# Bluetooth-Based Healthcare Information and Medical Resource Management System
## 4 Results
### Management of Medical Personnel and Equipment Location Information  
The location of medical devices and equipment in the hospital area can be grasped in real time, as shown in Figure 26, through the deployment of positioning devices and Bluetooth Hubs and using the circle-circle intersection technique to assist healthcare staff in management. For location positioning in security-sensitive areas, fingerprint positioning is used. In order to verify its accuracy, the following experiments were conducted. Figure 26: Medical Equipment Positioning information. Figure 25: (a) Real-time temperature and (b) temperature/distance alert setting. A 10 \(\times\) 8 m\({}^{2}\) indoor space was used as the experimental environment; it was divided into 49 cells, of which each is approximately 1 m\({}^{2}\), as shown in Figure 27; each cell was numbered 1, 2, 3, 4,..., 49 from top to bottom and left to right. Four Bluetooth Hubs were built at the four corners of the environment (cells #1, #7, #43, and #49) in order to collect the signal strength of the sensor being positioned and the signal strength of the sensor collected by each Bluetooth Hub was used as the feature of the sensor in the cell. In this experimental environment, there are tables, chains, computer equipment, iron cabinets, etc. The rectangular diagram in Figure 27 represents the iron cabinets, which are more likely to block the Bluetooth signal. This experimental environment is used to simulate the medical site, such as the outpatient hall and appraisal counter in the lobby (cells #3-5, #10-12, #17-19, and #24-26), the aisle area outside the ward (cells #6, #13, #20, #27, #34, #41, #48, #2, #9, #16, #23, and #30-33), the area inside the ward mostly contains beds and metal cabinets (cells #37-39 and #44-46). In order to collect the signal strength of the sensors in each cell, a sensor was placed in the center of each cell and simulated to be worn by the user (this sensor was placed 110 cm above the ground). Each Hub collected 100 points of signal strength. A total of (49 cells \(\times\)100 points \(\times\) 4 Hubs) and 19,600 samples of data were collected. The data collected by the four Bluetooth Hubs were combined through the time stamp of the sensor, and the signal strength data originally uploaded in a single entry were now combined into the signal strength data set received by the four Hubs at the same time, as shown in Figure 28, with a total of 4900 entries, providing the necessary data for the machine learning models. A total of 4900 signal entries were divided in a ratio of 9 to 1. 90% of the data were used as the training data and 10% as the test data, and the RSSI signal strength was used as the feature value and the cell number as the label. The data were trained using KNN and SVM. Figure 29 shows the accuracies and execution times of these two models. The accuracy and execution time are 0.84 and 0.171836 s for KNN, and 0.83 and 3.297741 s for SVM. Figure 27: Experimental environment for fingerprint positioning. In order to compare the accuracy of the intersection method and the accuracy of the fingerprinting positioning method, the test data were obtained by the sensor carried by a user walking from cell #2 to cell #49 (12 cells in total) illustrating by red arrows, as shown in Figure 30. One hundred points (signal samples) in each cell were collected by each of the four Hubs. A total of 4800 points (12 cells \(\times\) 100 points \(\times\) 4 Hubs) were collected. These data were then pre-processed and combined into 1200 entries, and each entry represents the locational signal strength data set received by the four Hubs at the same time. The coordinates of the sensor location were first calculated by performing the intersection method based on the signal strength collected from the Bluetooth Hubs.