# A Visual User Interfaces for Constant Checking of Non-Invasive Physiological Parameters
## 1 Introduction
To overcome these challenges, researchers have employed new technologies to develop more practical and efficient biosensors, offering promising prospects for their integration into clinical practice [10]. [PERSON] et al., 2022 [11] have explored various respiration monitoring devices, including wearable sensors integrated into clothing and smartphone-based approaches. While these methods offer convenience, challenges persist in achieving accurate and user-friendly designs for continuous monitoring. This work presents a soft, wireless, and non-invasive device for real-time evaluation of human respiration, addressing the need for effective portable electronic devices in medical and daily respiration monitoring. The device offers high accuracy and usability, promising improved respiratory health monitoring and medical assistance. [PERSON] et al., 2019 [12] have presented microfluidic contact lens sensors for unpowered, continuous, and non-invasive intraocular pressure (IOP) monitoring. The sensors consist of a soft-elastomer sensing layer and a hard plastic reference layer, with an annular sensing chamber filled with dyed liquid and a sensing microchannel serving as the IOP transducer. The IOP signal is detected from the displacement change of the dyed liquid's interface in the sensing channel, which can be observed optically using a smartphone camera. The sensors demonstrate high sensitivity (0.708 mm/mmHg), good reversibility and long-term stability. Tests on porcine eyes ex vivo show promising results, with a sensitivity of 0.2832 mm/mmHg in the range of 8-32 mmHg and good reproducibility. This work represents a significant advancement in IOP monitoring for glaucoma management. [PERSON] et al., 2021 [13] aimed to improve the performance of non-invasive EEG-based brain-computer interfaces (BCIs) for robotic arm control, where a proposed novel shared control model dynamically optimizes the control commands based on real-time context and user characteristics. Additionally, a hybrid BCI scheme is introduced, employing steady-state visual evoked potentials and motor imagery to enable multi-dimensional control of the robotic arm. The results demonstrate significant improvements in the accuracy and success rate of completing tasks using the shared control strategy with hybrid BCI, making brain-actuated robotic arm systems more effective. [PERSON] et al., 2023 [PERSON] et al. (2023) showed that machine learning models can optimize microfluidic systems by predicting outcomes prior to fabrication, minimizing cost and time. An educational interactive microfluidic module was developed for designing efficient micromixers at low Reynolds regimes for Newtonian and non-Newtonian fluids. The machine learning model achieved high accuracy (R2 = 0.9543) for predicting the mixing index and optimizing micromixer designs for Newtonian fluids. Non-Newtonian fluid cases were also optimized using machine learning (R2 = 0.9063). The framework serves as an interactive educational module, demonstrating the integration of artificial intelligence in engineering education. This study aims to advance the creation of an IoT-based non-invasive electronic prototype for targeted physiological parameter monitoring. It also offers the incorporation of an Internet of Things (IoT)-based application to read patient data, with a graphical user interface that emphasizes the use of a widget provided by both the application and the server to be able to determine data such as blood saturation, heart rate, and temperature in order to deliver succinct information in the present and lower the likelihood of complications in the future. Since COVID-19 attacks the entire immune system and puts all countries in the world in a precarious situation, it has caused a number of complications for people with pre-existing diseases such as cardiovascular, asthmatic, and pulmonary. Devices have also been developed and put into use to address these issues, but they are expensive and not readily accessible to patients.