Wearable Health Monitoring Devices Research Articles

Bio-Electronics Interface between Electronics and Biological Systems for Healthcare Applications.

The intersection of electronics and biology has led to the emergence of bio-electronics interfaces, which hold tremendous potential for revolutionizing healthcare applications. These interfaces facilitate seamless communication between electronic devices and biological systems, enabling a range of diagnostic, therapeutic, and monitoring capabilities with unprecedented precision and efficiency.This paper provides a comprehensive overview of the recent advancements in bio-electronics interfaces and their diverse applications in healthcare. The fundamental principles underlying the integration of electronics with biological systems, including the design and fabrication of bio-compatible materials, signal transduction mechanisms, and biointegration strategies are discussed. [1] The paper discusses specific healthcare applications enabled by bio-electronics interfaces, such as bio-sensing for disease diagnosis, neural interfaces for brain-machine communication and prosthetics, bio-electronic implants for targeted drug delivery and neuromodulation, and wearable devices for continuous health monitoring. We also examine the challenges and opportunities associated with the development and implementation of bio-electronics interfaces in healthcare settings. These challenges include biocompatibility issues, signal interference, power management, data security, and regulatory considerations. However, rapid advancements in materials science, microfabrication techniques, wireless communication, and machine learning are driving innovation and overcoming these hurdles. We highlight the potential of bio-electronics interfaces to transform personalized medicine by enabling real-time monitoring of physiological parameters, early detection of diseases, and personalized therapeutic interventions tailored to individual patients' needs. By integrating electronic devices with biological systems at the molecular, cellular, and organismal levels, bio-electronics interfaces have the potential to revolutionize healthcare delivery, improve patient outcomes, and enhance quality of life.

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.

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.

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.

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.

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.

A new particle filter algorithm filtering motion artifact noise for clean electrocardiogram signals in wearable health monitoring system.

With the evolution of wearable systems, more and more people tend to wear wearable devices for health monitoring during sports. However, a large amount of motion artifact noise is introduced at this time, which is difficult to filter out due to its stochasticity. The amplitude and characteristics of motion artifact noise vary with changes in motion intensity. In order to filter out the motion artifact noise, the paperproposes a new particle algorithm, which can detect the intensity of the motion artifact for adaptive filtering, especiallysuitable for wearable health monitoring systems. In this algorithm, variational mode decomposition was first introduced to analyze the noisy electrocardiogram (ECG) signal in order to find the clean components. Then, the Laguerre estimation technique was applied to obtain an accurate ECG polar model. Taking this model as the state equation, a particle filter algorithm was defined to filter out the motion artifact noise. In the particle filter algorithm, we defined a parameter γ whose values were obtained from the six-axis data of motion sensor MPU6050 in our wearable device. This parameter γ could reflect the current noise levels and adaptively update the particle weights. Finally, some exercise experiments proved that the parameter γ could map the motion artifacts in real time and also demonstrated the superiority of the algorithm in terms of signal-to-noise ratio improvement and error reduction compared to other algorithms. The new particle filter algorithm proposed in this paper combines the six-axis data (three-axis accelerometer and three-axis gyroscope) with the ECG signal to effectively eliminate a large amount of motion artifact noise, thus solving the problem of excess noise from wearable devices when people are exercising, allowing them to accurately obtain real-time ECG health information.

Adaptation Behavior of Construction Workers Using Wearable Sensing Devices for Safety and Health Monitoring

Adaptation Behavior of Construction Workers Using Wearable Sensing Devices for Safety and Health Monitoring

A Reconfigurable Non-Volatile Memory Architecture for Prolonged Wearable Health Monitoring Devices

A Reconfigurable Non-Volatile Memory Architecture for Prolonged Wearable Health Monitoring Devices

Harnessing Technology for Enhanced Patient Care and Monitoring

This study looks into how individuals with chronic illnesses' levels of physical activity are affected by wearable health monitoring devices. We looked at participant demographics, health status, and physical activity information throughout an eight-week period using a quantitative method. The findings show that wearing wearable technology is positively correlated with higher levels of physical activity. Our results support previous studies by highlighting the usefulness of wearables in encouraging behavior modification. Notably, the study offers a deeper picture of wearable device adoption by contributing information regarding health problems and demographic characteristics. The ramifications for the practice of medicine indicate that include wearables in care plans could improve patient interventions for chronic illnesses. While acknowledging study limitations, the overall conclusion supports the The general conclusion acknowledges the limits of the study but affirms the revolutionary role that wearable technology will play in reshaping healthcare, improving patient outcomes, and encouraging proactive health management.

Highly Sensitive and Linear Vibration‐Based Flexible Modulus Sensing System for Human Modulus Monitoring and Disease Prevention

AbstractIt is important to directly characterize the modulus of objects through wearable systems, which can further develop electronic skin and human‐computer interaction. In this work, a modulus sensing system based on a pressure sensor and vibration module is developed. This resistive pressure sensor exhibits good linearity and sensitivity. Combined with the vibration module to adjust the applied stress and strain, the modulus value of the objects can be directly calculated. The modulus sensing system demonstrates excellent modulus monitoring capabilities in the range of 60‒3000 kPa modulus and high linearity. In addition, it is used to detect modulus values for different parts of the human body and integrate a concept eye mask to prevent and monitor migraine. The system shows the potential to be further applied to wearable devices for human health monitoring and disease prevention.

Smart wearable devices for real-time health monitoring

Smartwatches have emerged as powerful tools for health monitoring in recent years. These wearable devices combine advanced technology with portability, enabling individuals to track various aspects of their health and well-being conveniently. Heart rate monitoring has several advantages. Many smartwatches are equipped with optical heart rate sensors that continuously monitor a user’s heart rate. This feature provides real-time data on heart rate variations, which can be crucial for identifying irregularities and assessing overall cardiovascular health. Activity tracking: Smartwatches often include accelerometers and gyroscopes that track physical activities such as steps taken, distance traveled, and calories burned. This information can aid in setting and achieving fitness goals. Sleep tracking:Some smartwatches offer sleep tracking features, recording sleep duration, quality, and patterns. These data help users understand their sleep habits and make improvements for better rest. Global positioning system (GPS) and location tracking: Integrated GPS allows users to track their outdoor activities accurately, including running and cycling. This feature not only measures performance but also enhances safety during workouts. Stress monitoring: Certain smartwatches include tools that measure stress levels through heart rate variability analysis. Users can learn to manage stress more effectively based on these data. Electrocardiogram (ECG) and blood pressure monitoring:High-end smartwatches may include ECG and blood pressure monitoring capabilities. These features are particularly useful for individuals with heart conditions or hypertension. Notifications and alerts: Smartwatches can deliver notifications and alerts related to health goals, reminders to move, medication schedules, and emergencies. This functionality enhances user engagement and timely interventions. BENEFITS OF SMARTWATCHES IN HEALTH MONITORINGUnlike periodic doctor visits, smartwatches enable continuous health monitoring. Users can track their vital signs and activities throughout the day, providing a more comprehensive picture of their health. Smartwatches empower individuals to take control of their health. Users can make informed decisions about their lifestyle, exercise, and diet by providing real-time data. Smartwatches equipped with ECG and heart rate monitoring can detect irregularities and abnormalities early on. This early detection can be lifesaving for individuals at risk of heart-related conditions. Activity tracking and goal-setting features motivate users to engage in physical activities and maintain a healthier lifestyle. Achieving fitness goals can lead to increased motivation and adherence to healthier habits. Health data collected by smartwatches can be used to tailor fitness and wellness plans to an individual’s specific needs. This personalized approach can lead to more effective health improvements. LIMITATIONS AND CONSIDERATIONSAccuracy: While smartwatches offer valuable health data, their accuracy may vary. Factors such as skin type,device placement, and motion can affect the precision of measurements. Battery life: Continuous health monitoring can drain a smartwatch’s battery quickly. Users may need to recharge their devices frequently, potentially limiting the monitoring duration. Privacy and data security: Storing sensitive health data on a smartwatch raise concerns about privacy and data security. Manufacturers must implement robust security measures to protect users’ information. User reliance: There is a risk of over-reliance on smartwatches for health information. Users should be encouraged to consult health-care professionals for a comprehensive health assessment. CONCLUSIONSmartwatches have become versatile tools for health monitoring, offering a range of features that empower users to track and improve their well-being. While they have numerous benefits, users should know their limitations and use them as complementary tools in their health-care journey. Manufacturers and health-care providers play a critical role in ensuring the accuracy and security of the health data collected by these devices.

Tiny-PPG: A lightweight deep neural network for real-time detection of motion artifacts in photoplethysmogram signals on edge devices

Photoplethysmogram (PPG) signals are easily contaminated by motion artifacts in real-world settings, despite their widespread use in Internet-of-Things (IoT) based wearable and smart health devices for cardiovascular health monitoring. This study proposed a lightweight deep neural network, called Tiny-PPG, for accurate and real-time PPG artifact segmentation on IoT edge devices. The model was trained and tested on a public dataset, PPG DaLiA, which featured complex artifacts with diverse lengths and morphologies during various daily activities of 15 subjects using a watch-type device (Empatica E4). The model structure, training method and loss function were specifically designed to balance detection accuracy and speed for real-time PPG artifact detection in resource-constrained embedded devices. To optimize the model size and capability in multi-scale feature representation, the model employed depth-wise separable convolution and atrous spatial pyramid pooling modules, respectively. Additionally, the contrastive loss was also utilized to further optimize the feature embeddings. With additional model pruning, Tiny-PPG achieved state-of-the-art detection accuracy of 87.4% while only having 19,726 model parameters (0.15 megabytes), and was successfully deployed on an STM32 embedded system for real-time PPG artifact detection. Therefore, this study provides an effective solution for resource-constraint IoT smart health devices in PPG artifact detection.

Investigation on using graphite filler for 3D printing of flexible electrically conductive polymer by extrusion‐based additive manufacturing

AbstractThe rapid use of additive manufacturing (AM) in electronic industry identifies the need of flexible electrically conductive materials suitable for 3D printing. Challenges like buckling of flexible filament and complexity in addition of conductive fillers makes pellet‐based extrusion the most suitable alternative for part printing. In the present work, graphite powder is combined with ethylene vinyl acetate (EVA) to print flexible electrically conductive parts. EVA graphite composite with filler loading up to 30% has been investigated for its electrical conductivity and part printability. The composite made was found electrically conductive and suitable for part printing. The parts were printed on a CNC milling machine by using a retrofitted material extrusion tool with nozzle of 0.4 mm diameter and temperature range of 130–140°C. Parts were printed on a detchable heating bed placed on the milling machine setup. Samples of EVA with graphite filler by weight of 5%, 10%, 20%, and 30% were printed and were checked for electrical conductivity. When the filler content was increased from 10% to 20%, the electrical resistance of the composite was reduced by 62.6%. The presence and uniform dispersion of graphite flakes in the polymer matrix of EVA were guaranteed by the rheological and morphological characterization of the composite. Applications of 3D printable flexible electrically conductive parts lies in numerous domains including biosensors, wearable health monitoring devices, capacitors, field effective transistors, light emitting diodes, solar cells, and electronic displays.Highlights CNC milling machine‐assisted 3D printing is used for part printing. Graphite powder is combined with EVA pellets. Pellet‐based extrusion is used to print flexible parts. 3D printability of parts with resistivity 32.64 Ω‐cm was achieved.

The role of remote data capture, wearables, and digital biomarkers in decentralized clinical trials.

Decentralized clinical trials (DCTs) are gaining momentum in clinical research as these studies can be executed remotely through telemedicine and mobile/local health-care providers. The COVID-19 pandemic has further accelerated advances and adoption in this area. In the past few years, there has been significant development and growth in the use of remote data that are electronically transmitted from a clinical trial (CT) participant, from outside the clinical setting, to a data repository. Such data may include laboratory data, safety data, or outcome measures reported by the participant, the clinician or the observer. Similarly, wearable health monitoring devices are being increasingly used in health-care and CT settings. Digital biomarkers, which can support continuous measurement of physiologic parameters outside the physical confines of the clinical environment, are also creating new and improved opportunities for patient care and biomedical research, enabling remote monitoring and DCTs. There are several benefits to using remote data capture, wearables, and digital biomarkers in clinical health-care research; however, several questions and challenges still need to be addressed. In an effort to understand the adoption of these technologies in DCTs, and the challenges therein, the authors of this workstream conducted an online survey of clinical research stakeholders across India and reviewed 80 responses. The review article summarizes the key findings from this online survey.

Cost-effective electroconductive sodium lignosulfonate/carbon nanotube composite coating for paper electronics

The high demand for flexible electroconductive elements, such as organic coatings, is associated with the development of IoT devices, wearable health monitoring devices, and similar devices. To meet cost and environmental standards, the ideal coating should use the richness of nature, be biodegradable, and be water soluble to avoid the use of harmful organic solvents. All of these criteria are met by the composite coating consisting of sodium lignosulfonate (SLS) and multiwall carbon nanotubes (MWCNTs). Furthermore, the SLS/CNT coating on paper substrates is highly electroconductive, hydrophobic, and resistant to annealing, high humidity, bending, and scratching. The electrical conductivity of the SLS/CNT coating varies depending on the substrate used, but it is highly conductive and resistant on all substrates tested. Most importantly, due to the use of abundant and mass produced and cheap materials, the price of a coating containing 77 wt% SLS and 23 wt% CNT, with a sheet resistance of 113 Ω/sq is estimated to be around 2.05 EUR/m2. This coating can be especially beneficial for large-scale applications, such as heating films.