Recent Advances in Stretchable and Wearable Capacitive Electrophysiological Sensors for Long-Term Health Monitoring

From lab to wearables: Innovations in multifunctional hydrogel chemistry for next-generation bioelectronic devices

Functional hydrogels have emerged as foundational materials in diagnostics, therapy, and wearable devices, owing to their high stretchability, flexibility, sensing, and outstanding biocompatibility. Their significance stems from their resemblance to biological tissue and their exceptional versatility in electrical, mechanical, and biofunctional engineering, positioning themselves as a bridge between living organisms and electronic systems, paving the way for the development of highly compatible, efficient, and stable interfaces. These multifaceted capability revolutionizes the essence of hydrogel-based wearable devices, distinguishing them from conventional biomedical devices in real-world practical applications. In this comprehensive review, we first discuss the fundamental chemistry of hydrogels, elucidating their distinct properties and functionalities. Subsequently, we examine the applications of these bioelectronics within the human body, unveiling their transformative potential in diagnostics, therapy, and human-machine interfaces (HMI) in real wearable bioelectronics. This exploration serves as a scientific compass for researchers navigating the interdisciplinary landscape of chemistry, materials science, and bioelectronics.

Wearable and implantable biosensors: mechanisms and applications in closed-loop therapeutic systems

This review article examines the current state of wearable and implantable biosensors, offering an overview of their biosensing mechanisms and applications. We also delve into integrating these biosensors with therapeutic systems, discussing their operational principles and incorporation into closed-loop devices. Biosensing strategies are broadly categorized into chemical sensing for biomarker detection, physical sensing for monitoring physiological conditions such as pressure and temperature, and electrophysiological sensing for capturing bioelectrical activities. The discussion extends to recent developments in drug delivery and electrical stimulation devices to highlight their significant role in closed-loop therapy. By integrating with therapeutic devices, biosensors enable the modulation of treatment regimens based on real-time physiological data. This capability enhances the patient-specificity of medical interventions, an essential aspect of personalized healthcare. Recent innovations in integrating biosensors and therapeutic devices have led to the introduction of closed-loop wearable and implantable systems capable of achieving previously unattainable therapeutic outcomes. These technologies represent a significant leap towards dynamic, adaptive therapies that respond in real-time to patients' physiological states, offering a level of accuracy and effectiveness that is particularly beneficial for managing chronic conditions. This review also addresses the challenges associated with biosensor technologies. We also explore the prospects of these technologies to address their potential to transform disease management with more targeted and personalized treatment solutions.

Cold atmospheric plasma deposition of antibacterial polypyrrole-silver nanocomposites on wearable electronics for prolonged performance

Wearable electronics have become integral for monitoring physiological parameters in diverse applications, particularly in medical and military fields. e-Textiles, featuring integrated conductive threads or fabrics, offer seamless integration and comfort for prolonged contact with the body. Despite their potential, the biofouling of textile-based electrode systems by skin microbes remains a significant challenge, limiting their operational lifespan. Recent studies have highlighted the efficacy of conductive nanocomposites with antibacterial agents, such as silver nanoparticles (AgNPs), in addressing biofouling concerns. However, implementing such systems on 3D fibrous structures and textile surfaces often proves complex and inefficient. To overcome these challenges, we explored cold atmospheric plasma (CAP)-based in situ polymerization for the direct deposition of functional conductive polypyrrole-silver (PPy-Ag) nanocomposites onto conductive textile surfaces. For this process, a customized CAP deposition system was engineered, enabling precise material deposition through robotic control of the plasma jet. This process achieved direct, conformal attachment onto textile fibrous structures, ensuring uniform distribution of conductive polypyrrole and silver in the form of AgNPs throughout the polymer polypyrrole matrix without compromising fabric flexibility and breathability, which was validated through different surface electron microscopy and chemical analysis (e.g., EDX, FTIR, Raman, and XRD). Systematic studies with various precursor mixtures identified an optimized PPy-Ag composition that demonstrated stable antibacterial properties and biocompatibility against common skin microbes and epithelial cells. Systematic studies with various precursor mixtures identified an optimized PPy-Ag composition, with the precursor mixture containing 96 wt% pyrrole and 4 wt% AgNO3 weight ratios as the optimal surface coating process, demonstrating stable antibacterial properties and biocompatibility against common skin microbes and epithelial cells respectively. As a proof of concept, the nanocomposite coating was applied to conductive carbon fabric surfaces as dry electrodes in a wearable garment for continues electrocardiography (ECG) monitoring over 10 days. Results revealed a significantly longer performance of the dry electrodes as comparable to standard gel-based Ag/AgCl electrodes (1 day) while providing less noise in ECG signal measurements from the subject, showcasing the potential of this technology for practical wearable applications. Envisioned as a groundbreaking solution, this technology opens new avenues for the scalable and effective integration of functional conductive circuits and sensors into everyday garments, ensuring prolonged and efficient performance in wearable electronics.

ECG signal quality in intermittent long-term dry electrode recordings with controlled motion artifacts

Wearable long-term monitoring applications are becoming more and more popular in both the consumer and the medical market. In wearable ECG monitoring, the data quality depends on the properties of the electrodes and on how they interface with the skin. Dry electrodes do not require any action from the user. They usually do not irritate the skin, and they provide sufficiently high-quality data for ECG monitoring purposes during low-intensity user activity. We investigated prospective motion artifact-resistant dry electrode materials for wearable ECG monitoring. The tested materials were (1) porous: conductive polymer, conductive silver fabric; and (2) solid: stainless steel, silver, and platinum. ECG was acquired from test subjects in a 10-min continuous settling test and in a 48-h intermittent long-term test. In the settling test, the electrodes were stationary, whereas both stationary and controlled motion artifact tests were included in the long-term test. The signal-to-noise ratio (SNR) was used as the figure of merit to quantify the results. Skin-electrode interface impedance was measured to quantify its effect on the ECG, as well as to leverage the dry electrode ECG amplifier design. The SNR of all electrode types increased during the settling test. In the long-term test, the SNR was generally elevated further. The introduction of electrode movement reduced the SNR markedly. Solid electrodes had a higher SNR and lower skin-electrode impedance than porous electrodes. In the stationary testing, stainless steel showed the highest SNR, followed by platinum, silver, conductive polymer, and conductive fabric. In the movement testing, the order was platinum, stainless steel, silver, conductive polymer, and conductive fabric.

Reversible thermochromic fibers with excellent elasticity and hydrophobicity for wearable temperature sensors

Color-changing fibers, which can intuitively convey information to the human eye, can be used to facilely add functionality to various types of clothing. However, they are often expensive and complex, and can suffer from low durability. Therefore, in this study, we developed highly elastic and hydrophobic thermochromic fibers as wearable temperature sensors using a simple method that does not require an electric current. A thermochromic pigment was embedded inside and outside hydrophobic silica aerogel particles, following which the thermochromic aerogel was fixed to highly elastic spandex fibers using polydimethylsiloxane as a flexible binder. In particular, multi-strand spandex fibers were used instead of single strands, resulting in the thermochromic aerogels penetrating the inside of the strands upon their expansion by solvent swelling. During drying, the thermochromic aerogel adhered more tightly to the fibers by compressing the strands. The thermochromic fiber was purple at room temperature (25 °C), but exhibited a two-stage color change to blue and then white as the temperature increased to 37 °C. In addition, even after 100 cycles of tension-contraction at 200%, the thermochromic aerogel did not detach and was strongly attached to the fiber. Additionally, it was confirmed that color change due to temperature was stable even after exposure to 1 wt% NaCl (artificial sweat) and 0.1 wt% detergent solutions. The developed thermochromic fiber therefore exhibited excellent elasticity and hydrophobicity, and is expected to be widely utilized as an economical wearable temperature sensor as it does not require electrical devices.

Advances in the Use of Conducting Polymers for Healthcare Monitoring

Conducting polymers (CPs) are an innovative class of materials recognized for their high flexibility and biocompatibility, making them an ideal choice for health monitoring applications that require flexibility. They are active in their design. Advances in fabrication technology allow the incorporation of CPs at various levels, by combining diverse CPs monomers with metal particles, 2D materials, carbon nanomaterials, and copolymers through the process of polymerization and mixing. This method produces materials with unique physicochemical properties and is highly customizable. In particular, the development of CPs with expanded surface area and high conductivity has significantly improved the performance of the sensors, providing high sensitivity and flexibility and expanding the range of available options. However, due to the morphological diversity of new materials and thus the variety of characteristics that can be synthesized by combining CPs and other types of functionalities, choosing the right combination for a sensor application is difficult but becomes important. This review focuses on classifying the role of CP and highlights recent advances in sensor design, especially in the field of healthcare monitoring. It also synthesizes the sensing mechanisms and evaluates the performance of CPs on electrochemical surfaces and in the sensor design. Furthermore, the applications that can be revolutionized by CPs will be discussed in detail.

Bioinspired nanoplatforms for human-machine interfaces: Recent progress in materials and device applications

The panoramic characteristics of human-machine interfaces (HMIs) have prompted the needs to update the biotechnology community with the recent trends, developments, and future research direction toward next-generation bioelectronics. Bioinspired materials are promising for integrating various bioelectronic devices to realize HMIs. With the advancement of scientific biotechnology, state-of-the-art bioelectronic applications have been extensively investigated to improve the quality of life by developing and integrating bioinspired nanoplatforms in HMIs. This review highlights recent trends and developments in the field of biotechnology based on bioinspired nanoplatforms by demonstrating recently explored materials and cutting-edge device applications. Section 1 introduces the recent trends and developments of bioinspired nanomaterials for HMIs. Section 2 reviews various flexible, wearable, biocompatible, and biodegradable nanoplatforms for bioinspired applications. Section 3 furnishes recently explored substrates as carriers for advanced nanomaterials in developing HMIs. Section 4 addresses recently invented biomimetic neuroelectronic, nanointerfaces, biointerfaces, and nano/microfluidic wearable bioelectronic devices for various HMI applications, such as healthcare, biopotential monitoring, and body fluid monitoring. Section 5 outlines designing and engineering of bioinspired sensors for HMIs. Finally, the challenges and opportunities for next-generation bioinspired nanoplatforms in extending the potential on HMIs are discussed for a near-future scenario. We believe this review can stimulate the integration of bioinspired nanoplatforms into the HMIs in addition to wearable electronic skin and health-monitoring devices while addressing prevailing and future healthcare and material-related problems in biotechnologies.

In vitro assessment of inflammatory skin potential of poly(methyl methacrylate) at non-cytotoxic concentrations

Poly(methyl methacrylate) (PMMA) is considered an attractive substrate material for fabricating wearable skin sensors such as fitness bands and microfluidic devices. Despite its widespread use, inflammatory and allergic responses have been attributed to the use of this material. Therefore, the main objective of this study was to obtain a comprehensive understanding of potential biological effects triggered by PMMA at non-cytotoxic concentrations using in vitro models of NIH3T3 fibroblasts and reconstructed human epidermis (RhE). It was hypothesized that concentrations that do not reduce cell viability are sufficient to activate pathways of inflammatory processes in the skin. The study included cytotoxicity, cell metabolism, cytokine quantification, histopathological, and gene expression analyses. The NIH3T3 cell line was used as a testbed for screening cell toxicity levels associated with the concentration of PMMA with different molecular weights (MWs) (i.e., MW ~5,000 and ~15,000 g/mol). The lower MW of PMMA had a half-maximal inhibitory concentration (IC50 ) value of 5.7 mg/cm2 , indicating greater detrimental effects than the higher MW (IC50  = 14.0 mg/cm2 ). Non-cytotoxic concentrations of 3.0 mg/cm2 for MW ~15,000 g/mol and 0.9 mg/cm2 for MW ~5,000 g/mol) induced negative metabolic changes in NIH3T3 cells. Cell viability was severely reduced to 7% after the exposure to degradation by-products generated after thermal and photodegradation degradation of PMMA. PMMA at non-cytotoxic concentrations still induced overexpression of pro-inflammatory cytokines, chemokines, and growth factors (IL1B, CXCL10, CCL5, IL1R1, IL7, IL17A, VEGFA, FGF2, IFNG, IL15) on the RhE model. The inflammatory response was also supported by histopathological and gene expression analyses of PMMA-treated RhE, indicating tissue damage and gene overexpression. Results suggested that non-cytotoxic concentrations of PMMA (3.0 to 5.6 mg/cm2 for MW ~15,000 g/mol and 0.9 to 2.1 mg/cm2 for MW ~5,000 g/mol) were sufficient to negatively alter NIH3T3 cells metabolism and activate inflammatory events in the RhE skin.

Graphene-Based Flexible Strain Sensor Based on PDMS for Strain Detection of Steel Wire Core Conveyor Belt Joints

Because of their superior performance, flexible strain sensors are used in a wide range of applications, including medicine and health, human-computer interaction, and precision manufacturing. Flexible strain sensors outperform conventional silicon-based sensors in high-strain environments. However, most current studies report complex flexible sensor preparation processes, and research focuses on enhancing and improving one parameter or property of the sensors, ignoring the feasibility of flexible strain sensors for applications in various fields. Since the mechanical properties of flexible sensors can be well combined with rubber conveyor belts, in this work polydimethylsiloxane (PDMS) was used as a flexible substrate by a simple way of multiple drop coating. Graphene-based flexible strain sensor films that can be used for strain detection at the joints of steel cord core conveyor belts were successfully fabricated. The results of the tests show that the sensor has a high sensitivity and can achieve a fast response (response time: 43 ms). Furthermore, the sensor can still capture the conveyor belt strain after withstanding high pressure (1.2-1.4 MPa) and high temperature (150 °C) during the belt vulcanization process. This validates the feasibility of using flexible strain sensors in steel wire core conveyor belts and has some potential for detecting abnormal strains in steel wire core conveyor belt, broadening the application field of flexible sensors.

An Exploration of Wearable Device Features Used in UK Hospital Parkinson Disease Care: Scoping Review

BACKGROUND

The prevalence of Parkinson disease (PD) is becoming an increasing concern owing to the aging population in the United Kingdom. Wearable devices have the potential to improve the clinical care of patients with PD while reducing health care costs. Consequently, exploring the features of these wearable devices is important to identify the limitations and further areas of investigation of how wearable devices are currently used in clinical care in the United Kingdom.

OBJECTIVE

In this scoping review, we aimed to explore the features of wearable devices used for PD in hospitals in the United Kingdom.

METHODS

A scoping review of the current research was undertaken and reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The literature search was undertaken on June 6, 2022, and publications were obtained from MEDLINE or PubMed, Embase, and the Cochrane Library. Eligible publications were initially screened by their titles and abstracts. Publications that passed the initial screening underwent a full review. The study characteristics were extracted from the final publications, and the evidence was synthesized using a narrative approach. Any queries were reviewed by the first and second authors.

RESULTS

Of the 4543 publications identified, 39 (0.86%) publications underwent a full review, and 20 (0.44%) publications were included in the scoping review. Most studies (11/20, 55%) were conducted at the Newcastle upon Tyne Hospitals NHS Foundation Trust, with sample sizes ranging from 10 to 418. Most study participants were male individuals with a mean age ranging from 57.7 to 78.0 years. The AX3 was the most popular device brand used, and it was commercially manufactured by Axivity. Common wearable device types included body-worn sensors, inertial measurement units, and smartwatches that used accelerometers and gyroscopes to measure the clinical features of PD. Most wearable device primary measures involved the measured gait, bradykinesia, and dyskinesia. The most common wearable device placements were the lumbar region, head, and wrist. Furthermore, 65% (13/20) of the studies used artificial intelligence or machine learning to support PD data analysis.

CONCLUSIONS

This study demonstrated that wearable devices could help provide a more detailed analysis of PD symptoms during the assessment phase and personalize treatment. Using machine learning, wearable devices could differentiate PD from other neurodegenerative diseases. The identified evidence gaps include the lack of analysis of wearable device cybersecurity and data management. The lack of cost-effectiveness analysis and large-scale participation in studies resulted in uncertainty regarding the feasibility of the widespread use of wearable devices. The uncertainty around the identified research gaps was further exacerbated by the lack of medical regulation of wearable devices for PD, particularly in the United Kingdom where regulations were changing due to the political landscape.

Carbon-Based Textile Sensors for Physiological-Signal Monitoring

As the focus on physical health increases, the market demand for flexible wearable sensors increases. Textiles combined with sensitive materials and electronic circuits can form flexible, breathable high-performance sensors for physiological-signal monitoring. Carbon-based materials such as graphene, carbon nanotubes (CNTs), and carbon black (CB) have been widely utilized in the development of flexible wearable sensors due to their high electrical conductivity, low toxicity, low mass density, and easy functionalization. This review provides an overview of recent advancements in carbon-based flexible textile sensors, highlighting the development, properties, and applications of graphene, CNTs, and CB for flexible textile sensors. The physiological signals that can be monitored by carbon-based textile sensors include electrocardiogram (ECG), human body movement, pulse and respiration, body temperature, and tactile perception. We categorize and describe carbon-based textile sensors based on the physiological signals they monitor. Finally, we discuss the current challenges associated with carbon-based textile sensors and explore the future direction of textile sensors for monitoring physiological signals.

Multi-Transduction-Mechanism Technology, an Emerging Approach to Enhance Sensor Performance

Conventional sensor systems employ single-transduction technology where they respond to an input stimulus and transduce the measured parameter into a readable output signal. As such, the technology can only provide limited corresponding data of the detected parameters due to relying on a single transformed output signal for information acquisition. This limitation commonly results in the need for utilizing sensor array technology to detect targeted parameters in complex environments. Multi-transduction-mechanism technology, on the other hand, may combine more than one transduction mechanism into a single structure. By employing this technology, sensors can be designed to simultaneously distinguish between different input signals from complex environments for greater degrees of freedom. This allows a multi-parameter response, which results in an increased range of detection and improved signal-to-noise ratio. In addition, utilizing a multi-transduction-mechanism approach can achieve miniaturization by reducing the number of required sensors in an array, providing further miniaturization and enhanced performance. This paper introduces the concept of multi-transduction-mechanism technology by exploring different candidate combinations of fundamental transduction mechanisms such as piezoresistive, piezoelectric, triboelectric, capacitive, and inductive mechanisms.

Functional Two-Dimensional Materials for Bioelectronic Neural Interfacing

Realizing the neurological information processing by analyzing the complex data transferring behavior of populations and individual neurons is one of the fast-growing fields of neuroscience and bioelectronic technologies. This field is anticipated to cover a wide range of advanced applications, including neural dynamic monitoring, understanding the neurological disorders, human brain-machine communications and even ambitious mind-controlled prosthetic implant systems. To fulfill the requirements of high spatial and temporal resolution recording of neural activities, electrical, optical and biosensing technologies are combined to develop multifunctional bioelectronic and neuro-signal probes. Advanced two-dimensional (2D) layered materials such as graphene, graphene oxide, transition metal dichalcogenides and MXenes with their atomic-layer thickness and multifunctional capabilities show bio-stimulation and multiple sensing properties. These characteristics are beneficial factors for development of ultrathin-film electrodes for flexible neural interfacing with minimum invasive chronic interfaces to the brain cells and cortex. The combination of incredible properties of 2D nanostructure places them in a unique position, as the main materials of choice, for multifunctional reception of neural activities. The current review highlights the recent achievements in 2D-based bioelectronic systems for monitoring of biophysiological indicators and biosignals at neural interfaces.

Wearable sensors and features for diagnosis of neurodegenerative diseases: A systematic review

Objective

Neurodegenerative diseases affect millions of families around the world, while various wearable sensors and corresponding data analysis can be of great support for clinical diagnosis and health assessment. This systematic review aims to provide a comprehensive overview of the existing research that uses wearable sensors and features for the diagnosis of neurodegenerative diseases.

Methods

A systematic review was conducted of studies published between 2015 and 2022 in major scientific databases such as Web of Science, Google Scholar, PubMed, and Scopes. The obtained studies were analyzed and organized into the process of diagnosis: wearable sensors, feature extraction, and feature selection.

Results

The search led to 171 eligible studies included in this overview. Wearable sensors such as force sensors, inertial sensors, electromyography, electroencephalography, acoustic sensors, optical fiber sensors, and global positioning systems were employed to monitor and diagnose neurodegenerative diseases. Various features including physical features, statistical features, nonlinear features, and features from the network can be extracted from these wearable sensors, and the alteration of features toward neurodegenerative diseases was illustrated. Moreover, different kinds of feature selection methods such as filter, wrapper, and embedded methods help to find the distinctive indicator of the diseases and benefit to a better diagnosis performance.

Conclusions

This systematic review enables a comprehensive understanding of wearable sensors and features for the diagnosis of neurodegenerative diseases.

An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal

The development of automatic monitoring and diagnosis systems for cardiac patients over the internet has been facilitated by recent advancements in wearable sensor devices from electrocardiographs (ECGs), which need the use of patient-specific approaches. Premature ventricular contraction (PVC) is a common chronic cardiovascular disease that can cause conditions that are potentially fatal. Therefore, for the diagnosis of likely heart failure, precise PVC detection from ECGs is crucial. In the clinical settings, cardiologists typically employ long-term ECGs as a tool to identify PVCs, where a cardiologist must put in a lot of time and effort to appropriately assess the long-term ECGs which is time consuming and cumbersome. By addressing these issues, we have investigated a deep learning method with a pre-trained deep residual network, ResNet-18, to identify PVCs automatically using transfer learning mechanism. Herein, features are extracted by the inner layers of the network automatically compared to hand-crafted feature extraction methods. Transfer learning mechanism handles the difficulties of required large volume of training data for a deep model. The pre-trained model is evaluated on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia and Institute of Cardiological Technics (INCART) datasets. First, we used the Pan-Tompkins algorithm to segment 44,103 normal and 6423 PVC beats, as well as 106,239 normal and 9987 PVC beats from the MIT-BIH Arrhythmia and IN-CART datasets, respectively. The pre-trained model employed the segmented beats as input after being converted into 2D (two-dimensional) images. The method is optimized with the using of weighted random samples, on-the-fly augmentation, Adam optimizer, and call back feature. The results from the proposed method demonstrate the satisfactory findings without the using of any complex pre-processing and feature extraction technique as well as design complexity of model. Using LOSOCV (leave one subject out cross-validation), the received accuracies on MIT-BIH and INCART are 99.93% and 99.77%, respectively, suppressing the state-of-the-art methods for PVC recognition on unseen data. This demonstrates the efficacy and generalizability of the proposed method on the imbalanced datasets. Due to the absence of device-specific (patient-specific) information at the evaluating stage on the target datasets in this study, the method might be used as a general approach to handle the situations in which ECG signals are obtained from different patients utilizing a variety of smart sensor devices.