Affiliations: [a] Business School, University of Shanghai for Science and Technology, Shanghai, China
| [b] Department of Operation Management, East Hospital Affiliated to Tongji University, Shanghai, China
Correspondence:
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Corresponding author. Zhu Renjie, Business School, University of Shanghai for Science and Technology, Shanghai, China. E-mail: [email protected].
Abstract: Nowadays, science and technology are developing, more and more things need to be detected by science and technology, and the content that people need to understand is becoming more and more complex. Not only need to know its size, but also need to know some of the relevant comprehensive information or internal information. At present, people’s demands can no longer be met only by existing medical systems, so data fusion technology has emerged. This technology can simultaneously obtain a variety of information, express various information, seek the internal relationship between various information, and comprehensively process and improve this relationship. In view of the existing medical equipment, this paper puts forward the design method of multi-sensor data fusion technology. The original whole system is decomposed into several small particles and extracted from the original system. The extracted particles are arranged independently and the neural network system is formed. On the basis of neural network computing and implementing network feature service, this paper introduces how to establish a new medical equipment system based on network registration, discovery and various management and fault-tolerant conditions. This article is a community-oriented, long-distance service intelligent system based on family health care, designed on network processors and Android systems. By combining various technologies, collecting various body information parameters of patients, under the guidance of network protocol and existing remote technology, the gateway of intelligent home can talk to the community to a certain extent. In this way, the data collected in smart homes can be uploaded to other communities through the community network.
Keywords: Multi-sensor data fusion, neural network, real-time operating system, telemedicine
