Health Monitoring Devices Research Articles

Impact of Bio-impedance Emitted from Wearable Smart Watches on Skin Microbiota: A Pilot Study

ABSTRACT Smart watches (SWs), which offer users a variety of features including bio-impedance measurements, have grown significantly in popularity as wearable health monitoring devices. Electrical currents are used in bio-impedance technology to measure things like body composition and hydration levels. However, little is known about how these electrical currents might affect the skin’s microbiota, which is vital for preserving skin health. This summary of the possible effects of bio-impedance measurements from SWs on the skin microbiota is intended. Hence, we tried to explore the effect of impedance emitted from smart watches on the skin microbiota. A total of 20 volunteers were recruited, and their skin microbial load was enumerated from the impedance-exposed region at two different time intervals and compared with non-exposed region. We found an overall 57% reduction of microbial load on impedance-exposed regions as compared to non-exposed regions. Impedance significantly (P < 0.0001) reduced the microbial load. Despite preliminary data pointing to a potential connection between bio-impedance readings from SWs and skin microbiota, further research is required to understand the impact of SWs on the human skin.

Multisensory Health Monitoring Device Based on Raspberry Pi 4B

The availability of health monitoring devices that can be used independently, conveniently, and portably is increasing in line with busy lifestyles and the difficulty of scheduling medical tests. Measuring vital body signals with various devices makes measurements longer, less effective, and relatively more expensive. The proposed research can monitor vital body signals, such as heart rate, body temperature, respiratory rate, oxygen saturation, GSR, blood pressure, and snoring, which are integrated into a Raspberry Pi 4B-based device, with results displayed on an LCD screen. Data acquisition results show reasonably good accuracy in almost all parameters but require improvement in respiratory rate measurements. In the subsequent work, these seven-acquisition data will be used to predict several possible diseases.

Extrusion-based additive manufacturing of aluminium-filled ethylene vinyl acetate for electrically conductive 3D part printing

Addition of carbon-based fillers to enhance electrical conductivity is quite popular but addition of metal fillers is still a challenge due to oxidation and unavailability of metal nano-powders. In this paper, fabrication of flexible electrically conductive parts is proposed using aluminium metal powder as filler material. Aluminium metal powder is mixed with ethylene vinyl acetate (EVA) by chemical mixing process. Cyclohexane is used as a mixing medium in which EVA is dissolved and aluminium powder is added by weight. An in-house developed material extrusion tool mounted on CNC milling machine is used for printing of flexible electrically conductive samples. Pellet-based extrusion process is used for printing flexible parts as conventional filament-based 3D is not suitable due to buckling. Composite with filler loading in 5%, 10%, 20% and 30% were made and samples were printed. Investigations were made on printability of composite and its electrical conductivity. The aluminium fillers presence and dispersion in polymer were ensured. The samples were printed in temperature range of 130–140 °C through 0.4 mm diameter nozzle. It was found that the resistivity drops by around 60% on varying the filler concentration from 20% to 30%; 81.6 kΩ is the lowest resistance observed with 30% filler concentration. A simulation study on thermal behaviour of material extrusion tool has been done and is validated practically. The application of flexible electrically conductive parts lies in fabrication of various electronic devices like antenna, display boards, solar panels, and wearable health monitoring devices.

A-Mode Ultrasound Bladder Volume Estimation Algorithm Based on Wavelet Energy Ratio Adaptive Denoising.

Assessing bladder function is pivotal in urological health, with bladder volume a critical indicator. Traditional devices, hindered by high costs and cumbersome sizes, are being increasingly supplemented by portable alternatives; however, these alternatives often fall short in measurement accuracy. Addressing this gap, this study introduces a novel A-mode ultrasound-based bladder volume estimation algorithm optimized for portable devices, combining efficient, precise volume estimation with enhanced usability. Through the innovative application of a wavelet energy ratio adaptive denoising method, the algorithm significantly improves the signal-to-noise ratio, preserving critical signal details amidst device and environmental noise. Ultrasonic echoes were employed to acquire positional information on the anterior and posterior walls of the bladder at several points, with an ellipsoid fitted to these points using the least squares method for bladder volume estimation. Ultimately, a simulation experiment was conducted on an underwater porcine bladder. The experimental results indicate that the bladder volume estimation error of the algorithm is approximately 8.3%. This study offers a viable solution to enhance the accuracy and usability of portable devices for urological health monitoring, demonstrating significant potential for clinical application.

Optimizing Piezoelectric Energy Harvesting from Mechanical Vibration for Electrical Efficiency: A Comprehensive Review

In the current era, energy resources from the environment via piezoelectric materials are not only used for self-powered electronic devices, but also play a significant role in creating a pleasant living environment. Piezoelectric materials have the potential to produce energy from micro to milliwatts of power depending on the ambient conditions. The energy obtained from these materials is used for powering small electronic devices such as sensors, health monitoring devices, and various smart electronic gadgets like watches, personal computers, and cameras. These reviews explain the comprehensive concepts related to piezoelectric (classical and non-classical) materials, energy harvesting from the mechanical vibration of piezoelectric materials, structural modelling, and their optimization. Non-conventional smart materials, such as polyceramics, polymers, or composite piezoelectric materials, stand out due to their slender actuator and sensor profiles, offering superior performance, flexibility, and reliability at competitive costs despite their susceptibility to performance fluctuations caused by temperature variations. Accurate modeling and performance optimization, employing analytical, numerical, and experimental methodologies are imperative. This review also furthers research and development in optimizing piezoelectric energy utilization, suggesting the need for continued experimentation to select optimal materials and structures for various energy applications.

Acoustic wave devices based on piezoelectric/ferroelectric thin films for high-frequency communication systems and sensing applications

Surface acoustic wave (SAW) and bulk acoustic wave (BAW) based radio-frequency (RF) filters dominate in handsets markets mainly because they have miniature size so multiple filters can be used but take up very small board area. Besides, due to their inherent advantages, such as low insertion loss, relatively high resonant frequency, and miniature size, their application in the physical and biochemical sensing fields has also gradually expanded. The successful implementation requires specific knowledge of acoustic wave properties, the working mechanisms, material properties, and device design. So, in this review, we survey the SAW and BAW filter technology by discussing the working principles and the comparison between the operation frequency band allocations. In addition, the approaches to enhance the performance including the selection of single crystal/ferroelectric materials for BAW and the structure design optimization for SAW are introduced with discussions for further improvement. Then, flexible SAW and BAW devices are also presented and their new application opportunities in the fields of skin-like electronics and wearable health monitoring devices can be foreseen. Finally, the potential challenges of high frequency, wide bandwidth, miniaturization, and compatibility with the integrated circuits (IC) manufacturing process are overviewed as well.

Wearable Sweat Management Technologies

AbstractThe aging population and increasing prevalence of diseases among younger individuals have heightened the pressing demand for wearable health monitoring devices among the general public. In comparison to self‐health monitoring at physical signal level, wearable biochemical monitoring can provide more comprehensive and insightful information regarding one's own health status. Wearable sweat‐sensing technologies hold great potential for non‐invasive and continuous biochemical monitoring, and have emerged as a focal point of wearable health monitoring research. The effective management of sweat, encompassing convenient induction, reliable collection, and efficient flow control and storage, is fundamental in wearable sweat sensing. To elucidate the research progression in these aspects, this review first summarizes sweat secretion mechanisms, induction method, and sampling mode. Additionally, detailed summaries and discussion on sweat sampling and transport modes are provided. Furthermore, within the context of epidermal microfluidics, the technologies on sweat flow control and storage management based on microvalves are discussed. Lastly, current commercialization and key challenges are summarized.

Optimization of low power consumption in wearable health monitoring devices and algorithm design

This paper surveys state-of-the-art approaches designed to decrease the power consumption of wearable health monitoring devices, thereby optimizing battery life. Specifically, this paper examines a concept proposed by researchers in a separate investigation, which involves active computation offloading. This entails developing an algorithm that efficiently distributes data processing tasks between wearable health monitoring devices and mobile applications. Researchers have successfully reduced system power consumption by up to 20% by intelligently offloading computations based on existing device characteristics. In contrast, another ECG remote monitoring system focuses on designing the lowest power sensor while keeping the system low cost and scalable. Lastly, this paper provides ideas to further lower power by state-of-the-art chip design, algorithmic and system design techniques.

Interface‐Confined Assembly of Layered Molecular Crystal Arrays Toward Wearable UV‐Radiation Monitor

AbstractOrganic semiconductors have significant potential for wearable optoelectronics due to their designable molecular structures, tunable photoelectronic properties, and flexibility. For their integration into optoelectronic devices, organic semiconductors need to be assembled into single‐crystalline arrays with both precise alignment of micro‐/nanostructures and long‐range order of molecules. In this study, an interface‐confined lithography technique is developed for fabricating organic single‐crystalline microwire arrays with layered molecular stacking and ordered crystallographic orientation toward wearable UV radiation monitor. This interface‐confined strategy introduces parallel gas–liquid–solid three‐phase contact line (TPCL) arrays to suppress the lateral growth of single crystals. Meanwhile, TPCLs with controlled dewetting direction promote longitudinal growth, thus universally yielding 1D arrays based on various organic semiconducting small molecules. These 1D single‐crystalline arrays exhibit high average mobility of 12.5 cm2 V−1 s−1 with uniform electronic properties of 9.3% variation, and good mechanical stability after 1000 bending times. Based on organic 1D arrays, wearable devices with robust photoresponsivity of 8.26 × 103 A W−1 and high mechanical flexibility achieve real‐time monitoring of UV radiation to prevent skin disease. This confined‐assembly strategy opens a new avenue to construct wearable optoelectronic devices for health monitoring.

NiMBaLWear analytics pipeline for wearable sensors: a modular, open-source platform for evaluating multiple domains of health and behaviour

BackgroundRecent technological advances have led to a surge in the use of wearable devices for personal health and fitness monitoring; however, clinical uptake of wearable devices for remote or ‘free-living’ measurement of daily health-related behavior has lagged. To advance the field, there is need for valid and reliable outcomes across multiple health domains specific to the cohorts or patients of interest and centralized tools to build capacity for use of these data. The NiMBaLWear pipeline provides a flexible and integrated approach to wearables analytics applied to raw sensor data that considers multiple, inter-related physiological and behavioral signals to provide a holistic view of health status.Results & discussionNiMBaLWear is a modular, open-source, wearable sensor analytic pipeline that quantifies physical activity, mobility, and sleep from raw single- or multi-sensor free-living data collected over multiple days. Data captured from any device, in different possible formats, are standardized prior to processing. Data preparation includes accelerometer autocalibration, cross-device synchronization, and non-wear detection. Validated, domain-specific algorithms detect events, generate outcome measures, and output standardized tabular data and user-friendly summary collection reports. NiMBaLWear was developed in Python using an iterative and incremental software development process, which included a combination of semi-automated inspection and expert review of data collected from 286 participants across two remote-measurement studies. A comparative analysis revealed a paucity of open-source packages capable of deriving and sharing health-related behavioral outcomes across multiple domains from multi-sensor wearables data. Forthcoming improvements to the pipeline will leverage sensor fusion techniques to add new, and refine existing, domain- and disease-specific analytics, and optimize pipeline accessibility and reporting.ConclusionThe NiMBaLWear pipeline transforms raw multi-sensor wearables data into accurate and relevant outcomes across multiple health domains to objectively characterize and measure an individual’s daily health-related behavior. NiMBaLWear’s focus on high-quality, clinically relevant outcomes, as well as end-user optimization, provides a foundation for innovation to improve the utility of wearables for clinical care and self-management of health.

Automatic Health Monitoring Device using Gloves and Gesture Recognition System for Emergency Alert

Even after voice assistants, virtual assistants, and other new technologies have come into existence, many are still facing problems to use them conveniently and effectively. Our project is mainly applicable to old people, physically challenged, and bedridden people. Our project application areas are hospitals and old age homes. During emergency situations where the user is not able to access his/her mobile phone, our prototype can handle such situations effectively by detecting the user’s hand gestures and sending alerts to caretakers and hospitals. Our model also has a medical alert system. According to the prior initialization of the time for the medicines to be taken, this prototype alerts the user at that exact time. The alert message is displayed on the mobile and also through a buzzer. This glove has a temperature sensor, flex sensors, and pulse-oxy sensors embedded which give the continuous readings of user-health parameters and are displayed on the display placed on the glove. The main feature of the glove is to communicate the needs of the user which can be accomplished by a flex sensor

Precision Population Cancer Medicine in Cancer of the Uterine Cervix: A Potential Roadmap to Eradicate Cervical Cancer.

With the success of the Human Genome Project, the era of genomic medicine (GM) was born. Later on, as GM made progress, there was a feeling of exhilaration that GM could help resolve many disease processes. It also led to the conviction that personalized medicine was possible, and a relatively synonymous word, precision medicine (PM), was coined. However, the influence of environmental factors and social determinants of diseases was only partially given their due importance in the definition of PM, although more recently, this has been recognized. With the rapid advances in GM, big data, data mining, wearable devices for health monitoring, telemedicine, etc., PM can be more easily extended to population-level health care in disease management, prevention, early screening, and so on.and the term precision population medicine (PPM) more aptly describes it. PPM's potential in cancer care was posited earlier,and the current authors planned a series of cancer disease-specific follow-up articles. These papers are mainly aimed at helping emerging students in health sciences (medicine, pharmacy, nursing, dentistry, public health, population health), healthcare management (health-focused business administration, nonprofit administration, public institutional administration, etc.), and policy-making (e.g., political science), although not exclusively. This first disease-specific report focuses on the cancer of the uterine cervix (CC). It describes how recent breakthroughs can be leveraged as force multipliers to improve outcomes in CC - by improving early detection, better screening for CC, potential GM-based interventions during the stage of persistent Human papillomavirus (HPV) infection and treatment interventions - especially among the disadvantaged and resource-scarce populations. This work is a tiny step in our attempts to improve outcomes in CC and ultimately eradicate CC from the face of the earth.

Application of behavior data to predictive exploratory models of metritis self-cure and treatment failure in dairy cows

The objective was to evaluate the performance of exploratory models containing routinely available on-farm data, behavior data, and the combination of both to predict metritis self-cure (SC) and treatment failure (TF). Holstein cows (n = 1,061) were fitted with a collar-mounted automated- health monitoring device (AHMD) from -21 ± 3 to 60 ± 3 d relative to calving to monitor rumination and activity. Cows were examined for diagnosis of metritis at 4 ± 1, 7 ± 1, and 9 ± 1 DIM. Cows diagnosed with metritis (n = 132), characterized by watery, fetid, reddish/brownish vaginal discharge (VD) were randomly allocated to one of 2 treatments: Control (CON; n = 62) - no treatment at the time of metritis diagnosis (d 0); Ceftiofur (CEF; n = 70) - subcutaneous injection of 6.6 mg/kg of ceftiofur crystalline-free acid on d 0 and 3 relative to diagnosis. Cure was determined 12 d after diagnosis and was considered when VD became mucoid and not fetid. Cows in CON were used to determine SC and cows in CEF were used to determine TF. Univariable analyses were performed using farm-collected data (parity, calving season, calving-related disorders, body condition score, rectal temperature, and days in milk at metritis diagnosis) and behavior data (i.e., daily averages of rumination, activity generated by AHMD, and derived variables) to assess their association with metritis SC or TF. Variables with a P ≤ 0.20 were included in the multivariable logistic regression exploratory models. To predict SC, the area under the curve (AUC) for the exploratory model containing only data routinely available on-farm was 0.75. The final exploratory model to predict SC combining routinely available on-farm data and behavior data increased the AUC to 0.87, sensitivity (Se) 87% and specificity (Sp) 71%. To predict TF, the AUC for the exploratory model containing only data routinely available on-farm was 0.90. The final exploratory model combining routinely available on-farm data and behavior data increased the AUC to 0.93, Se of 93% and Sp of 82%. Cross-validation analysis revealed that generalizability of the exploratory models was poor, which indicates that the findings are applicable to the conditions of the present exploratory study. In summary, the addition of behavior data contributed to increasing the prediction of SC and TF. Developing and validating accurate prediction models for SC could lead to a reduction in antimicrobial use, whereas accurate prediction of cows that would have TF may allow for better management decisions.

Hybrid Piezoelectric/Triboelectric Wearable Nanogenerator Based on Stretchable PVDF-PDMS Composite Films.

Hybrid piezoelectric/triboelectric nanogenerators combine the merits of piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs), possessing enhanced electrical output and sensitivity. However, the structures of the majority of hybrid nanogenerators are rather complex in integrating both functions, limiting their practical application in wearable electronics. Herein, we propose to construct a piezoelectric/triboelectric hybrid nanogenerator (PT-NG) with a simple structure based on a composite film to simultaneously achieve the coupling of piezoelectric charge generation and triboelectrification with improved energy conversion efficiency. The composite film consists of electrospun PVDF nanofibers embedded in the surface of the PDMS film, which not only forms a rough nanomorphology on the surface of PDMS but also provides structural protection to the PVDF nanofibers by PDMS during compressive deformation. The results have shown that the PT-NG can generate much higher electrical outputs than individual TENG and PENG devices. The PT-NG devices exhibit a high level of mechanical-to-electrical energy conversion efficiency with superior performance in charging capacitors and functioning as self-powered wearable sensors for the detection of different signals from finger movement, the recognition of various gestures, and the monitoring of respiration. More importantly, the composite device possesses an impressive structure durability, maintaining its layered structure over 5000 testing cycles without noticing any obvious damage on the nanofibers or detachment between the layers. Our results have demonstrated that the combining of piezoelectric nanofibers and triboelectric substrate is an efficient way to fabricate highly efficient energy harvesting devices for intelligent identification and health monitoring.

Dynamic analysis of a novel multilink-spring mechanism for vibration isolation and energy harvesting

Due to technical limitations, existing vibration isolation and energy harvesting (VIEH) devices have poor performance at low frequency. This paper proposes a new multilink-spring mechanism (MLSM) that can be used to solve this problem. The VIEH performance of the MLSM under harmonic excitation and Gaussian white noise was analyzed. It was found that the MLSM has good vibration isolation performance for low-frequency isolation and the frequency band can be widened by adjusting parameters to achieve a higher energy harvesting power. By comparison with two special cases, the results show that the MLSM is basically the same as the other two oscillators in terms of vibration isolation but has better energy harvesting performance under multistable characteristics. The MLSM is expected to reduce the impact of vibration on high-precision sensitive equipment in some special sites such as subways and mines, and at the same time supply power to structural health monitoring devices.

Wearable Microneedle Patch for Transdermal Electrochemical Monitoring of Urea in Interstitial Fluid.

Microneedle-based wearable electrochemical biosensors are the new frontier in personalized health monitoring and disease diagnostic devices that provide an alternative tool to traditional blood-based invasive techniques. Advancements in micro- and nanofabrication technologies enabled the fabrication of microneedles using different biomaterials and morphological features with the aim of overcoming existing challenges and enhancing sensing performance. In this work, we report a microneedle array featuring conductive recessed microcavities for monitoring urea levels in the interstitial fluid of the skin. Microcavities are small pockets on the tip of each microneedle that can accommodate the sensing layer, provide protection from delamination during skin insertion or removal, and position the sensing layer in a deep layer of the skin to reach the interstitial fluid. The wearable urea patch has shown to be highly sensitive and selective in monitoring urea, with a sensitivity of 2.5 mV mM-1 and a linear range of 3 to 18 mM making it suitable for monitoring urea levels in healthy individuals and patients. Our ex vivo experiments have shown that recessed microcavities can protect the sensing layer from delamination during skin insertion and monitor changing urea levels in interstitial fluid. This biocompatible platform provides alternative solutions to the critical issue of maintaining the performance of the biosensor upon skin insertion and holds great potential for advancing transdermal sensor technology.

Research Progress in Self‐Powered Pressure Sensors for Internet of Healthcare

AbstractTo mitigate the risks posed by noncommunicable chronic diseases (NCDs) to human health, the Internet of Healthcare (IoH) requires sensors with real‐time and long‐term monitoring capabilities. However, traditional pressure sensors, due to their high‐power consumption and non‐rechargeability, are unbale to meet the increasingly stringent requirements of physiological monitoring devices in the IoH. The emergence of self‐powered pressure sensing technology, exemplified by nanogenerators, provides a new strategy for the next generation of wearable health monitoring devices. This review begins by discussing the merits and drawbacks of different pressure‐sensing modes for healthcare monitoring. Then, it introduces the development and working mechanism of self‐powered triboelectric and piezoelectric sensors. Furthermore, it summarizes the optimizations of sensor structure and material selection aimed at improving sensing performance and achieving high accuracy in sensor networks within the IoH. Relevant application based on independent, or hybrid mechanisms are also covered. Finally, the challenges and prospects for achieving large‐scale commercial applications of self‐power sensing system in IoH are discussed.

Polymer design for solid-state batteries and wearable electronics.

Solid-state batteries are increasingly centre-stage for delivering more energy-dense, safer batteries to follow current lithium-ion rechargeable technologies. At the same time, wearable electronics powered by flexible batteries have experienced rapid technological growth. This perspective discusses the role that polymer design plays in their use as solid polymer electrolytes (SPEs) and as binders, coatings and interlayers to address issues in solid-state batteries with inorganic solid electrolytes (ISEs). We also consider the value of tunable polymer flexibility, added capacity, skin compatibility and end-of-use degradability of polymeric materials in wearable technologies such as smartwatches and health monitoring devices. While many years have been spent on SPE development for batteries, delivering competitive performances to liquid and ISEs requires a deeper understanding of the fundamentals of ion transport in solid polymers. Advanced polymer design, including controlled (de)polymerisation strategies, precision dynamic chemistry and digital learning tools, might help identify these missing fundamental gaps towards faster, more selective ion transport. Regardless of the intended use as an electrolyte, composite electrode binder or bulk component in flexible electrodes, many parallels can be drawn between the various intrinsic polymer properties. These include mechanical performances, namely elasticity and flexibility; electrochemical stability, particularly against higher-voltage electrode materials; durable adhesive/cohesive properties; ionic and/or electronic conductivity; and ultimately, processability and fabrication into the battery. With this, we assess the latest developments, providing our views on the prospects of polymers in batteries and wearables, the challenges they might address, and emerging polymer chemistries that are still relatively under-utilised in this area.

Recent advances in bismuth oxychalcogenide nanosheets for sensing applications.

This review offers insights into the fundamental properties of bismuth oxychalcogenides Bi2O2X (X = S, Se, Te) (BOXs), concentrating on recent advancements primarily from studies published over the past five years. It examines the physical characteristics of these materials, synthesis methods, and their potential as critical components for gas sensing, biosensing, and optical sensing applications. Moreover, it underscores the implications of these advancements for the development of military, environmental, and health monitoring devices.

Self-assembled superstructures derived from suitably substituted phenanthro[9,10-d] imidazole-based Zn(ii)/Cd(ii) complexes for selective detection of acetone

Newly synthesized (L1)2M, (L2)2M, and (L3)2M (M = Zn(ii)/Cd(ii)) self-assembled into well-ordered superstructures and exhibited promising potential for developing a new class of sensor devices for human health monitoring and environmental safety measurements.