Wearable Internet Of Things Devices Research Articles

Mathematical Framework for Wearable Devices in the Internet of Things Using Deep Learning.

To avoid dire situations, the medical sector must develop various methods for quickly and accurately identifying infections in remote regions. The primary goal of the proposed work is to create a wearable device that uses the Internet of Things (IoT) to carry out several monitoring tasks. To decrease the amount of communication loss as well as the amount of time required to wait before detection and improve detection quality, the designed wearable device is also operated with a multi-objective framework. Additionally, a design method for wearable IoT devices is established, utilizing distinct mathematical approaches to solve these objectives. As a result, the monitored parametric values are saved in a different IoT application platform. Since the proposed study focuses on a multi-objective framework, state design and deep learning (DL) optimization techniques are combined, reducing the complexity of detection in wearable technology. Wearable devices with IoT processes have even been included in current methods. However, a solution cannot be duplicated using mathematical approaches and optimization strategies. Therefore, developed wearable gadgets can be applied to real-time medical applications for fast remote monitoring of an individual. Additionally, the proposed technique is tested in real-time, and an IoT simulation tool is utilized to track the compared experimental results under five different situations. In all of the case studies that were examined, the planned method performs better than the current state-of-the-art methods.

A smart IoT-enabled heart disease monitoring system using meta-heuristic-based Fuzzy-LSTM model

A continuous heart disease monitoring system is one of the significant applications specified by the Internet of Things (IoT). This goal might be achieved by combining sophisticated expert systems with extensive healthcare data on heart diseases. Several machine learning-based methods have recently been proven for predicting and diagnosing cardiac illness. However, these algorithms are unable to manage high-dimensional information due to the lack of a smart framework that can combine several sources to anticipate cardiac illness. The Fuzzy-Long Short Term Memory (LSTM) model is used in this work to present a unique IoT-enabled heart disease prediction method. The benchmark data for the experiment came from public sources and collected via wearable IoT devices. An improved Harris Hawks Optimization (HHO) called Population and Fitness-based HHO (PF-HHO) is utilized to select the best features, with the objective function of correlation maximization within the same class and correlation minimization among different classes. The scientific contributions of the health care monitoring system are depicted here that help to improve heart disease healthcare efficiency and also it can be reducing the death rate in the current world. The important section of this persistent healthcare mode is the real-world monitoring system. The simulation outcomes proved that the recommended approach is more successful at predicting heart illness than existing technologies.

Flexible and Semi-Transparent Silicon Solar Cells as a Power Supply to Smart Contact Lenses.

Supplying electric power to wearable IoT devices, particularly smart contact lenses (SCLs), is one of the main obstacles to widespread adoption and commercialization. In the present study, we have successfully designed, fabricated, and characterized semi-transparent, self-supported, and flexible single crystalline silicon solar cells using a single-sided micromachining procedure. Optical, mechanical, and electrical simulations, together with the practical measurements, verify the application of our developed solar cells to be mounted on a limited-footprint and flexible SCL. The 15 μm-thick silicon solar cells conformally fit on a dome-shaped contact lens (ROC = 8 mm) without any mechanical and electrical degradation. This homojunction photovoltaic device containing an array of micro-holes exhibits a V oc, J sc, and maximum power density of 504 mV, 6.48 mA cm-2, and 1.67 mW cm-2, respectively, at 25% visible light transparency under an AM1.5 one sun condition. Furthermore, the measurements were conducted under low-intensity indoor light conditions and resulted in a maximum power output of 25 and 42 μW cm-2 for the 50 and 25% transparent solar cells, respectively.

AN IOT-BASED WEARABLE FALL DETECTION SYSTEM AND ALERTING SYSTEM

Seamless monitoring is one of the major challenges for human community because of increasing the strength of sixty years old people and occurrence of many accidents in falls-risk related works like construction, manufacture and others. Wearable IoT devices are useful for continuous health monitoring, analyzing the behaviours, and fall detection. Falls are one of the foremost causes for injuries and death in our life. Present work proposes the human movement monitoring by using the pressure sensor for avoiding human falls and also the health report prepared from historical data’s uploaded in cloud by support-vector machine (SVM) algorithm. The proposed wearable IoT device for seamless monitoring system is proving 99% accuracy, less complex and compatible to use over other IoT wearable devices.

Wearable Antennas for Sensor Networks and IoT Applications: Evaluation of SAR and Biological Effects.

In recent years, there has been a rapid development in the wearable industry. The growing number of wearables has led to the demand for new lightweight, flexible wearable antennas. In order to be applicable in IoT wearable devices, the antennas must meet certain electrical, mechanical, manufacturing, and safety requirements (e.g., specific absorption rate (SAR) below worldwide limits). However, the assessment of SAR does not provide information on the mechanisms of interaction between low-intensity electromagnetic fields emitted by wearable antennas and the human body. In this paper, we presented a detailed investigation of the SAR induced in erythrocyte suspensions from a fully textile wearable antenna at realistic (net input power 6.3 mW) and conservative (net input power 450 mW) conditions at 2.41 GHz, as well as results from in vitro experiments on the stability of human erythrocyte membranes at both exposure conditions. The detailed investigation showed that the 1 g average SARs were 0.5758 W/kg and 41.13 W/kg, respectively. Results from the in vitro experiments demonstrated that the short-term (20 min) irradiation of erythrocyte membranes in the reactive near-field of the wearable antenna at 6.3 mW input power had a stabilizing effect. Long-term exposure (120 min) had a destabilizing effect on the erythrocyte membrane.

Energy-Accuracy Aware Finger Gesture Recognition for Wearable IoT Devices.

Wearable Internet of Things (IoT) devices can be used efficiently for gesture recognition applications. The nature of these applications requires high recognition accuracy with low energy consumption, which is not easy to solve at the same time. In this paper, we design a finger gesture recognition system using a wearable IoT device. The proposed recognition system uses a light-weight multi-layer perceptron (MLP) classifier which can be implemented even on a low-end micro controller unit (MCU), with a 2-axes flex sensor. To achieve high recognition accuracy with low energy consumption, we first design a framework for the finger gesture recognition system including its components, followed by system-level performance and energy models. Then, we analyze system-level accuracy and energy optimization issues, and explore the numerous design choices to finally achieve energy–accuracy aware finger gesture recognition, targeting four commonly used low-end MCUs. Our extensive simulation and measurements using prototypes demonstrate that the proposed design achieves up to 95.5% recognition accuracy with energy consumption under 2.74 mJ per gesture on a low-end embedded wearable IoT device. We also provide the Pareto-optimal designs among a total of 159 design choices to achieve energy–accuracy aware design points under given energy or accuracy constraints.

Determinants of user acceptance of wearable IoT devices

Determinants of user acceptance of wearable IoT devices

Novel Stacking Classification and Prediction Algorithm Based Ambient Assisted Living for Elderly

Recognition of human activity is a significant area of research with numerous uses. In developed countries, the rising age of citizens requires the improvement of the medical service structure, which raises the price of resources, both financial and human. In that sense, ambient assisted living (AAL) is a relatively novel information and communication technology (ICT) that presents services and recognizes various products that enable older people and the disabled to live autonomously and improve their quality of life. It further assists in reducing the cost of hospital services. In the AAL environment, various sensors and devices are fixed to gather a broad range of data. Moreover, AAL will be the motivating technology for the latest care models by acting as an adjunct. This will become thought-provoking research in a fast-growing world, but exploring different ADL and self-classification will become a major challenge. This paper proposed a Novel Stacking Classification and Prediction (NSCP) algorithm based AAL for the elderly with Multi-strategy Combination based Feature Selection (MCFS) and Novel Clustering Aggregation (NCA) algorithms. This paper’s main aim is to recognize the activity of older people, such as standing, walking, sitting, falling, cramps, and running. The dataset is derived from the Kaggle repository, which refers to data collection from wearable IoT devices. The experimental outcomes demonstrate that the MCFS, NCA, and NSCP algorithms work more efficiently than existing feature selection, clustering, and classification algorithms, respectively, regarding the accuracy, sensitivity, specificity, precision, recall, F-measure, and execution time dataset size and the number of features. Furthermore, the NSCP algorithm provided high accuracy, precision, recall, and F-measure are 98%, 0.96, 0.95, and 0.98, respectively.

Millimeter-Wave Power Transmission for Compact and Large-Area Wearable IoT Devices Based on a Higher Order Mode Wearable Antenna

Owing to the shorter wavelength in the millimeter-wave (mmWave) spectrum, miniaturized antennas can receive power with a higher efficiency than UHF bands, promising sustainable mmWave-powered Internet of Things (IoT) devices. Nevertheless, the performance of a mmWave power receiver has not been compared, numerically or experimentally, to its sub6-GHz counterpart. In this article, the performance of mmWave-powered receivers is evaluated based on a novel wearable textile-based higher order mode microstrip antenna, showing the benefits of wireless power transmission (WPT). First, a broadband antenna is proposed maintaining a stable wearable measured bandwidth from 24.9 to 31.1 GHz, over threefold improvement compared to a conventional patch. The proposed antenna has a measured 8.2 dBi co-polarized gain with the highest thickness-normalized efficiency of a wearable antenna. When evaluated for compact power receivers, the measured path gain shows that WPT at 26 GHz outperforms 2.4 GHz by 11 dB. A rectenna array based on the proposed antenna is then evaluated analytically showing the potential for up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.3\times $ </tex-math></inline-formula> higher power reception compared to a UHF patch, based on the proposed antenna’s gain and an empirical path-loss model. Both use cases demonstrate that mmWave-powered rectennas are suitable for area-constrained and large-area wearable IoT applications.

Scrybe: A Secure Audit Trail for Clinical Trial Data Fusion.

Clinical trials are a multi-billion dollar industry. One of the biggest challenges facing the clinical trial research community is satisfying Part 11 of Title 21 of the Code of Federal Regulations [7] and ISO 27789 [40]. These controls provide audit requirements that guarantee the reliability of the data contained in the electronic records. Context-aware smart devices and wearable IoT devices have become increasingly common in clinical trials. Electronic Data Capture (EDC) and Clinical Data Management Systems (CDMS) do not currently address the new challenges introduced using these devices. The healthcare digital threat landscape is continually evolving, and the prevalence of sensor fusion and wearable devices compounds the growing attack surface. We propose Scrybe, a permissioned blockchain, to store proof of clinical trial data provenance. We illustrate how Scrybe addresses each control and the limitations of the Ethereum-based blockchains. Finally, we provide a proof-of-concept integration with REDCap to show tamper resistance.

Flexible and Wearable Hybrid RF and Solar Energy Harvesting System

In this article, we demonstrate a flexible and wearable hybrid radio frequency (RF) and solar energy harvesting system for powering wearable electronic devices. The system consists of a flexible transparent antenna, a flexible transparent rectifying circuit, and an amorphous silicon solar cell. By utilizing transparent stacked structure, the rectenna and the solar cell can provide more hybrid output power to accommodate more application scenarios. The flexible transparent antenna shows two impedance matching bandwidths of 3.5–3.578 and 4.79–5.09 GHz, covering n78/n79 fifth-generation (5G) communication frequency band and 5-GHz WiFi frequency band. At the frequency of 3.5 GHz, the flexible transparent rectifying circuit achieves a high RF-to-dc conversion efficiency of 54.67% at 13 dBm RF input power. In the room with a light intensity of 210 lux, compared with a single solar cell, the hybrid energy harvesting system can obtain an additional 35.6%–769.5% output power when the RF source power is varied from 4 to 10 dBm. These results demonstrate that the proposed flexible and wearable RF-solar energy harvester can provide reliable electric power supply, which can be a potential candidate for powering wearable electronic devices, distributed sensors and IOT devices.

Remotely Monitoring COVID-19 Patient Health Condition Using Metaheuristics Convolute Networks from IoT-Based Wearable Device Health Data.

Today, COVID-19-patient health monitoring and management are major public health challenges for technologies. This research monitored COVID-19 patients by using the Internet of Things. IoT-based collected real-time GPS helps alert the patient automatically to reduce risk factors. Wearable IoT devices are attached to the human body, interconnected with edge nodes, to investigate data for making health-condition decisions. This system uses the wearable IoT sensor, cloud, and web layers to explore the patient’s health condition remotely. Every layer has specific functionality in the COVID-19 symptoms’ monitoring process. The first layer collects the patient health information, which is transferred to the second layer that stores that data in the cloud. The network examines health data and alerts the patients, thus helping users take immediate actions. Finally, the web layer notifies family members to take appropriate steps. This optimized deep-learning model allows for the management and monitoring for further analysis.

Learning Your Heart Actions From Pulse: ECG Waveform Reconstruction From PPG

This article studies the relation between electrocardiogram (ECG) and photoplethysmogram (PPG) and investigates the inference of the ECG waveforms from the PPG signals that can be obtained from affordable wearable Internet-of-Things (IoT) devices for mobile health. In order to address this inverse problem, a transform is proposed to map the discrete cosine transform (DCT) coefficients of each PPG cycle to those of the corresponding ECG cycle based on the proposed cardiovascular signal model. The proposed method is evaluated with different morphologies of the PPG and ECG signals on three benchmark data sets with a variety of combinations of age, weight, and health conditions under several training setups. The experimental results show that the proposed method can achieve a high prediction accuracy greater than 0.92 in averaged correlation for each data set when the model is trained subjectwise. With a signal processing and learning system that is designed synergistically, we are able to reconstruct ECG signals by exploiting the relation of these two types of cardiovascular measurement. The reconstruction capability of the proposed method can enable low-cost ECG screening from affordable wearable IoT devices for continuous and long-term monitoring. This work opens up a new research direction to transfer the clinical ECG knowledge base to build a knowledge base for PPG and sensing data from wearable devices.

Data Privacy in Wearable IoT Devices: Anonymization and Deanonymization

With the development of IoT devices, wearable devices are being used to record various types of information. Wearable IoT devices are attached to the user and can collect and transmit user data at all times along with a smartphone. In particular, sensitive information such as location information has an essential value in terms of privacy, and therefore some IoT devices implement data protection by introducing methods such as masking. However, masking can only protect privacy to a certain extent in logs having large numbers of recorded data. However, the effectiveness may decrease if we are linked with other information collected from within the device. Herein, a scenario-based case study on deanonymizing anonymized location information based on logs stored in wearable devices is described. As a result, we combined contextual and direct evidence from the collected information. It was possible to obtain the result in which the user could effectively identify the actual location. Through this study, not only can a deanonymized user location be identified but we can also confirm that cross-validation is possible even when dealing with modified GPS coordinates.

Internet of things-assisted advanced dynamic information processing system for physical education system.

In recent years the Internet of Things (IoT) has become a popular technological culture in the physical education system. Though several technologies have grown in the physical education system domain, IoT plays a significant role due to its optimized health information processing framework for students during workouts. In this paper, an advanced dynamic information processing system (ADIPS) has been proposed with IoT assistance to explore the traditional design architecture for physical activity tracking. To track and evaluate human physical activity in day-to-day living, a new paradigm has been integrated with wearable IoT devices for effective information processing during physical workouts. Continuous observation and review of the condition and operations of various students by ADIPS helps to evaluate the sensed information to analyze the health condition of the students. The result of ADIPS has been implemented based on the performance factor correlation with the traditional system.

Dynamic data processing system for sports training system using internet of things.

The Internet of Things (IoT) has recently become a prevalent technological culture in the sports training system. Although numerous technologies have grown in the sports training system domain, IoT plays a substantial role in its optimized health data processing framework for athletes during workouts. In this paper, a Dynamic data processing system (DDPS) has been suggested with IoT assistance to explore the conventional design architecture for sports training tracking. To track and estimate sportspersons physical activity in day-to-day living, a new paradigm has been combined with wearable IoT devices for efficient data processing during physical workouts. Uninterrupted observation and review of different sportspersons condition and operations by DDPS helps to assess the sensed data to analyze the sportspersons health condition. Additionally, Deep Neural Network (DNN) has been presented to extract important sports activity features. The numerical results show that the suggested DDPS method enhances the accuracy of 94.3%, an efficiency ratio of 98.2, less delay of 24.6%, error range 28.8%, and energy utilization of 31.2% compared to other existing methods.

Internet of things-based energy-efficient optimized heuristic framework to monitor sportsperson's health.

Recently, wearable technologies have gained attention in diverse applications of the medical platform to guarantee the health and safety of the sportsperson with the assistance of the Internet of things (IoT) device. The IoT device's topology varies due to the shift in users' orientation and accessibility, making it impossible to assign resources, and routing strategies have been considered the prominent factor in the current medical research. Further, for sportspersons with sudden cardiac arrests, hospital survival rates are low in which wearable IoT devices play a significant role. In this paper, the energy efficient optimized heuristic framework (EEOHF) has been proposed and implemented on a wearable device of the sportsperson's health monitoring system. The monitoring system has been designed with cloud assistance to locate the nearest health centers during an emergency. The wearable sensor technologies have been used with an optimized energy-efficient algorithm that helps athletes monitor their health during physical workouts. The monitoring system has fitness tracking devices, in which health information is gathered, and workout logs are tracked using EEOHF. The proposed method is applied to evaluate and track the sportsperson's fitness based on case study analysis. The simulation results have been analyzed, and the proposed EEOHF achieves a high accuracy ratio of 97.8%, a performance ratio of 95.3%, and less energy consumption of 9.4%, delay of 13.1%, and an average runtime of 98.2% when compared to other existing methods.

STUDY ON IOT BASED WOMEN SAFETY DEVICES WITH SCREAMING DETECTION AND VIDEO CAPTURING

To propose an idea for implementing wearable IoT devices using the Raspberry Pi, which is equipped with a sound sensor, camera module, GPS, and GSM. When the sound sensor detects screaming, the SVM (support vector machine) algorithm in machine learning eliminates unnecessary noise and evaluates only the victim screaming. After assessing the victim's scream, the gadget promptly activates the camera module, which takes 30 seconds of footage. GPS will track the geographical coordinates and, using the GSM module, will send an alert message and an emergency call to the nearest police station. When the victim activates the switch in the second scenario, the camera module is immediately activated, and a 30-second clip is captured. The GPS will be active, and GSM will send an alert message with the status. The idea's major goal is to present a smart gadget for a woman that is entirely comfy and portable. When compared to other existing safety devices, the first and most crucial difference is the smart band's ability to shrink the size of gadgets.

Wearable IoT intelligent recommender framework for a smarter healthcare approach

PurposeThe increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.Design/methodology/approachThis data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.FindingsThe proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.Research limitations/implicationsThe research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.Practical implicationsThe emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.Originality/valueIn this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

C-shaped antenna based artificial magnetic conductor structure for wearable IoT healthcare devices

A wearable C-shaped antenna based on a fabric material operating at 2.4 GHz frequency is proposed for use in flexible/wearable IoT medical systems. The wearable IoT device plays a key role in medical applications, and the antenna is a key part of it. Loading the presented antenna on the body models showed a frequency detuned with the gain and efficiency reduced from 1. 28 to −9 dB and 90% to 10%. In addition, the SAR did not meet the safety health requirement defined by the FCC or ICNIRP standards. Therefore, an “Artificial Magnetic Conductor” structure (AMC) is added to the C-shaped antenna to overcome these problems. The AMC acts as shielding material between the human skin and the presented antenna because of its 0° reflection phase, which mimics the action of the Perfect Magnetic Conductor (PMC). The overall size of the proposed design was 54 × 54 × 3.9 mm3. Numerical and experimental findings indicated that integrating the AMC structures with a C-shaped antenna was robust for body deformation and load. The C-shaped antenna worked equally well with the AMC, whether positioned in free space or on the chest or the arm of the human body. The integrated antenna with AMC structures has excellent performances. The gain and efficiency without loading on the chest were 6.49 dB and 84%, respectively. While for loaded on the chest were 6.21 dB and 81%, respectively. It also decreased the back radiation and raised the Front to Back Ration (FBR) by 13.8 dB. SAR levels have been reduced by more than 90% between the FCC and ICNIRP standards compared to the C-shaped antenna alone, which does not comply with the standards. As a result, the C-shaped integration with AMC structures is highly suitable for assembly in any wearable system.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11276-021-02770-4.