Wearable Internet Of Things Devices Research Articles

A Hybrid IoT-Enabled Transfer Learning Model for Accurate Diagnosis of Coronary Heart Disease

Coronary heart disease (CHD) is the top cause of death in the world, which shows how important it is to get a correct evaluation as soon as possible. The Internet of Things (IoT) opens up new ways to improve healthcare, and transfer learning has shown promise in making machine learning models work better, especially when it comes to medical analysis. IoT-Enabled Transfer Learning Model (HIETLM), which is new, is proposed in this study as a way to accurately diagnose congenital heart disease. Wearable IoT devices are used by the HIETLM to continuously collect vital data from patients, such as their heart rate, blood pressure, and level of exercise. These pieces of information are sent to a central computer to be processed and analyzed. The suggested model has two main parts: a base model that was learned on a big external dataset, and a transfer learning part that was fine-tuned using data from a single patient. The base model has already been trained on a big set of general health data to learn general trends and traits that are useful for diagnosing CHD. This training helps the model understand complicated patterns and connections that are hard to learn from a small collection. The transfer learning part then tweaks the base model using the patient-specific data to make it fit the unique traits of each patient. To test how well the HIETLM worked, we did tests with a group of patients who were thought to have congenital heart disease. From the data, we can see that the HIETLM is more accurate, sensitive, and detailed than other models. The suggested model could help make CHD diagnoses more accurate and faster, which would improve patient results and lower healthcare costs.

A Hybrid IoT-Enabled Transfer Learning Model for Accurate Diagnosis of Coronary Heart Disease

Coronary heart disease (CHD) is the top cause of death in the world, which shows how important it is to get a correct evaluation as soon as possible. The Internet of Things (IoT) opens up new ways to improve healthcare, and transfer learning has shown promise in making machine learning models work better, especially when it comes to medical analysis. IoT-Enabled Transfer Learning Model (HIETLM), which is new, is proposed in this study as a way to accurately diagnose congenital heart disease. Wearable IoT devices are used by the HIETLM to continuously collect vital data from patients, such as their heart rate, blood pressure, and level of exercise. These pieces of information are sent to a central computer to be processed and analyzed. The suggested model has two main parts: a base model that was learned on a big external dataset, and a transfer learning part that was fine-tuned using data from a single patient. The base model has already been trained on a big set of general health data to learn general trends and traits that are useful for diagnosing CHD. This training helps the model understand complicated patterns and connections that are hard to learn from a small collection. The transfer learning part then tweaks the base model using the patient-specific data to make it fit the unique traits of each patient. To test how well the HIETLM worked, we did tests with a group of patients who were thought to have congenital heart disease. From the data, we can see that the HIETLM is more accurate, sensitive, and detailed than other models. The suggested model could help make CHD diagnoses more accurate and faster, which would improve patient results and lower healthcare costs.

A Wearable Internet of Things Device for Noninvasive Remote Monitoring of Vital Signs Related to Heart Failure

Cardiovascular disease is one of the leading causes of death in the world. Heart failure is a cardiovascular disease in which the heart is unable to pump sufficient blood to fulfill the body’s requirements and can lead to fluid overload. Traditional solutions are not adequate to address the progression of heart failure. Herein, we report a body-mounted wearable sensor to monitor the parameters related to heart failure. These include heart rate, blood oxygen saturation, thoracic impedance, and activity status. The device is compact and wearable and measures the parameters continuously in real time. The device is an Internet of Things (IoT) device connected with a cloud-based database enabling the parameters to be visualized on a mobile application.

Cloud-Connected Bracelet for Continuous Monitoring of Parkinson’s Disease Patients: Integrating Advanced Wearable Technologies and Machine Learning

Parkinson’s disease (PD) is one of the most unremitting and dynamic neurodegenerative human diseases. Various wearable IoT devices have emerged for detecting, diagnosing, and quantifying PD, predominantly utilizing inertial sensors and computational algorithms. However, their proliferation poses novel challenges concerning security, privacy, connectivity, and power optimization. Clinically, continuous monitoring of patients’ motor function is imperative for optimizing Levodopa (L-dopa) dosage while mitigating adverse effects and motor activity decline. Tracking motor function alterations between visits is challenging, risking erroneous clinical decisions. Thus, there is a pressing need to furnish medical professionals with an ecosystem facilitating comprehensive Parkinson’s stage evaluation and disease progression monitoring, particularly regarding tremor and bradykinesia. This study endeavors to establish a holistic ecosystem centered around an energy-efficient Wi-Fi-enabled wearable bracelet dubbed A-WEAR. A-WEAR functions as a data collection conduit for Parkinson’s-related motion data, securely transmitting them to the Cloud for storage, processing, and severity estimation via bespoke learning algorithms. The experimental results demonstrate the resilience and effectiveness of the suggested technique, with 86.4% accuracy for bradykinesia and 90.9% accuracy for tremor estimation, along with good sensitivity and specificity for each scoring class. The recommended approach will support the timely determination of the severity of PD and ongoing patient activity monitoring. The system helps medical practitioners in decision making when initially assessing patients with PD and reviewing their progress and the effects of any treatment.

Rational and irrational factors affecting the purchase decision of wearable IoT devices

Rational and irrational factors affecting the purchase decision of wearable IoT devices

Rational and irrational factors affecting the purchase decision of wearable IoT devices

Rational and irrational factors affecting the purchase decision of wearable IoT devices

Estimation of Kinetic Energy Harvesting Potential for Self-Powered Wearable IoT Devices With 67 000 Participants From the UK Biobank

Estimation of Kinetic Energy Harvesting Potential for Self-Powered Wearable IoT Devices With 67 000 Participants From the UK Biobank

Implementation of Wearable IoT Devices for Continuous Physiological Monitoring and Analysis

The development of wearable devices for ongoing physiological monitoring and analysis has been made possible by the emergence of the Internet of Things (IoT), which has completely changed the healthcare industry. This study discusses the use of such wearable IoT devices and their potential to improve healthcare delivery. Our research focuses on the development, manufacture, and implementation of wearable sensors capable of real-time monitoring of vital signs like heart rate, body temperature, blood pressure, and activity levels. These devices leverage the Internet of Things to transmit data wirelessly to a cloud service for further analysis. The core components of these gadgets are miniature sensors, low-power microcontrollers, and wireless communication modules. These developments permit the invisible and continuous collection of data, which aids in the early detection of irregularities and permits urgent medical treatments. Data collected by these devices is processed by sophisticated analytics and machine learning algorithms before being transmitted securely to a cloud-based server. As a result, doctors and nurses are able to anticipate health issues, monitor their patients' physical conditions in real time, and provide individualised care. Los patients have more control over their own medical decisions since they have access to their own data. Frequent use of Internet-connected devices for monitoring and analysing physical phenomena may lead to better medical care, lower hospitalisation costs, and higher quality of life. Additionally, these devices have the potential to revolutionise clinical research by providing extensive real-world data for medical research. These wearable IoT devices have the potential to enhance the management of chronic illnesses, encourage early treatments, and raise general wellbeing. Wearable physiological monitoring technology has a lot of potential for the future of healthcare thanks to the advancements of the internet of things.

Develop the ubiquitous computing wearable IoT devices using IoT-distributed framework

Body Area Sensor Network (BASN) communication methods meet clients' multi-level requirements through Wearable Internet of Things (WIoT) systems. Transparency at management levels, heterogeneous support, and distributed communication to users are requirements of the PCS. Providing essential functions is a serious problem. A flexible Ubiquitous Computing (UC) architecture presented in this study is used to improve the reliability of end-user communication in the WIoT-based health sensor approach. According to this method, demand and sharing variables provide the foundation for the evaluation of resource allocation through recurrent learning. A proposed method could be used to implement end-user mobility management on WIoT healthcare sensors for data transmission, resource allocation and sharing. To show the integrity of the approach, some parameters were used in the actual evaluation and performance predictions. In the ubiquitous ecosystem, the uninterrupted flow was guaranteed to reduce the likelihood of a request being blocked. Memory control to density assignment at the framework level through storage management helps reduce backlogs and failures when allocating resources. The proposed approach enhances storage utilization, bandwidth, and request management besides lowering connection speed, backlogs, and request failure compared to the existing systems.

Dynamic visualization simulation of light motion capture in dance image recognition based on IoT wearable devices

Dynamic visualization simulation of light motion capture in dance image recognition based on IoT wearable devices

Novel Unidirectional Radiation Principle by Backward Field Cancellation and Its Application to Wearable IoT Devices

Novel Unidirectional Radiation Principle by Backward Field Cancellation and Its Application to Wearable IoT Devices

Retracted: Hybrid Optimized GRU-ECNN Models for Gait Recognition with Wearable IOT Devices

Retracted: Hybrid Optimized GRU-ECNN Models for Gait Recognition with Wearable IOT Devices

A machine learning based wearable depression, anxiety and somatisation monitoring IOT system (WDASMS) for the prevention of suicides

Tamil Nadu reportedly is the alternate loftiest rates of pupil self-murder in India. According to report from the National Crime Records Bureau (NCRB) for 2020, about 46 people failed each day by self-murder in the state; further than two of these victims each day were scholars. The government of Indias domestic seminaries for meritorious pastoral children have reportedly witnessed 49 self-murders in just five times and half of the children who killed themselves on the premises were Dalits and Adivasis, Educationalists point at the pressure mounted on scholars for marks and competitive examinations as one of the main reasons. Utmost seminaries have councillors but going to the council has a smirch about it still. Domestic seminaries where these cases are passing more need to be covered more. Children dying by self-murder due to academic pressure are unheard of in other countries. Utmost of the A huge number of enquiries revealed depression and anxiety to be implicit risk factors for self-murder. Heart health is good among students between the ages of 15 and 25. A few physiological parameters are altered by anxiety and depression. Hence, current pulsation is the symptom for psychiatric diseases. Clinical trials have demonstrated that mental reasons, most frequently anxiety, depression, and somatoform illnesses, are present in cases with pulsation. Psychiatric problems like anxiety and somatization are linked to palpitation. A study found that individuals with high levels of both depression and anxiety are 54.77 times more likely to commit suicide, which is a significant increase above individuals with either high levels of anxiety or depression (2.46). (26.32).. Naturally students studying +2 and preparing for high marks are in under depression and it's dragged for two times. When the examinations are nearing and the results are nearing, the combined depression and anxiety factor increases the threat for committing self-murders for84.5. Hence, a system with literacy and decision making medium is needed to constantly monitor the students to take prompt conduct. A wearable IoT device transmits with scholars Id, the pulsation position constantly to the fellow knot. Logistic Retrogression machine literacy algorithm is used for decision making grounded on the probability of circumstance. The authorities are informed in their widgets and prompt conduct can be taken. This system will be more applicable to the boarding seminaries, where the children are far down from their parents and the number of self-murders are high particularly among +2 grade. This system can be used for colourful communities, where the working culture gives depression and anxiety.

Evaluation of wearable sports rehabilitation medical measurement system based on the internet of things

AbstractSports rehabilitation medicine uses sports training and rehab treatment to improve physical function recovery after illnesses, injuries, disabilities, or surgeries. It involves restoring muscle strength, joint range of motion, flexibility, and coordination while reducing pain and soreness, and improving cardiopulmonary function. Wearable IoT devices with sensors and wireless communication tech collect and transmit physiological information for analysis and processing. These devices monitor health, provide exercise training, and assist with rehab in daily wear equipment. The Internet of Things technology can provide a more accurate, convenient and personalized rehabilitation treatment method, accelerating the recovery and rehabilitation process of patients. This project would utilize Internet of Things technology to construct a medical measurement system for sports rehabilitation through measurement algorithms for sports rehabilitation data. Through comparative experiments, the accuracy of the exercise rehabilitation data measurement algorithm based on Internet of Things technology in measuring patient body data was studied and tested. After experimental verification, the actual heart rates of the 10 subjects were 76, 80, 79, 83, 72, 81, 77, 71, 72, and 78, respectively. The data measured using Internet of Things based algorithms were 75, 80, 80, 83, 73, 81, 78, 71, 72, and 77, respectively. Based on this, it could be concluded that the measured values of the algorithm were very close to the actual values, indicating that the algorithm had quite accurate heart rate measurement ability. Therefore, the sports rehabilitation medical testing system constructed by applying this algorithm had strong and accurate data measurement performance.

Transition to Value-Based Care IoT Wearable Devices in Maternity Healthcare for Low-Resource Countries

Transition to Value-Based Care IoT Wearable Devices in Maternity Healthcare for Low-Resource Countries

Federated Learning and Blockchain-Enabled Fog-IoT Platform for Wearables in Predictive Healthcare

Over the years, the popularity and usage of wearable Internet of Things (IoT) devices in several healthcare services are increased. Among the services that benefit from the usage of such devices is predictive analysis, which can improve early diagnosis in e-health. However, due to the limitations of wearable IoT devices, challenges in data privacy, service integrity, and network structure adaptability arose. To address these concerns, we propose a platform using federated learning and private blockchain technology within a fog-IoT network. These technologies have privacy-preserving features securing data within the network. We utilized the fog-IoT network’s distributive structure to create an adaptive network for wearable IoT devices. We designed a testbed to examine the proposed platform’s ability to preserve the integrity of a classifier. According to experimental results, the introduced implementation can effectively preserve a patient’s privacy and a predictive service’s integrity. We further investigated the contributions of other technologies to the security and adaptability of the IoT network. Overall, we proved the feasibility of our platform in addressing significant security and privacy challenges of wearable IoT devices in predictive healthcare through analysis, simulation, and experimentation.

Protecting IoТ Wearable Devices from Electromagnetic Radiation Using Radio-Opaque Fabrics

The number of sources of electromagnetic radiation is increasing every day. In most cases, electromagnetic radiation has a negative effect on the human body, animals, and other living beings. Electromagnetic radiation negatively affects the operation of electronic devices. Due to the electromagnetic radiation of electronic devices, information may leak from them. The negative impact of electromagnetic radiation on the human body provokes a high level of fatigue, headache, heartache, etc. The everyday use of mobile equipment, household magnetic appliances (for example, microwave ovens), and telecommunication networks puts the majority of the planet's population at risk. Otherwise, the external influence of electromagnetic fields on the various sensors used in the IoT can contribute to receiving incorrect data from the sensors. The powerful external influence of the electromagnetic field on devices that process large data sets can contribute to a failure in mathematical calculations. Thus, protection from electromagnetic fields is necessary not only for electronic devices but also for human protection. Radio-opaque fabrics are one of the modern materials for protection from electromagnetic radiation. They can be used to protect stationary objects and wearable IoT devices and to protect people. Industrial samples of foreign and Ukrainian radio-opaque fabrics were analyzed in the work. Ukrainian manufacturers continue to develop new variants of radio-opaque fabrics. The next options for radiopaque fabrics are fabrics G7, G8, and G9. The article proposed and described a method of researching the shielding properties of the fabric. Experimental studies were carried out, and the shielding coefficient was calculated in the frequency range of 50 MHz - 2 GHz for two fabrics G7 and G8. Experimental studies were carried out for two cases of the location of fabric fibers relative to the radiating antenna and field polarization. Plots of dependences of the shielding coefficient for two fabrics with different locations of fabric fibers were presented. A comparative analysis of the shielding coefficients of two fabrics was made, and relevant conclusions were presented.

FusedAR: Energy-Positive Human Activity Recognition Using Kinetic and Solar Signal Fusion

Today’s wearable Internet of Things (IoT) devices, which have been fêted for numerous applications, suffer from the limited lifetime of batteries due to the high power consumption of conventional inertial activity sensors. Recently, kinetic energy harvesters have been employed as a source of energy as well as context information to replace conventional activity sensors. However, the harvested power from human movements using miniaturised kinetic transducers may not be sufficient to enable a perpetual and self-powered activity recognition system. In this paper, we propose a novel mechanism of Fused signal based human Activity Recognition (FusedAR), which employs miniaturised wearable solar and kinetic energy harvesters simultaneously as an energy source as well as an activity sensor. As human activities engender distinct movement patterns, and interact and interfere with the ambient light differently, the kinetic and solar energy harvesting signals incorporate unique information about the underlying activities while generating sufficient power. After detailed experiments, we find that FusedAR which employs both solar and kinetic energy signals, achieves superior activity recognition performance by up to 10%, particularly in outdoor and night-time contexts, and can recognise not only activities but also contexts, compared to the individual energy harvesting signals. Furthermore, our analysis demonstrates that FusedAR, in addition to significant energy generation, consumes up to 22% less power than the highly optimised conventional 3-axis accelerometer-based mechanisms, achieving energy-positive human activity recognition leading towards perpetual, uninterrupted and autonomous operation of wearable IoT devices.

Three-Stage Power Supply System Model for a Wearable IoT Device for COVID-19 Patients.

During the current crisis caused by the COVID-19 pandemic, Wearable IoT (WIoT) health devices have become essential resources for remote monitoring of the main physiological signs affected by this disease. As well as sensors, microprocessor, and wireless communication elements are widely studied, the power supply unit has the same importance for the WIoT technology, since the autonomy of the system between recharges is of great importance. This letter presents the design of the power supply system of a WIoT device capable of monitoring oxygen saturation and body temperature, sending the collected data to an IoT platform. The supply system is based on a three-stage block consisting of a rechargeable battery, battery charge controller, and dc voltage converter. The power supply system is designed and implemented as a prototype in order to test performance and efficiency. The results show that the designed block provides a stable supply voltage avoiding energy losses, which makes it an efficient and rapidly developing system.

Generation of Electrical Energy Using Fish Market Waste Fish Fin from Mechanical Motion for Battery-Less Self—Powered Wearable Sensors and IoT Devices

Generation of Electrical Energy Using Fish Market Waste Fish Fin from Mechanical Motion for Battery-Less Self—Powered Wearable Sensors and IoT Devices