## 4 Discussion and Conclusions  
This paper proposed a multisensor system that can compensate for each individual sensor. The entire system included a main body, an IV fluid hunger, hardware, firmware for the hardware, and a mobile device application. Even though the developed system yielded good accuracy, communication via Wi-Fi showed characteristics of latency. In its current form, it cannot be used for practical applications. Thus, additional studies using 4G or 5G networks are required, as these networks have low latencies of approximately 20 ms that allow the entire system to be connected to a cloud server, thus establishing the basis for telemedicine applications. Although a complete set of comparative tests was conducted under the same dynamic conditions, a device that generates consistent movement is required to quantify the entire dataset. It was challenging to quantify the accuracy of the load cell during motion. The value obtained from the load cell during motion was affected by considerable bouncing motions, which could not be adequately accounted for. The cost of the entire system is lower than that of available products in the market; however, the cost of sensors is high. This implies that adopting artificial intelligence (e.g., deep learning or machine learning) in medicine and healthcare [28] can help achieve another breakthrough. This study tested single-sensor systems that have been used in previous studies and have exhibited nonoptimal characteristics under dynamic conditions. The proposed system was tested under dynamic conditions and yielded an average accuracy of 88%. Furthermore, a 94% accuracy in alert notifications was obtained when the fluid volume was less than 10%. Figure 7: Captured image of the application on a mobile device. (**a**) Communication between the nursing station and microcontroller, and (**b**) image of a notification sent to a mobile device. The entire system can be used under both static and dynamic conditions. Its ability to be connected to the medical staff's mobile devices in NSs justifies its versatility. All intended purposes were achieved through the precise development of the entire system. This system can send notifications with high accuracy to prevent the backflow of blood. Furthermore, it can help medical staff and prevent unwanted infections, which is particularly important in pandemic situations such as the ongoing COVID-19 pandemic. The use of the proposed system will improve the experiences of patients and medical staff. Furthermore, the use of IoT in the medical field will help expand the range of telemedicine applications. With the rapidly increasing number of inpatients owing to COVID-19, there is a high possibility of malpractice, errors, and accidents. Most medical staff members are still manually calculating \(Gtt\) values to monitor whether the infusion was set as advised or not. However, interobserver drop/min count rate variability can occur. Accordingly, an automated and accurate monitoring system is necessary. This study suggests a system that can prevent possible accidents related to IV infusion therapy, which is the most common treatment in hospitals. This study was conducted based on the demand for remote monitoring systems that can benefit both patients and medical staff. Additionally, in cases where there is a lack of medical staff to treat patients, particularly in rural areas, this remote system can help alleviate the workloads of the available medical staff members. In conclusion, the proposed system can help hospitals avoid severe accidents during IV therapy and can reduce concerns among medical staff and patients, resulting in increased service efficiency. Design and simulation, J.-K.L. ; theory analysis and advisory, K.-C.Y. ; supervisor, K.G.K. All authors have read and agreed to the published version of the manuscript. This research was financially supported by the Seoul R&D Program (BT190153) through the Research and Development for Regional Industry and GRRC program of Gyeonggi province (No. GRRC-Gachon2020(B01).