# Bluetooth-Based Healthcare Information and Medical Resource Management System
## 5 Discussion  
The results of (a) the management of patient physiological information monitoring, (b) management of medical personnel and equipment location information, (c) management of medical staff work coordination information exchange and transmission, and (d) message transmission security protection mechanism show the presented system works properly and is able to provide necessary service functions to medical/healthcare institutions to reduce the loading of medical staff. Since Bluetooth is used as a medium for data transmission in the system, integrating additional medical or wearable devices into the presented system in the future is feasible as long as they are Bluetooth-based devices and their data protocols are accessible. Such devices could be ear thermometers, sphygomanometers, oximeters, intravenous drip controllers, electrocardiography (ECG) units, electronic stethoscopes, etc. Moreover, the Bluetooth Hub at the entrance of the ward in Figure 7 collects the data from the smart T-shirts and smart diapers through Bluetooth broadcasting. There can be many Bluetooth-based devices in the ward. The use of Bluetooth-based smart T-shirts and smart diapers in the institutions provides, for the first time, the ability for long-term, continuous monitoring of patients or care recipients. Since the variation of temperature or humidity is slow, these data are broadcast every second. The size of the data packet is about 32-50 Bytes. The tags of smart T-shirts and smart diapers not only can collect physiological data from wearable devices (T-shirts and diapers) but also can be used for indoor positioning as sensors. Therefore, the person who carries or wears it can be tracked or located. If security issues are concerned, when the person enters a dangerous area, a warning message can be sent to his/her caregiver to intervene for protection or treatment. For the purpose of finding lost or misplaced medical equipment and devices in the institution, the indoor positioning function using the intersection technique provides sufficient location information to locate medical equipment and devices, although it may not be good enough for navigation purposes. However, for location positioning in security-sensitive areas, fingerprint positioning was adopted in this study to provide better location positioning resolution to improve the accuracy of positioning and to avoid danger to patients or care recipients. Unlike the experimental scenario proposed by [PERSON] et al. [49], where 36 Hubs were deployed in a space of 300 m\({}^{2}\) with a grid size of 0.6 m \(\times\) 0.6 m and 868 reference points, the experimental site in this study is 49 m\({}^{2}\) with a grid size of 1 m \(\times\) 1 m and 49 reference points and has only four Hubs with a spacing of 7 m. Two machine learning models, KNN and SVM, were tested for fingerprint positioning. It was found that the accuracy of KNN was better than that of SVM. However, the difference between them was not significant, but the time of training SVM was more than 18 times  
Figure 34: Contents of encrypted message packets. that of KNN. While looking at the positioning error of [49] using the weighted KNN, we found that the average error is 4.04 m. The KNN used in this study obtained an accuracy of 0.84, i.e., 84% of errors were within 1 m. When compared with the intersection method, the KNN fingerprinting positioning method can effectively and significantly improve the accuracy of localization, which is very helpful for the safety management of patients or care recipients. However, to perform fingerprinting positioning, it is necessary to collect enough locational features in the area to be localized beforehand to achieve the above accuracy, which is a considerable burden and causes a lot of localization setup costs.