Wearable Sensors Research Articles

A high sensing performance piezoresistive sensor based on TPU/c-MWCNTs/ V2CTX-MXene electrospun film for human motion detection

Since wearable pressure sensors have promising applications in the fields of human physiological signal detection and bionic robots, there is an urgent need for wearable pressure sensors with comprehensive attributes such as high sensitivity and wide detection range. Here, we introduce a facile electrospinning process to fabricate a film composed of thermoplastic polyurethane/carboxyl multiwalled carbon nanotube/V2CTX-MXene (TPU/c-MWCNTs/V2C). This film has high sensitivity and great linearity, designed for flexible piezoresistive sensors. To further enhance sensor performance, we applied a spin-coating of polydimethylsiloxane (PDMS) on the sandpaper surface to create a top substrate with intermittent spinosum structures. Additionally, a layer of TPU fibers was electrospun between the conductive film and the interdigitated electrodes. The assembled components form a flexible piezoresistive sensor with a wide detection range of 0–200 kPa, a high sensitivity of 545.2 kPa−1, a low detection limit of 5 Pa, a short response time of 48 ms, and excellent long-term durability after 7000 loading/unloading cycles, making the piezoresistive sensor highly discriminating for detecting complex human movements. Thanks to the great sensing performance and stable structure, we conducted various demonstrations to capture human physiological signals, including signals of muscle activity and pulse. These findings indicate the potential of the designed flexible pressure sensor for applications in electronic skin and smart devices.

Validity of Valor Inertial Measurement Unit for Upper and Lower Extremity Joint Angles.

Inertial measurement units (IMU) are increasingly utilized to capture biomechanical measures such as joint kinematics outside traditional biomechanics laboratories. These wearable sensors have been proven to help clinicians and engineers monitor rehabilitation progress, improve prosthesis development, and record human performance in a variety of settings. The Valor IMU aims to offer a portable motion capture alternative to provide reliable and accurate joint kinematics when compared to industry gold standard optical motion capture cameras. However, IMUs can have disturbances in their measurements caused by magnetic fields, drift, and inappropriate calibration routines. Therefore, the purpose of this investigation is to validate the joint angles captured by the Valor IMU in comparison to an optical motion capture system across a variety of movements. Our findings showed mean absolute differences between Valor IMU and Vicon motion capture across all subjects' joint angles. The tasks ranged from 1.81 degrees to 17.46 degrees, the root mean squared errors ranged from 1.89 degrees to 16.62 degrees, and interclass correlation coefficient agreements ranged from 0.57 to 0.99. The results in the current paper further promote the usage of the IMU system outside traditional biomechanical laboratories. Future examinations of this IMU should include smaller, modular IMUs with non-slip Velcro bands and further validation regarding transverse plane joint kinematics such as joint internal/external rotations.

Lignin-induced rapid polymerization of asymmetrical adhesion Janus gel for strain sensor

Functional hydrogel sensors have shown explosive growth in the health and medical fields. However, the uniform adhesion and the complicated polymerization process of hydrogels seriously hinder their further development. Herein, inspired by the layered structure of human skin, we prepare a Janus gel using in-situ polymerization. Based on the lignin-Fe3+ dual catalytic system, the rapid polymerization of the gel was achieved at room temperature. By tailoring the mass ratio of lignin and Fe3+ in the precursor, the adhesion of the upper and bottom layers can be easily adjusted. In addition, hydrophobic association is introduced into the upper layer to improve the gel's mechanical properties. The obtained asymmetric bilayer gel has a significant difference in adhesion (7 times), and exhibits excellent mechanical properties in the elongation at break (1437 %) and the breaking strength (463.2 kPa). Moreover, the bilayer gel also has good freezing and UV resistance. We use the bilayer gel as a wearable strain sensor, which shows a wide strain detection range of 0–800 % (maximum gauge factor = 5.3). The proposed simple strategy avoids UV irradiation and heating processes, which provides a new idea for the rapid polymerization of multifunctional Janus hydrogels with adjustable performances.

Intelligent fibers and textiles for wearable biosensors

AbstractIn conjunction with the advancement of digital healthcare, the field of wearable biosensors has experienced rapid growth in recent years and is projected to expand further in the coming years. As wearable biosensors enter their next phase, emphasis is increasingly placed on developing comfortable, breathable, washable, and lightweight intelligent fibers and textiles as key components. This review examines the contributions of both natural and synthetic fibers and textiles to wearable biosensors. The structure and preparation process of intelligent fibers and textiles are systematically elucidated, including the development of the conductive layer. Additionally, key micro–nano technologies that have emerged in this domain are highlighted such as built‐in power supplies, wireless data transmission, precise data analysis driven by artificial intelligence, and machine learning. Furthermore, advanced functionalities integrated into recent biosensing systems, such as heat management, real‐time displays, and human–machine interaction are summarized. Moreover, the applications of fiber‐based wearable biosensors for monitoring biophysical and biochemical signals are presented. Finally, the challenges faced by the community working on wearable sensors based on intelligent fibers and textiles are discussed alongside promising future directions.

A microfluidic patch for wireless wearable electrochemical detection of sweat metabolites

Wearable sweat sensors provide an innovative detection method for analyzing human physiological and biochemical parameters and play an important role in detecting the potential risks of chronic diseases. Existing commercial wearable sensors (Apple iWatch, HUAWEI smart band) have problems such as single detection index (heart rate or blood pressure), and mainly focus on the physical information. This work studied an integrated microfluidic patch to simultaneously detect several biomarkers (pH, sodium ions, and uric acid) in sweat. We achieved the detection of pH level, sodium, and uric acid in linear ranges of 4–8, 0–160 mM, and 5–160 μM, respectively, covering well the clinically relevant ranges in the healthy human sweat. Laser engraving was chosen to prepare a microfluidic layer, and the sweat collection function was realized by combining hydrophilic and hydrophobic microfluidic synergy. It could efficiently collect the required sweat sample volume (80 μL) within 30 s while avoiding the contamination caused by directly contact with the skin. In addition, a wireless signal acquisition and transmission system was designed with the signal reading, processing, and wireless transmission functions, which can display the testing results in real time on Android phone-specific application software. We combined the wireless system with a microfluidic patch for on-body testing to verify the good wearability of the sweat sensor. This research realized the wireless data processing and transmission while improving sweat collection efficiency. In addition, it could detect various sweat physiological components in real-time, and provides a multiparameter, real-time, and portable sensing platform.

Novel fillers for wearable technology: Impact of titanium carbide and thinners on room temperature vulcanized silicone rubber composites

Wearable technology has emerged as a robust pathway to achieve smart functionalities in healthcare, sports, fitness, and beyond. This study investigates the mechanical and electromechanical properties of silicone rubber (SR) composites. The SR was reinforced with titanium carbide (TiC) and modified with varying (room-temperature vulcanized) RTV-thinner (silicone oil) concentrations. RTV-SR was utilized, which cures at room temperature to form a flexible, durable, and moldable rubber widely used for sealing, bonding, and making molds. The addition of TiC enhanced tensile strength from 0.5 MPa (control) to 0.59 MPa (5 phr of TiC), and tensile modulus from 0.41 MPa to 0.51 MPa. However, the thinner inclusion reduced tensile properties. For instance, the T10 sample (10 phr thinner) showed a tensile strength of 0.51 MPa and a tensile modulus of 0.29 MPa making the composites soft. Thinner addition also increased ductility, with fracture strains rising from 151 % (control) to 217 % (T10). Compressive strength followed a similar trend, with the control and 5 phr TiC samples exhibiting compressive strengths of 0.18 MPa and 0.17 MPa, respectively, which decreased to 0.12 MPa for the T20 sample. Under cyclic loading, TiC-filled samples showed consistent load patterns, while thinner addition resulted in increased energy dissipation and reduced stiffness. Electromechanical analysis revealed that TiC 5 phr samples exhibited stable piezoelectric responses, with voltage outputs increasing from ±0.1 mV at 15 % strain to ±1.0 mV at 40 % strain. The T5 samples demonstrated significant voltage improvements, generating ±2.5 mV at 15 % strain and ±5 mV at 40 % strain. Long-term cyclic tests confirmed the stability of T5 samples, maintaining voltage outputs of −1.5 mV to 1.5 mV over 50 minutes. Response time analysis indicated faster response times for TiC 5 phr samples, decreasing from 145 ms at 15 % strain to 102 ms at 40 % strain, while thinner addition led to longer response times. Real-time electromechanical tests under mechanical study showed that T5 samples achieved optimal performance with voltage peaks up to ±2.4 mV under thumb pressing. These findings highlight the trade-off between mechanical strength and flexibility, suggesting that an optimal balance of TiC and thinner enhances the properties of silicone rubber composites for applications in wearable technology and sensors.

Enhancing self-induced polarization of PVDF-based triboelectric film by P-doped g-C3N4 for ultrasensitive triboelectric pressure sensors

Progress in wearable energy devices has reached the point of popularity over a few decades, as reflected by the rise of numerous sensors and storage devices driven by the urgent need for Internet of Things (IoT) applications. Triboelectric sensor (TES), on the basis of triboelectric nanogenerators (TENGs), exhibits high sensitivity and stability for pressure detection. Maximizing the sensitivity of the TES hinges upon the triboelectric layer selection capable of harnessing triboelectrification and electrostatic induction. Among them, PVDF has gained attention owing to its exceptional flexibility and tribo-negativity. However, the multiphase of pristine PVDF poses challenges, limiting its potential for TES with higher sensitivity. Tackling this issue, herein, we introduced phosphorus-doped graphitic carbon nitride (PCN) into the PVDF films to achieve self-induced polarization to enhance the sensitivity of the triboelectric pressure sensor. Through a controlled annealing temperature, phosphorus atoms are incorporated into the g-C3N4 (CN) matrix, enhancing the electronegativity of PVDF/PCN composite film while inducing a polarized β crystal phase in PVDF, thereby enabling greater attraction of positive charges. Consequently, the triboelectric output with PVDF/PCN composite film exhibited a significant improvement compared to the pristine PVDF film, achieving over a 100 V increment and the ability to power more than 100 LEDs. Furthermore, for the pressure-sensing applications, the TES with flexible PVDF/PCN triboelectric film exhibits a pressure sensitivity of 1.48 V/kPa, which shows a threefold increase in sensitivity compared to the pristine PVDF film (0.51 V/kPa), suggesting their great potential in self-powered wearable pressure sensors.

Wearable electro-optical sensor for monitoring of nitrogen dioxide gas in environment

Environmental monitoring for detection of harmful pollutants is a challenge in today’s world. Development of wearable sensors with cost-effectiveness and practicality, while using accessible electronic components can be a solution to this challenge. In the present work ultraviolet light emitting diodes (UV-LEDs) and a Light Dependent Resistor (LDR) pair with carefully designed programmable circuitry is used to detect Nitrogen Dioxide (NO2) in the environment. The sensor is battery operated and rechargeable, making it convenient and eco-friendly. The sensor developed is miniaturized and wearable in nature for the detection of analyte gas in the environment. The sensor is stable and inexpensive as compared to the existing NO2 sensors available, which are electro-chemical, or oxide based. The sensor system is sensitive and selective to its exposure to NO2 gas and has no interference with typical environmental attributes such as humidity and temperature, which is a common challenge in gas detection technologies. The principle of gas detection is absorption based, light falling on the detector absorbed by the test gas present in the environment, thus changing the detector resistance. Absorption-based gas detection is indigenously simple but highly effective technique. This method not only ensures good sensitivity but enhances the selectivity of the electro-optical sensor towards the test gas preventing false triggering in the presence of other interfering gases.

A Simple and Sensitive Wearable SERS Sensor Utilizing Plasmonic-Active Gold Nanostars.

Wearable sweat sensors hold great potential for offering detailed health insights by monitoring various biomarkers present in sweat, such as glucose, lactate, uric acid, and urea, in real time. However, most previously reported sensors, primarily based on electrochemical technology, are limited to monitoring only a single analyte at a given time. This study introduces a simple, sensitive, wearable patch based on surface-enhanced Raman spectroscopy (SERS), integrated with highly plasmonically active sharp-branched gold nanostars (GNS) for the simultaneous detection of three sweat biomarkers: lactate, urea, and glucose. We have fabricated the GNS on commercially available adhesive tape, resulting in achieving a low-cost, flexible, and adhesive wearable SERS patch. The limits of detection for lactate, urea, and glucose were achieved at 0.7, 0.6, and 0.7 μM, respectively, which are significantly lower than the clinically relevant concentrations of these biomarkers in sweat. We further evaluated the performance of our wearable SERS patch during outdoor activities, including sitting, walking, and running. To evaluate its overall effectiveness, we simultaneously measured the concentrations of lactate, urea, and glucose during these activities. Overall, our simple, sensitive wearable SERS sensor represents a significant breakthrough by enabling the simultaneous detection of lactate, urea, and glucose present in sweat, marking a major step toward future applications in autonomous and noninvasive personalized healthcare monitoring at home.

The Utility of Calibrating Wearable Sensors before Quantifying Infant Leg Movements.

While interest in using wearable sensors to measure infant leg movement is increasing, attention should be paid to the characteristics of the sensors. Specifically, offset error in the measurement of gravitational acceleration (g) is common among commercially available sensors. In this brief report, we demonstrate how we measured the offset and other errors in three different off-the-shelf wearable sensors available to professionals and how they affected a threshold-based movement detection algorithm for the quantification of infant leg movement. We describe how to calibrate and correct for these offsets and how conducting this improves the reproducibility of results across sensors.

Molecularly Imprinted Wearable Sensor with Paper Microfluidics for Real-Time Sweat Biomarker Analysis.

The urgent need for real-time and noninvasive monitoring of health-associated biochemical parameters has motivated the development of wearable sweat sensors. Existing electrochemical sensors show promise in real-time analysis of various chemical biomarkers. These sensors often rely on labels and redox probes to generate and amplify the signals for the detection and quantification of analytes with limited sensitivity. In this study, we introduce a molecularly imprinted polymer (MIP)-based biochemical sensor to quantify a molecular biomarker in sweat using electrochemical impedance spectroscopy, which eliminates the need for labels or redox probes. The molecularly imprinted biosensor can achieve sensitive and specific detection of cortisol at concentrations as low as 1 pM, 1000-fold lower than previously reported MIP cortisol sensors. We integrated multimodal electrochemical sensors with an iontophoresis sweat extraction module and paper microfluidics for real-time sweat analysis. Several parameters can be simultaneously quantified, including sweat volume, secretion rate, sodium ion, and cortisol concentration. Paper microfluidic modules not only quantify sweat volume and secretion rate but also facilitate continuous sweat analysis without user intervention. While we focus on cortisol sensing as a proof-of-concept, the molecularly imprinted wearable sensors can be extended to real-time detection of other biochemicals, such as protein biomarkers and therapeutic drugs.

Patterned Gold Nanoparticle Superlattice Film for Wearable Sweat Sensors.

Nanoparticle superlattices are beneficial in terms of providing strong and uniform signals in analysis owing to their closely packed uniform structures. However, nanoparticle superlattices are prone to cracking during physical activities because of stress concentrations, which hinders their detection performance and limits their analytical applications. In this work, template printing methods were used in this study to prepare a patterned gold nanoparticle (AuNP) superlattice film. By adjustment of the size of the AuNP superlattice domain below the critical size of fracture, the mechanical stability of the AuNP superlattice domain is improved. Thus, long-term sustainable high-performance signal output is achieved. The patterned AuNP superlattice film was used to construct a wearable sweat sensor based on surface-enhanced Raman scattering (SERS). The designed sensor showed promise for long-term reliable use in actual scenarios in terms of recommending water replenishment, monitoring hydration states, and tracking the intensity of activity.

Sleep During the COVID-19 Pandemic: Longitudinal Observational Study Combining Multisensor Data With Questionnaires.

The COVID-19 pandemic prompted various containment strategies, such as work-from-home policies and reduced social contact, which significantly altered people's sleep routines. While previous studies have highlighted the negative impacts of these restrictions on sleep, they often lack a comprehensive perspective that considers other factors, such as seasonal variations and physical activity (PA), which can also influence sleep. This study aims to longitudinally examine the detailed changes in sleep patterns among working adults during the COVID-19 pandemic using a combination of repeated questionnaires and high-resolution passive measurements from wearable sensors. We investigate the association between sleep and 5 sets of variables: (1) demographics; (2) sleep-related habits; (3) PA behaviors; and external factors, including (4) pandemic-specific constraints and (5) seasonal variations during the study period. We recruited working adults in Finland for a 1-year study (June 2021-June 2022) conducted during the late stage of the COVID-19 pandemic. We collected multisensor data from fitness trackers worn by participants, as well as work and sleep-related measures through monthly questionnaires. Additionally, we used the Stringency Index for Finland at various points in time to estimate the degree of pandemic-related lockdown restrictions during the study period. We applied linear mixed models to examine changes in sleep patterns during this late stage of the pandemic and their association with the 5 sets of variables. The sleep patterns of 27,350 nights from 112 working adults were analyzed. Stricter pandemic measures were associated with an increase in total sleep time (TST) (β=.003, 95% CI 0.001-0.005; P<.001) and a delay in midsleep (MS) (β=.02, 95% CI 0.02-0.03; P<.001). Individuals who tend to snooze exhibited greater variability in both TST (β=.15, 95% CI 0.05-0.27; P=.006) and MS (β=.17, 95% CI 0.03-0.31; P=.01). Occupational differences in sleep pattern were observed, with service staff experiencing longer TST (β=.37, 95% CI 0.14-0.61; P=.004) and lower variability in TST (β=-.15, 95% CI -0.27 to -0.05; P<.001). Engaging in PA later in the day was associated with longer TST (β=.03, 95% CI 0.02-0.04; P<.001) and less variability in TST (β=-.01, 95% CI -0.02 to 0.00; P=.02). Higher intradaily variability in rest activity rhythm was associated with shorter TST (β=-.26, 95% CI -0.29 to -0.23; P<.001), earlier MS (β=-.29, 95% CI -0.33 to -0.26; P<.001), and reduced variability in TST (β=-.16, 95% CI -0.23 to -0.09; P<.001). Our study provided a comprehensive view of the factors affecting sleep patterns during the late stage of the pandemic. As we navigate the future of work after the pandemic, understanding how work arrangements, lifestyle choices, and sleep quality interact will be crucial for optimizing well-being and performance in the workforce.

Recent Advances in Wearable Electromechanical Sensors Based on Auxetic Textiles

AbstractTextile‐based electromechanical sensors are increasingly used as wearable sensors for various applications, such as health monitoring and human‐machine interfaces. These sensors are becoming increasingly popular as they offer a comfortable and conformable sensing platform and possess properties that can be tuned by selecting different fiber materials, yarn‐spinning techniques, or fabric fabrication methods. Although it is still in its early stages, recent attempts have been made to introduce auxeticity to textile sensors to enhance their sensitivity. Having a negative Poisson's ratio, i.e., undergoing expansion laterally when subjected to tensile forces and contraction laterally under compressive forces, makes them distinct from conventional sensors with positive Poisson's ratio. This unique feature has demonstrated great potential in enhancing the performance of electromechanical sensors. This review presents an overview of electromechanical sensors based on auxetic textiles (textiles made from auxetic materials and/or non‐auxetic materials but with auxetic structures), specifically focusing on how the unique auxetic deformation impacts sensing performance. Sensors based on different working mechanisms, including piezoelectric, triboelectric, piezoresistive, and piezocapacitive, are covered. It is envisioned that incorporating auxeticity and electromechanical sensing capabilities into textiles will significantly advance wearable technology, leading to new sensors for health monitoring, fitness tracking, and smart clothing.

MXene/MWCNTs-based capacitive pressure sensors combine high sensitivity and wide detection range for human health and motion monitoring

Wearable pressure sensors with exceptional performance are playing a crucial role in various fields such as electronic skin, human motion monitoring, medical diagnosis, and human-computer interaction. However, achieving both high sensitivity and a wide sensing range with simple fabrication and low cost has proven to be a significant challenge for sensors. In this study, a simple capacitive pressure sensor is proposed that multi-walled carbon nanotubes (MWCNTs) are introduced into MXene (Ti3C2TX) as a ‘bridge’. Then the electrode which has excellent conductivity is obtained by filtration of MXene solution and MXene/MWCNTs solution at intervals. Additionally, green and degradable cellulose paper is used as the dielectric layer. The fabricated pressure sensor exhibits a high sensitivity of 4.7 kPa−1 for low pressure from 0 to 1 kPa, a wide detection range of 0–700 kPa, ultra-fast response and recovery times of 46 ms and 62 ms, respectively, and an extremely low detection limit of 0.32 Pa. The sensor remains stable even after 4500 cycles and can accurately monitor the movement of the human joints. Furthermore, it can track human physiological signals, which is beneficial for medical diagnosis and disease prevention, and has significant potential for application in the wearable technology field.

Temporal localization of upper extremity bilateral synergistic coordination using wearable accelerometers.

The human upper extremity is characterized by inherent motor abundance, allowing a diverse array of tasks with agility and adaptability. Upper extremity functional limitations are a common sequela to Stroke, resulting in pronounced motor and sensory impairments in the contralesional arm. While many therapeutic interventions focus on rehabilitating the weaker arm, it is increasingly evident that it is necessary to consider bimanual coordination and motor control. Participants were recruited to two groups differing in age (Group 1 (n=10): 23.4 ±2.9 years, Group 2 (n=10): 55.9 ±10.6 years) for an exploratory study on the use of accelerometry to quantify bilateral coordination. Three tasks featuring coordinated reaching were selected to investigate the acceleration of the upper arm, forearm, and hand during activities of daily living (ADLs). Subjects were equipped with acceleration and inclination sensors on each upper arm, each forearm, and each hand. Data was segmented in MATLAB to assess inter-limb and intra-limb coordination. Inter-limb coordination was indicated through dissimilarity indices and temporal locations of congruous movement between upper arm, forearm, or hand segments of the right and left limbs. Intra-limb coordination was likewise assessed between upper arm-forearm, upper arm-hand, and forearm-hand segment pairs of the dominant limb. Acceleration data revealed task-specific movement features during the three distinct tasks. Groups demonstrated diminished similarity as task complexity increased. Groups differed significantly in the hand segments during the buttoning task, with Group 1 showing no coordination in the hand segments during buttoning, and strong coordination in reaching each button with the upper arm and forearm guiding extension. Group 2's dissimilarity scores and percentages of similarity indicated longer periods of inter-limb coordination, particularly towards movement completion. Group 1's dissimilarity scores and percentages of similarity indicated longer periods of intra-limb coordination, particularly in the coordination of the upper arm and forearm segments. The Expanding Procrustes methodology can be applied to compute objective coordination scores using accessible and highly accurate wearable acceleration sensors. The findings of task duration, angular velocity, and peak roll angle are supported by previous studies finding older individuals to present with slower movements, reduced movement stability, and a reduction of laterality between the limbs. The theory of a shift towards ambidexterity with age is supported by the finding of greater inter-limb coordination in the group of subjects above the age of thirty-five. The group below the age of thirty was found to demonstrate longer periods of intra-limb coordination, with upper arm and forearm coordination emerging as a possible explanation for the demonstrated greater stability.

Rapid photo-crosslinking, highly conductive, and anti-freezing acrylamide-based hydrogels applied for ECG sensors

In recent years, ionic conductive hydrogels have attracted significant attention in the wearable sensors field due to their simplified preparation process, cost- effectiveness, and high conductivity. However, a large amount of water in traditional hydrogels inevitably condense into ice at low temperatures and make them lose various properties in their practical applications. Hence, this study proposes a method to rapidly synthesized a water/glycerol binary anti-freezing hydrogel only in 10 s through photo-initiated free-radical polymerization. The introduction of LiCl into the crosslinked network composed of acrylamide (AAm) and poly (ethylene glycol) diacrylate (PEGDA) regulated the ionic conductivity of the hydrogel. The hydrogel exhibits a fracture strain of 1062.1 %, a tensile strength of 31 kPa, and an adhesive strength of 6.49 kPa. The synergistic effects of glycerol and LiCl imparted excellent anti-freezing properties (−60 °C). The three-dimensional network structure of the hydrogel facilitates ion transport, exhibiting superior conductivity (30.25 S/m), maintaining high conductivity capability of 1.46 S/m even at a low temperature of −55 °C. Notably, the hydrogel demonstrates prolonged durability and stability in real-time human motion monitoring applications while concurrently exhibiting remarkable sensitivity as an ECG sensor. Hence, this as-prepared hydrogel can adapt to extreme environments and deliver comprehensive performance as an ECG sensor.

Application of Industry 4.0 and digital twin in the field of smart healthcare

There is an increasing need for preventive health care as well as precise diagnosis and tailored treatment of different diseases in recent years. Providing customized treatment for each patient and maximizing accuracy and efficiency are main goals of a good healthcare system. This thesis explores the integration of digital twin technology with Industry 4.0 in healthcare. Digital twins create virtual representations of physical systems, enabling real-time monitoring and tailored treatments. Key elements include the living human body, IoT, digital twin, cloud computing, and simulation. The architecture comprises data acquisition, data munging, data storage, data simulation with analytics, and user access layers. Creating a digital twin involves precise 3D modeling, representing the entire body or specific organs. A case study demonstrates real-time monitoring using wearable sensors for blood pressure, blood sugar, and heart rate. Data is transmitted, integrated with the digital twin, and accessible via a website with alarms for abnormal readings. While Industry 4.0 and digital twin adoption in healthcare is evolving, the architecture serves as a reference. It offers real-time data for diagnosis and treatment, with potential for advanced simulations. Challenges include automated data systems and privacy concerns. Despite limitations, this integration holds promise for precision medicine and personalized healthcare.

Application of Wearable Electronics Sensors for Public Health Monitoring and Disease Prevention

Each component of contemporary technology depends on sensors. Improved and more advanced sensors have the potential to advance technological advancements and ultimately contribute to a better quality of life for humans. Adopting more modern materials, designs, and sensing mechanism types is crucial to the development of sensors. Wearable sensors can be used to monitor several aspects of human gait by placing them on the hip, knee, wrist, and foot, among other parts of the body. Measurements of public health, including body temperature, heart rate, pulse oxygenation, respiration rate, blood pressure, blood glucose, ECG signal, and disease prevention, can be made with these sensors. Numerous recent studies have taken into consideration wearable sensor-based systems. With an emphasis on monitoring human physiological indicators, the research work aimed to demonstrate new sensor utilisation in the development of wearable devices for biomonitoring applications.

Effective Public Health Assessment: Research on Alzheimer Disease Diagnosis Model With Communication Technology

Rapid identification and appropriate management of Alzheimer's pose important challenges, particularly within public health contexts. This research introduces a unique framework for diagnosing Alzheimer's disease using communication technologies (such as wearable sensors). Novel chimp search-driven ReLu recurrent network (CS-RRN) strategy is introduced in this study for effectively detecting Alzheimer's disease. The ReLu function is employed in long/short-term memory (LSTM) to learn nonlinear dependencies, and then the chimp optimization strategy is applied to enhance the detection performance of the LSTM network. The gait signal data is collected from various wearable sensor devices to analyze the effectiveness of the suggested CS-RRN m technique. The performance of the proposed method is analyzed through a Python-driven implementation platform in terms of various metrics. The CS-RRN approach effectively achieved the maximum performance in detecting Alzheimer's disease when compared with other existing approaches. Based on the findings of this research, communication technology may substantially boost public health by providing a low-cost and high-performing method for Alzheimer disease identification.