level temperature and humidity monitoring platform is able to realize the integrated monitoring and management on such areas as ICU, pharmacy and operation room etc, where both of the temperature and humidity must be monitored. In every temperature and humidity acquisition node, the collected environmental data about the indoor humidity and temperature will be sent over the 433M wireless sensor network to the internet of things (IoT) gateway, through which data will be uploaded via wifi or the wired access network within the hospital to the background server, where in addition to data collection and analysis, various reports will be generated and control commands will be transferred. Then all of the medical staffs can make a real-time inquiry and control on the environmental data by connecting their intelligent terminals (such as cell phone) to the server. Actually when the humidistat thermostat controller is designed according to the DDC principle and the integral-separation PID control algorithm, the control on the central airconditioning units is able to realize the real-time control of constant temperature and humidity. Also a prototype system has been established to conduct the functional verification. The experiment shows that this system is able to run steadily with precise data collection and reliable control.

Introduction. Health monitoring and remote diagnosis can be realized through Smart Healthcare. In view of the existing problems such as simple measurement parameters of wearable devices, huge computing pressure of cloud servers, and lack of individualization of diagnosis, a novel Cloud-Internet of Things (C-IOT) framework for medical monitoring is put forward. Methods. Smart phones are adopted as gateway devices to achieve data standardization and preprocess to generate health gray-scale map uploaded to the cloud server. The cloud server realizes the business logic processing and uses the deep learning model to carry out the gray-scale map calculation of health parameters. A deep learning model based on the convolution neural network (CNN) is constructed, in which six volunteers are selected to participate in the experiment, and their health data are marked by private doctors to generate initial data set. Results. Experimental results show the feasibility of the proposed framework. The test data set is used to test the CNN model after training; the forecast accuracy is over 77.6%. Conclusion. The CNN model performs well in the recognition of health status. Collectively, this Smart Healthcare System is expected to assist doctors by improving the diagnosis of health status in clinical practice.