Smart Health Research Articles

Feasibility of continuous smart health monitoring in pregnant population: A mixed-method approach.

The utilization of smart monitoring technology offers potential for enhancing health outcomes, yet its feasibility and acceptance among Hispanic pregnant individuals remain uncertain. This is particularly crucial to investigate within the context of apparently healthy individuals identified as low risk, who still face a 10% likelihood of complications. Given their frequent underrepresentation in healthcare services and relative lack of attention, improving the feasibility of remote monitoring in this population could yield significant benefits. To address this gap, our study aimed to adapt and evaluate the practicality of a smart monitoring platform among healthy Hispanic pregnant women during the second and third trimesters of pregnancy, as well as one week following childbirth, a period when complications often arise. This longitudinal study followed n = 16 participants for an average of 17 weeks. Participants were instructed to wear the Oura ring for objective data collection, including activity, sleep, and heart rate, and to complete survey questions through REDcap to assess mental health and lifestyle factors. The study framework utilized the RE-AIM approach, with acceptability and adherence as key components of the feasibility evaluation. Our findings revealed that completion rates for biweekly and monthly surveys remained consistently high until after childbirth (approximately 80%), while daily question completion remained above 80% until 38th week of gestation, declining thereafter. The wearing rate of the Oura ring remained consistently above 80% until the 35th gestational week, decreasing to around 31% postpartum. Participants cited barriers to wearing the ring during the postpartum period, including difficulties managing the newborn, forgetfulness, and concerns about scratching the baby's skin. The enrollment rate was 71.42%, with an attrition rate of 6.25%. Thematic analysis of one-on-one interviews identified three main themes: personal desire for health improvement, social acceptability and support, and conditions influencing device/platform efficiency. In conclusion, while adherence varied based on gestational week and survey frequency, the study demonstrated strong acceptability of the smart monitoring platform among the study population, indicated by the high enrollment rate. Qualitative insights underscored the significance of personal motivation, social support, and device/platform efficiency in enhancing patient engagement with digital health monitoring during pregnancy, offering valuable considerations for future healthcare interventions in this domain.

Avoidance of bile duct injury in laparoscopic cholecystectomy with feasible intraoperative resources: A cohort study.

Laparoscopic cholecystectomy (LC) is one of the most commonly performed surgeries and is considered the standard treatment for cholelithiasis. However, it is associated with a risk of bile duct or hepatic artery injuries. This study evaluated the safety of LCs and the conversion rate (CR) by achieving a critical view of safety (CVS) and identification of Rouviere's sulcus (RS). This was a single-group cohort study that included consecutive patients undergoing LC at Smart Health Tower (Sulaimani, Iraq) from January 2021 to January 2023. The data were prospectively collected from patients' profiles or surgical notes within the hospital's database. A total of 419 patients underwent LC, of which females were the predominant gender (78.5%). The mean and median ages of the cases were 46.3±15.8 and 45 years, with a range of 2-90 years, respectively. The most common indications for surgery were biliary colic (69.5%), followed by acute cholecystitis (23.9%). The duration of the operations was significantly shorter for cases in which the CVS (45.6±17.9 min) or identification of RS (45.6±18.6 min) was achieved compared to those where the CVS (63.7±27.7 min) or RS (50.7±21.7 min) was not observed. Surgeries for patients with both CVS achievement and RS identification were also significantly less time-consuming (44.3±17.6) than counterparts (53.3±22.6). Among the cases without CVS achievement or RS identification (n=97, 23%), eight (8.2%) had adhesions, 12 (12.4%) had a distended gallbladder (GB) and 10 (10.3%) had thick GB walls. In addition, four (4.1%) experienced GB perforation, two (2.1%) had bleeding and one (1%) had stone spillage. There was no conversion. The achievement of CVS and identification of RS are practical landmarks in performing safe LC and decreasing the CR.

Robust Encryption Framework for IoT Devices Based on Bit-plane Extraction, Chaotic Sine Models, and Quantum Operations

In the domain of Internet of Things (IoT) applications, quantum computers offer the promise of addressing complex computational issues that traditional cryptographic methods find challenging. The unparalleled computational power of quantum computers makes existing cryptographic schemes, tailored to mitigate computational complexity, ineffective. This research introduces a robust encryption scheme designed specifically for digital data exchanged between IoT devices such as smartphones, smart sensors, and smart health monitoring devices. The proposed encryption framework is based on bit-plane extraction-based encryption, the chaotic sine model, the hyperchaotic map, the quantum baker map, and quantum operations such as quantum selective scrambling. The proposed methodology involves three key phases: the first and last phase related to permutation, which is performed at the bit level based on discrete bit-plane scrambling, bit-plane extraction, and hyperchaotic mapping. However, the second phase is based on diffusion, which depends on the chaotic sine model. During the initial stage, creating correlations among image pixels, along with a couple of chaotic sequences using a quantum baker map, is accomplished through a superposition of quantum-states. Before applying diffusion, the pixels of the grayscale image are encoded using a single qubit, and quantum operations are based on a quantum circuit featuring swap gates. To introduce pixel-level permutation, a hyperchaotic map generates random sequences within the range of 0 to 255. A circular shift further shuffles the pixel rows and pixel columns of the permuted image pixels. To enhance the digital image security further, bit-planes are extracted from the permuted bit-planes. Then, the bits within each bit plane are scrambled using a random sequence generated by the hyperchaotic map. Finally, the encoded bit planes are merged to produce a final encrypted image. Experimental results demonstrate the effectiveness of the proposed methodology, with security parameters such as correlation, entropy, and key space measuring at 0.0001, 7.999, and >2100, respectively. Furthermore, to assess performance in relation to computational complexity, a detailed analysis of computational time is conducted. The results reveal that the proposed encryption scheme can execute all encryption steps presented in the proposed method in less than one second. These findings demonstrate the secure transmission of digital data between IoT devices and show the suitability of the proposed encryption scheme for real-time applications.

Smart Health in the 6G Era: Bringing Security to Future Smart Health Services

Smart Health in the 6G Era: Bringing Security to Future Smart Health Services

Optimization of resources in intelligent electronic health systems based on internet of things to predict heart diseases via artificial neural network

As a paradigm shift in tandem with the expansion of ICT, smart electronic health systems hold great promise for enhancing healthcare delivery and illness prevention efforts. These systems acquire an in-depth understanding of patient health states through the real-time collection and analysis of medical data enabled by the Internet of Things (IoT) and machine learning. With the widespread use of cutting-edge artificial intelligence and machine learning techniques, predictive analytics in medicine can assist in making the shift from a reactive to a proactive healthcare strategy. With the ability to rapidly and precisely evaluate massive amounts of data, draw intelligent conclusions, and solve difficult issues, artificial neural networks could revolutionize several industries. Two cardiac illnesses were assessed in this study using a multilayer perceptron artificial neural network that incorporated a genetic algorithm and an error-back propagation mechanism. The ability of artificial neural networks to handle consecutive time series data is crucial for optimizing resources in smart electronic health systems, especially with the increasing volume of patient information and the broad use of electronic clinical records. This requires the creation of more accurate predictive models. Through the use of Internet of Things (IoT) sensors, the proposed system gathers data, which is then used to do predictive analytics on patient history-related electronic clinical data saved in the cloud. A smart healthcare system that uses Mu-LTM (multidirectional long-term memory) to accurately monitor and predict the risk of heart disease has a coverage error of 97.94 %, an accuracy of 97.89 %, a sensitivity of 97.96 %, and a specificity of 97.99 %. In comparison to other smart heart disease prediction systems, the F1-score of 97.95 % and precision of 97.71 % is very good.

Retraction: ManikamBabu, ThangarajuJesudas. An artificial intelligence‐based smart health system for biological cognitive detection based on wireless telecommunication. Comput Intell38:1365–1378, 2022 (10.1111/coin.12513)

The above article, published online on 08 March 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor‐in‐Chief, Diana Inkpen, and Wiley Periodicals LLC. The article was published as part of a guest‐edited special issue. Following publication, it came to our attention that two of those named as Guest Editors of this issue were being impersonated and/or misrepresented by a fraudulent entity. An investigation by the publisher found that all of the articles, including this one, experienced compromised editorial handling and peer review which was not in line with the journal's ethical standards. Therefore, a decision has been made to retract this article. We did not find any evidence of misconduct by the authors. The authors have been informed of the decision to retract.

THE ROLE OF STARTUPS IN PROMOTING THE DEVELOPMENT OF SMART CITIES – DUBAI AS A MODEL

Objective: The objective of this study is to learn about the role of start-ups in promoting the development of the smart city of Dubai, since these companies are always seeking innovative solutions to the challenges facing cities and rely on the latest methods and software used, thus helping cities to achieve the indicators that guarantee them important rank among other smart cities. Theoretical Framework: In this study, the basic concepts of the variables of the study were addressed by identifying the concept of start-ups, their characteristics and their importance in general, as well as by highlighting the subject of smart cities by listing some definitions and mentioning their advantages and dimensions. To then link the two variables by clarifying the relationship between startups and smart cities. Method: The methodology adopted for this research includes a sample of the most important start-up companies in Dubai selected from STARTUPRANKING, and the services provided by each company and its area of operation have been identified. To then determine the impact of the services provided on any indicators on which cities rely to become smarter (smart governance, smart transport, smart energy, smart technology, smart infrastructure, smart health, smart buildings and intelligent populations). Through which the role of these companies in the development of smart cities is determined. Results and Discussion: The results obtained revealed that Start-ups promote the development of cities by contributing to the challenges facing cities, communities and individuals by: - Ensuring a clean environment and smart transport systems that help to facilitate the movement of citizens and tourists through the use of telephones and at the lowest cost; - Strengthening cyber security to protect digital systems from threats; - Developing programs and techniques used in the educational sector, and focusing on developing the capabilities of individuals to ensure optimal use of technology; - Finding energy and environmental solutions to ensure optimum use of available resources. Originality/Value: This study sheds light on the importance of modern technology-based start-ups in promoting the development of smart cities. Dubai has been chosen as a model for a leading and developing Arab city that ranks first globally compared to other smart cities, and can be used as a successful experiment. In addition, the importance of both start-ups and smart cities, which are the pillars for the economic development of countries, they rely on advanced technology and the swift provision of services, ultimately facilitating life and enhancing the standard of living for their citizens.

Exploring and analyzing the role of hybrid spectrum sensing methods in 6G-based smart health care applications.

Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world's rapid technological improvements. One of the best options is smart healthcare, which uses a variety of on-body and off-body sensors and gadgets to monitor patients' health and exchange data with hospitals and healthcare professionals in real time. Utilizing the primary user (PU) spectrum, cognitive radio (CR) can be highly useful for efficient and intelligent healthcare systems to send and receive patient health data. In this work, we propose a method that combines energy detection (ED) and cyclostationary (CS) spectrum sensing (SS) algorithms. This method was used to test spectrum sensing in CR-based smart healthcare systems. The proposed ED-CS in cognitive radio systems improves the precision of the spectrum sensing. Owing to its straightforward implementation, ED is initially used to identify the idle spectrum. If the ED cannot find the idle spectrum, the signals are found using CS-SS, which uses the cyclic statistical properties of the signals to separate the main users from the interference. In the simulation analysis, the probability of detection (Pd), probability of a false alarm (Pfa), power spectral density (PSD), and bit error rate (BER) of the proposed ED-CS is compared to those of the traditional Matched Filter (MF), ED, and CS. The results indicate that the suggested strategy improves the performance of the framework, making it more appropriate for smart healthcare applications.

Smart Health Monitoring with Power-Aware IoT Using a Wireless Body Area Network

Smart health monitoring systems have become increasingly popular in recent years due to the growth of the Internet of Things (IoT) technology and its applications in healthcare. This project proposes an IoT-based smart health monitoring system that utilises an ESP32 Microcontroller, MAX30100 Pulse Oximeter Heart Rate Sensor Module, MLX90614 ESF Non-Contact Human Body Infrared Temperature Measurement Module, LCD 16*2, 3.7v 2000mAh Lithium Battery, TP4056 1A Li-Ion Lithium Battery charging Module, and some buttons. The system is designed to monitor the user's heart rate, blood oxygen level, and body temperature in real-time and display the readings on an LCD screen as well as an IoT web page. The project aims to provide an affordable, portable, and user-friendly smart health monitoring solution that can be used at home, in hospitals, or other healthcare settings. The project methodology involved a combination of hardware and software components, including circuit design and programming using the Arduino IDE. The system was tested and evaluated for its performance, accuracy, and usability using a sample population. The results showed that the system was effective in monitoring the user's vital signs and displaying the readings in real-time. The system was found to be accurate and reliable, with a high level of user satisfaction and ease of use. Overall, the IoT-based smart health monitoring system proposed in this project has significant implications for healthcare, particularly in terms of remote monitoring, telemedicine, and personalised healthcare. The system has the potential to improve patient outcomes, reduce healthcare costs, and increase access to healthcare services. The project also highlights the potential of IoT technology in healthcare and the need for further research and development in this area

Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress Detection.

Smartwatch health sensor data are increasingly utilized in smart health applications and patient monitoring, including stress detection. However, such medical data often comprise sensitive personal information and are resource-intensive to acquire for research purposes. In response to this challenge, we introduce the privacy-aware synthetization of multi-sensor smartwatch health readings related to moments of stress, employing Generative Adversarial Networks (GANs) and Differential Privacy (DP) safeguards. Our method not only protects patient information but also enhances data availability for research. To ensure its usefulness, we test synthetic data from multiple GANs and employ different data enhancement strategies on an actual stress detection task. Our GAN-based augmentation methods demonstrate significant improvements in model performance, with private DP training scenarios observing an 11.90-15.48% increase in F1-score, while non-private training scenarios still see a 0.45% boost. These results underline the potential of differentially private synthetic data in optimizing utility-privacy trade-offs, especially with the limited availability of real training samples. Through rigorous quality assessments, we confirm the integrity and plausibility of our synthetic data, which, however, are significantly impacted when increasing privacy requirements.

Sleeping more than 8 h: a silent factor contributing to decreased muscle mass in Chinese community-dwelling older adults

BackgroundMuscle mass loss is an age-related process that can be exacerbated by lifestyle, environmental and other factors, but can be mitigated by good sleep. The objective of this study was to investigate the correlation between varying time lags of sleep duration and the decline in muscle mass among individuals aged 60 years or older by using real-world health monitoring data obtained from wearable devices and smart home health monitoring devices.MethodsThis study included 86,037 observations from 2,869 participants in the Mobile Support System database. Missing data were supplemented by multiple imputation. The investigation utilized generalized estimating equations and restricted cubic spline curve to examine the relationship between sleep duration and low muscle mass. Various lag structures, including 0, 1, 2, 0–1, 0–2, and 1–2 months, were fitted, and the interaction effect of observation time with sleep duration was estimated for each lag structure. Additionally, subgroup analyses were conducted. The models were adjusted for various covariates, including gender, age, body mass index, footsteps, smoking status, drinking status, marital status, number of chronic diseases, number of medications, diabetes mellitus, hyperlipidemia, coronary artery disease, respiratory disease, and musculoskeletal disease and an interaction term between time and sleep duration.ResultsThe results of the generalized estimating equation showed a significant correlation (p < 0.001) between sleep duration of 8 h or more and low muscle mass in older adults, using 6–7 h of sleep as a reference. This effect was seen over time and prolonged sleep accumulated over multiple months had a greater effect on muscle mass loss than a single month. The effect of long sleep duration on muscle mass loss was significantly greater in females than in males and greater in the over-75 than in the under-75 age group. Restricted cubic spline plots showed a non-linear relationship between sleep duration and low muscle mass (p < 0.001).ConclusionsThis study found an association between sustained nighttime sleep of more than eight hours and decreased muscle mass in older adults, especially older women.

Advancement in AI-Based Devices for Monitoring Heart Health in Patients with Cardiac Conditions

In the contemporary landscape of cardiac healthcare, the integration of Artificial Intelligence (AI) into monitoring devices represents a groundbreaking shift towards enhancing patient outcomes and streamlining the management of heart health. This review paper delves into the burgeoning domain of AI-based devices designed for the meticulous monitoring of heart patients, elucidating the methodologies behind their operation, the breadth of their applications, and the palpable impact they have had on both clinical practices and patient experiences. Through a meticulous selection and analysis of current studies, we uncover the transformative capabilities of AI technologies in detecting and predicting heart-related anomalies, offering a personalized approach to cardiac care. The findings underscore the paramount importance of AI in facilitating early intervention, reducing the incidence of heart-related morbacies, and ultimately, charting a course towards a more responsive and adaptive healthcare ecosystem. The significance of this review lies not only in its comprehensive synthesis of existing research but also in its exploration of future potentials, highlighting the ongoing evolution of AI as a pivotal ally in the quest for improved heart health monitoring. This paper aims to contribute to the academic and medical discourse surrounding AI applications in healthcare, offering insights that may guide future innovations and research directions. Keywords--- AI-driven Cardiac Monitoring, Wearable Health Technology, Remote Heart Health Monitoring, Smart Health Devices, Cardiovascular Disease Management, Machine Learning in, Cardiology, Digital Health Solutions, Real-time Heart Health Analytics, Sensor Technology in Cardiac Care, Patient-Centric Heart Monitoring, AI-based Cardiac Diagnostics, Continuous Heart Rate Monitoring, Mobile Health Applications, Personalized Cardiac Care, Telemedicine in Cardiology.

Measuring Vital Signs such as Temperature, BPM, Humidity using different IOT Sensors and Designing a Mobile Application using Firebase

Abstract: This research describes the creation of an Internet of Things-based smart health surveillance system that allows for the real-time data tracking of a person's vital signs. An ESP8266 Node MCU microcontroller is integrated with temperature, humidity, and heart rate sensors in the system to gather information on the user's body temperature, ambient humidity, and heart rate. A specially created Android mobile application shows the gathered data, which is wirelessly sent to a Firebase cloud database. With the use of the mobile app, users can easily create individualized profiles, view vital sign data, and get warnings. Early health issue diagnosis and proactive illness management are aided by successful implementation, which shows that lowcost IoT devices, cloud computing, and mobile technologies may all be used for effective remote health monitoring

The Study on Security in Internet of Things (IoT)

The Internet of Things, or IoT, has received a lot of attention lately since it has drastically altered human existence. In a wide range of applications, including smart buildings, smart health, smart transportation, and more, the Internet of Things facilitates the sharing of information or data. Sensitive information that may be disclosed is interchangeable among billions of interconnected objects. Thus, maintaining user privacy and bolstering IoT security present a significant issue. This paper aims to provide an in-depth analysis of IoT security. A taxonomy of security requirements depending on the objectives of the attacks is proposed after a number of IoT security threats are studied. In addition, current security solutions are categorised and characterised according to their use cases. In the end, open research directions and security issues are talked about. Keyword: Internet of Things (IOT), Wireless Sensor, Security, Privacy, Issues Networks.

“Research on the construction of internet based aerobics competitive ability assessment model”

A vast field that focuses on restoring motor abilities through exercise is known as sports rehabilitation training, which is also sometimes referred to as recovery athletic exercise. A background in sporting recovery can assist competitors in getting back into peak physical condition, recovering from injuries, preventing additional damage, and better coping with the demands of regular practice. The importance of sports rehabilitation teaching for physical activity is analyzed and discussed at length throughout this research. The definition of sports therapy is presented first, followed by an examination of how it might be used to fitness. Make use of this information as a reference to discover how to include physical therapy in the workout plan you use for sports conditioning. Many tried-and-true algorithms for monitoring patients' vitals over time have emerged as a result of widespread use and advancements in IHM technology, which has led to an increase in the number of such algorithms. The purpose of this research is to investigate the ways in which intelligent health monitoring systems can be applied to the process of developing rehab methods for athletes. It not only provides the structure for a smart wellness terminal, but it also offers the usual ways for monitoring a moving object. Methods such as this include the optical flow strategy, the frame contrast technique, and the background difference method, to name just a few examples. The process of rehabilitation that an athlete went through in X city is presented as an example to show the processes that are included in an ideal study. According to the findings of the study, 92.6 percent of the athletes with injuries were able to make a full recovery with the support of rehabilitative physical exercise. The researchers were able to determine this by keeping close track on the athletes' vital signs in real time using sophisticated health monitoring equipment.

Smart Health Village in Improving Disaster Preparedness

Background: Disaster-prone areas are areas that have the potential for natural disasters such as earthquakes, mountain eruptions, landslides caused by activity or movement from the base of the earth. Understanding the potential for natural disasters in each region as one of the anticipatory steps to minimize the number of victims of natural disasters. Precise and accurate information is needed for disaster management. The slope of the area is quite steep, and road access to villages and tourist attractions is vulnerable to landslides. The current disaster incident information reporting system still applies the conventional model. The community also experiences difficulties in receiving information and submitting reports regarding village conditions. Purpose: This research aims to analyze the effectiveness of smart health villages in improving disaster preparedness. Method: The research design used a pre-experiment with a pre-test and post-test design approach with a sample size of 64 volunteers who were taken using purposive sampling. The independent variable in this study is Smart Health Village and the dependent variable is the voluntary task force's ability to preparedness disaster. The research instrument used a questionnaire for each variable. Data analysis in this study is the Wilcoxon Signed Rank Test. The implementation of this activity consists of the design stage, application creation, system design, socialization and training, and trial. Results: The research results showed that the voluntary task force's preparedness was in a good category as much as 88% and as enough as 12% with p value = 0.000. This application system smart health village based on the web allows both the community (users), admin (task force), and verifiers (village officials) to access anywhere and at any time, thus increasing the capability and quality of human resources in disaster preparedness and disaster-prone areas. Conclusion: Based on the results it can be concluded that smart health village can improve ability in preparedness of disaster in disaster- prone areas thus reducing impact of anxiety and and panic due to disaster.

Association between indoor temperature during winter and falls at home in the past year among community-dwelling older adults: A cross-sectional analysis of the nationwide Smart Wellness Housing survey in Japan

Some studies have reported a higher incidence of falls during winter with similar proportions of indoor and outdoor falls. We investigated the relationship between indoor temperature during winter and falls at home in the past year among community-dwelling older adults. This cross-sectional study enrolled 964 individuals of ≥65 years of age in Japan. Participants answered questions about falls (including trips) at home within the past year, and the living room temperature was measured for 2 weeks during winter. Participants were divided into those living in cold (mean temperature near the floor <12°C), slightly cold (12-17.9°C), and warm (≥18°C) houses. The association between indoor temperature (cold vs. slightly cold vs. warm houses) and falls at home in the past year was examined using a logistic regression analysis adjusted for potential confounding factors. Valid data were obtained from 907 participants (mean age: 72.0±6.3 years), of whom 265,553, and 89 lived in cold, slightly cold, and warm houses, respectively. In the past year, falls occurred once in 325 (35.8%) participants and multiple times in 148 (16.3%) participants. In warm houses, the odds ratio of falling once and multiple times in the past year was 0.49 (p=0.032) and 0.34 (p=0.035), respectively, in comparison to cold houses. Living in cold houses may be associated with an increased risk of falling at home among older adults. Maintaining an appropriate indoor thermal environment during winter may reduce the risk of falling among individuals who spend most of their time at home.

Intelligent Health Monitoring in Smart Homes

Intelligent Health Monitoring in Smart Homes is a burgeoning field that integrates advanced technologies to revolutionize healthcare delivery within residential environments. This paper explores the concept of smart homes as proactive health monitoring ecosystems, leveraging interconnected devices, sensors, and data analytics to monitor residents' health parameters and behaviors in real-time. Through a comprehensive review of existing literature and case studies, this study elucidates the potential benefits, challenges, and emerging trends in intelligent health monitoring within smart home environments.Key components of intelligent health monitoring systems, including wearable devices, ambient sensors, and machine learning algorithms, are examined in detail, highlighting their roles in detecting early signs of health deterioration, predicting health-related events, and facilitating timely interventions. Furthermore, the integration of telemedicine platforms and communication technologies enhances remote patient monitoring and enables seamless interaction between residents and healthcare providers.The paper also addresses critical considerations such as privacy, data security, and interoperability standards to ensure the ethical and responsible implementation of smart health monitoring solutions. By fostering collaboration among healthcare professionals, technology developers, and policymakers, intelligent health monitoring in smart homes has the potential to improve health outcomes, enhance quality of life, and reduce healthcare costs. The integration of artificial intelligence, Internet of Things (IoT) devices, and cloud computing enables seamless communication and coordination among various components of the smart home ecosystem. Through real-time monitoring and analysis, potential health risks can be identified early, enabling timely interventions and proactive healthcare management. Additionally, the ability to remotely access and share health data facilitates collaborative care and empowers individuals to take control of their own health and well-being. However, the widespread adoption of intelligent health monitoring in smart homes also raises important ethical, privacy, and regulatory considerations. Safeguarding the security and privacy of sensitive health data, ensuring transparency and accountability in algorithmic decision-making, and promoting equitable access to technology are essential aspects that require careful attention and proactive measures. In conclusion, Intelligent Health Monitoring in Smart Homes holds tremendous potential to revolutionize healthcare delivery, promoting preventive care, empowering individuals, and ultimately improving health outcomes. By addressing technical, ethical, and regulatory challenges, this transformative approach can pave the way for a more connected, proactive, and personalized healthcare experience.This paper studies recent state-of-the-art research on the field of IoT for health monitoring and smart homes, examines several potential use-cases of blending the technology, and proposes integration with an existing smart home test bed for further study. Challenges of adoption and future research on the topic are also discussed, Keywords: Intelligent Health Monitoring, Smart Homes, Healthcare, IoT, Machine Learning, Data Analytics, Remote Monitoring, Proactive Healthcare, Privacy, Ethics, Regulatory Compliance.

Internet of Things Based-Early Diagnosis System of Diabetes Using Optimized Neural Network

One of the most common diseases today is diabetes. This disease has an initial phase, if it is diagnosed on time, it helps to control and treat this disease to a great extent. But early diagnosis of this disease is very challenging due to the difficulty of repeated tests, especially in some geographical areas. Therefore, in smart health systems, a series of information is frequently taken from users by mobile phone, and by analyzing this information using artificial intelligence and machine learning algorithms and with the help of the Internet of Things, the doctor can diagnose diabetes in the first steps remotely. In this paper, we used a database that has 16 features related to early diabetes. Our proposed method for early diagnosis of diabetes is based on feature ranking along with MLP neural network optimized with whale optimization algorithm (WOA). also, the NCA algorithm is used to rank the features, and the WOA algorithm is used to optimize the parameters of the MLP neural network. Finally, based on the simulation results of the proposed method, we achieved 98.1% accuracy in the early diagnosis of diabetes.

Health Monitoring System on Patient Through IOT Nibe Sakshi Babasaheb,

Advancement in technology makes great efforts in health care system. Smart health monitoring will reduce effort of people by automating the patient monitoring task which improves patient work flow management. This study reviews enhancement in health care system. It focuses on portable, wearable and android technology based health monitoring system. This study reveals different approaches for health monitoring. Keywords—mobile health monitoring, wearable health monitoring, remote health monitoring