A feasibility study of multi-mode intelligent fusion medical data transmission technology of industrial Internet of Things combined with medical Internet of Things

Abstract

As a branch of the Internet of Things (IoT) dedicated to medical care industry, the Internet of Medical Things (IoMT) includes networking devices and applications for medical and health information technology applications. The IoMT still has many shortcomings, such as unstable information transmission, low accuracy of fault diagnosis and classification, and lack of anomaly detection capability. Therefore, this paper first comprehensively describes some relatively new research and popular background technologies of the Industrial Internet of Things (IIoT) and IoMT, and then, according to the functional requirements analysis of the intelligent data transmission system based on IoMT, we discuss in detail the problems of fault diagnosis and resource allocation faced in the IoMT model. An intelligent data transmission model is proposed to apply the wireless communication transmission technology of the industrial Internet of Things to the Internet of Medical Things scene. This model has the ability of high-quality data transmission, high accuracy accident diagnosis classification and real-time anomaly monitoring, which makes up for the shortcomings of traditional IoMT models. In particular, for the accident diagnosis and classification functions in this model, we innovatively adopt the multi-mode data fusion CNN algorithm. The experimental results show that the classification accuracy of the accident diagnosis results is effectively improved. Compared with other algorithms, this model with multi-mode data fusion CNN algorithm improves the data transmission rate, reduces the average data delay, and improves the real-time anomaly monitoring capability of IoMT without increasing the signal leakage rate, thus further improving the overall stability of IoMT.