Mathematical Model Simulation of Detailed Classification of Telemedicine Sensing Data

Medical and health field is a hot application field of wireless sensor networks. How to correctly refine and classify telemedicine sensor data is the research focus in related fields. Therefore, a detailed classification mathematical model simulation of telemedicine sensor data based on multi feature fusion is proposed. On the basis of telemedicine sensor data acquisition, it is preprocessed to reduce the computational overhead of detailed classification. The reliability features of the preprocessed telemedicine sensing data are extracted, the extracted features are fused by the principal component analysis method, and the refined classification model of telemedicine sensing data is constructed based on the principle of machine learning. The fused features are input into the model to complete the refined classification of telemedicine sensing data. The experimental results show that the correct refinement classification rate of the proposed method is more than 90%, the refinement classification accuracy is higher than 98.5%, the convergence speed is good, and the refinement classification time is 4 similar to 12 s, which proves that the correct refinement classification rate and accuracy of the proposed method are high, the classification time is short, and has good application performance.

Multi-Sensor Data Fusion with a Reconfigurable Module and Its Application to Unmanned Storage Boxes.

We present a multi-sensor data fusion model based on a reconfigurable module (RM) with three fusion layers. In the data layer, raw data are refined with respect to the sensor characteristics and then converted into logical values. In the feature layer, a fusion tree is configured, and the values of the intermediate nodes are calculated by applying predefined logical operations, which are adjustable. In the decision layer, a final decision is made by computing the value of the root according to predetermined equations. In this way, with given threshold values or sensor characteristics for data refinement and logic expressions for feature extraction and decision making, we reconstruct an RM that performs multi-sensor fusion and is adaptable for a dedicated application. We attempted to verify its feasibility by applying the proposed RM to an actual application. Considering the spread of the COVID-19 pandemic, an unmanned storage box was selected as our application target. Four types of sensors were used to determine the state of the door and the status of the existence of an item inside it. We implemented a prototype system that monitored the unmanned storage boxes by configuring the RM according to the proposed method. It was confirmed that a system built with only low-cost sensors can identify the states more reliably through multi-sensor data fusion.