# A Visual User Interfaces for Constant Checking of Non-Invasive Physiological Parameters
## 1 Introduction  
One of the most popular technologies is the Internet of Things (IoT), which is referred to as a global network that includes a monitoring system that gathers data thanks to IoT sensors and enables the processing and analysis of that data [1]. IoT devices were created and can adopt protocols that are used depending on the needs or requirements they provide; devices can also connect with each other. Today, humanity depends on the Internet, and to obtain true and high-performance communication, IoT devices were developed. To enable the exchange of information on a network from anywhere in the world, in addition to being able to send and receive data between electronic devices connected to a Wi-Fi network, communication protocols are made up of layers that provide specific functionalities that make up a set of rules [2]. As IoT researchers build more dependable, adaptable networks, the number of mobile devices connected to the Internet increases over time. Up until 2020, there were at least 8 billion devices connected to the Internet without counting phones [3]. Deep neural network-powered IoT in healthcare has resulted in ground-breaking advancements for the medical community, distinctive probability, and medical data analysis in the healthcare sector [4]. In order to do this, monitoring systems have been put in place that support human health and medical care while utilizing IoT technology to support ongoing and remote monitoring. Its structures are crucial because they increase access to immediate medical care, cut costs associated with travel to and from medical appointments, and generally improve health [5]. Hospitals have also created sensors that use deep learning methods, such as seizure neural networks, to measure a variety of parameters, including temperature, heart rate, and blood pressure [6]. The study by [PERSON] et al. in 2019 [7] sought to design a chair for everyday use by incorporating wireless sensors to monitor air quality and heart rate continuously in real-time; they employed a variety of hardware and open-source software tools, including PPG sensors (Photoplethysmography), a simple and low-cost optical technique used to calculate heart rate, and ECGs (Electrocardiogram), which are used to extract signals corresponding to heart rate variability. A Raspberry Pi for data transmission and a web app for information visualization were used in this study. The device was dependable and trustworthy, producing excellent results crucial for those with high or low blood pressure. [PERSON] et al., 2019 [8] created an erratic electronic belt to continuously monitor the body's pulse and body temperature. As hardware, it used a Wemo microcontroller, which is smaller than the Arduino but performs the same functions. The software incorporates the same sensors as the first study, with the exception that it uses GMSs. [PERSON] and [PERSON], 2020 [9] designed an IoT-based pulse oximeter to track specific physiological parameters, including SpO2, heart rate frequency, and blood pressure. To do this, they used GPS software to determine the precise coordinates of the patient and some sensors already mentioned in the second study [8], such as GMS and PPG. With the aid of a new sensor built into this prototype, the GPRS (General Packet Radio Service), which connects with the GMS to generate an alert, calls the emergency room, and sends a text message to the patient's nearest family members, it was possible to create a mobile application. Among the various types of biosensors, electrochemical (EC), optical, and magnetic biosensors have been successfully developed for the detection of tumor biomarkers, including those related to prostate cancer, in clinical and point-of-care (POC) settings. While POC biosensors have shown potential for detecting PC biomarkers, certain limitations, such as sample preparation, need to be addressed.