IoT-Based Telemedicine Health Monitoring System with a Fuzzy Inference-Based Medical Decision Support Module for Clinical Risk Evaluation

This paper discusses the design and implementation of an Internet of Things (IoT)-based telemedicine health monitoring system (THMS) with an early warning scoring (EWS) function that reads, assesses, and logs physiological parameters of a patient such as body temperature, oxygen saturation level, systemic arterial pressure, breathing patterns, pulse (heart) rate, supplemental oxygen dependency, consciousness, and pain level using Particle Photon microcontrollers interfaced with biosensors and switches. The Mandami fuzzy inference-based medical decision support system (FI-MDSS) was also developed using MATLAB to assist medical professionals in evaluating a patient's health risk and deciding on the appropriate clinical intervention. The patient's physiological measurements, EWS, and health risk category are stored on the Particle cloud and Thing Speak cloud platforms and can be accessed remotely and in real-time via the Internet. Furthermore, a RESTful application programming interface (API) was developed using GO language and PostgreSQL database to enhance data presentation and accessibility. Based on the paired samples t-tests obtained from 6 sessions with 10 trials for each vital sign per session, there were no significant differences between the clinical data obtained from the designed prototype and the commercially sold medical equipment. The mean differences between the compared samples for each physiological data were not more than 0.40, the standard deviations were less than 2.3, and the p-values were greater than 0.05. With a 96.67% accuracy, the FI-MDSS predicted health risk levels that were comparable to conventional EWS techniques such as the Modified National Early Warning Score (m-NEWS) and NEWS2, which are used in the clinical decision-making process for managing patients with COVID-19 and other infectious illnesses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Fuzzy Inference for Plate Classification

One of the sectors affected by rapid developments in technology and epidemics is the food and beverage sector. Especially with the Covid-19 pandemic, the use of robots in this sector has become important and has become increasingly widespread in order to keep human contact at the minimum level. Businesses such as restaurants, hotels and catering companies, which are the pioneers of the food and beverage service industry, have started to use robots more effectively to fulfill the tasks performed by their staff. The dishwashing room, which is one of the units where robots are used in the service sector, is important in terms of both reducing the interaction of people with dishes as much as possible in terms of health and time saving by washing the dishes quickly and classifying them according to their types. Therefore, in this study, classification and separation process of tea, dessert, dinner and salad plates with known width and depth dimensions of the surface according to their types was carried out by using the triangular and gaussian membership functions with Mamdani fuzzy interference metod. © 2022 IEEE.

Deep Reinforcement Learning based Haptic Enhancement for Tele-Diagnosis

Tele-Diagnosis is beneficial for medical care in areas with inadequate resources, which helps control the spread of Covid-19 in the current pandemic. Most teleoperated diagnostics are dependent on humans, possibly leading to cognitive issue caused by distanced communication. In this paper, we propose a local haptic enhancement framework to facilitate the remote palpation. The deep deterministic policy gradient (DDPG) algorithm is exploited to compensate for signal transmission due to latency, allowing human to operate without the sense of delay. With the help of weighted recursive least squares (WRLS) method, the interactive force can be estimated on the patient's side despite the lack of force sensors. Fuzzy inference is used to diagnose and classify the extent of disease based on the estimated force and motion state on the remote side, thereby leveraging the remote sensory information to conduct autonomous reasoning. Finally, the final diagnosis is derived by performing minimum risk Bayesian decision based on local and remote inference results. Comparative simulation results have validated the superior performances of the proposed scheme. © 2022 IEEE.

Fuzzy Inference Speed Enhancement for Low Budget Computers using Hash Indices

Fuzzy inference is a powerful tool used in many fields of science nowadays, including medical science. However, for applications where the number of fuzzy rules is very large, the increased computational complexity for systems with limited resources (such as low budget computers and embedded systems) can result in a very slow operation. In this paper, a new method is proposed to accelerate the operation of Fuzzy Inference Systems that is faster than the conventional sequential procedure, primarily for such computer systems. © 2022 IEEE.

Intelligent Cloud IoMT Health Monitoring-Based System for COVID-19

The most common alarming and dangerous disease in the world today is the coronavirus disease 2019 (COVID-19). The coronavirus is perceived as a group of coronaviruses which causes mild to severe respiratory diseases among human beings. The infection is spread by aerosols emitted from infected individuals during talking, sneezing, and coughing. Furthermore, infection can occur by touching a contaminated surface followed by transfer of the viral load to the face. Transmission may occur through aerosols that stay suspended in the air for extended periods of time in enclosed spaces. To stop the spread of the pandemic, it is crucial to isolate infected patients in quarantine houses. Government health organizations faced a lack of quarantine houses and medical test facilities at the first level of testing by the proposed model. If any serious condition is observed at the first level testing, then patients should be recommended to be hospitalized. In this study, an IoT-enabled smart monitoring system is proposed to detect COVID- 19 positive patients and monitor them during their home quarantine. The Internet of Medical Things (IoMT), known as healthcare IoT, is employed as the foundation of the proposed model. The least-squares (LS) method was applied to estimate the linear model parameters for a sequential pilot survey. A statistical sequential analysis is performed as a pilot survey to efficiently collect preliminary data for an extensive survey of COVID-19 positive cases. The Bayesian approach is used, based on the assumption of the random variable for the priori distribution of the data sample. Fuzzy inference is used to construct different rules based on the basic symptoms of COVID- 19 patients to make an expert decision to detect COVID-19 positive cases. Finally, the performance of the proposed model was determined by applying a four-fold cross-validation technique. © 2022 Tech Science Press. All rights reserved.