Wearable Biosensors Research Articles

(Invited) Next-Generation Enabling Technologies for Disease Diagnosis and Therapeutic Monitoring

To enable personalized and precision medicine, it is crucial to monitor patient health status and bring information on disease-related agents and therapeutic drug molecules into the clinic. This requires new technologies to interrogate different body fluids that are rich sources of biomarkers, such as whole blood and interstitial fluid (ISF). Such technologies enable rapid, sensitive and - ultimately - real-time and continuous analysis of the clinically important biomarkers. Micro and nanofabrication and nanomaterials as well as materials chemistry and polymer and molecular engineering are crucial tools in this endeavor.At IDEATION Lab, we apply innovative engineering solutions to advance patient health monitoring using two main technologies: Microneedles and Microfluidics. Our new transdermal biosensing technologies powered by engineered hydrogel microneedles (HMNs), aptamer probes, and in-situ metallic nanoparticle synthesis enable minimally invasive, on-needle, and real-time measurement of clinically important biomarkers in ISF. Our HMN assays expect to pave the way for the next-generation of polymeric-based wearable biosensors. We developed a real-time biosensor driven by microfluidic techniques that continuously updates specific biomolecules’ fluctuating concentration levels with picomolar sensitivity directly in whole blood. For the first time, our microfluidic assay enables measuring the dynamic changes in blood insulin which is an important knowledge gap in diabetes management. Another microfluidic technology integrated with electrochemical biosensor has been recently developed in our lab that enables cervical cancer screening at point-of-care setting.These new advances enrich the level of information that can be collected from different body fluids and provide new means and potentials for highly accurate patient health status monitoring, thus transforming the field of personalized and precision medicine. Figure 1

Electrochemical Wearable Biosensors and Bioelectronic Devices Based on Hydrogels: Mechanical Properties and Electrochemical Behavior.

Hydrogel-based wearable electrochemical biosensors (HWEBs) are emerging biomedical devices that have recently received immense interest. The exceptional properties of HWEBs include excellent biocompatibility with hydrophilic nature, high porosity, tailorable permeability, the capability of reliable and accurate detection of disease biomarkers, suitable device-human interface, facile adjustability, and stimuli responsive to the nanofiller materials. Although the biomimetic three-dimensional hydrogels can immobilize bioreceptors, such as enzymes and aptamers, without any loss in their activities. However, most HWEBs suffer from low mechanical strength and electrical conductivity. Many studies have been performed on emerging electroactive nanofillers, including biomacromolecules, carbon-based materials, and inorganic and organic nanomaterials, to tackle these issues. Non-conductive hydrogels and even conductive hydrogels may be modified by nanofillers, as well as redox species. All these modifications have led to the design and development of efficient nanocomposites as electrochemical biosensors. In this review, both conductive-based and non-conductive-based hydrogels derived from natural and synthetic polymers are systematically reviewed. The main synthesis methods and characterization techniques are addressed. The mechanical properties and electrochemical behavior of HWEBs are discussed in detail. Finally, the prospects and potential applications of HWEBs in biosensing, healthcare monitoring, and clinical diagnostics are highlighted.

Breathable and Stretchable Organic Electrochemical Transistors with Laminated Porous Structures for Glucose Sensing.

Dynamic glucose monitoring is important to reduce the risk of metabolic diseases such as diabetes. Wearable biosensors based on organic electrochemical transistors (OECTs) have been developed due to their excellent signal amplification capabilities and biocompatibility. However, traditional wearable biosensors are fabricated on flat substrates with limited gas permeability, resulting in the inefficient evaporation of sweat, reduced wear comfort, and increased risk of inflammation. Here, we proposed breathable OECT-based glucose sensors by designing a porous structure to realize optimal breathable and stretchable properties. The gas permeability of the device and the relationship between electrical properties under different tensile strains were carefully investigated. The OECTs exhibit exceptional electrical properties (gm ~1.51 mS and Ion ~0.37 mA) and can retain up to about 44% of their initial performance even at 30% stretching. Furthermore, obvious responses to glucose have been demonstrated in a wide range of concentrations (10-7-10-4 M) even under 30% strain, where the normalized response to 10-4 M is 26% and 21% for the pristine sensor and under 30% strain, respectively. This work offers a new strategy for developing advanced breathable and wearable bioelectronics.

Powering a wearable sweat biosensor for continuous and non-invasive monitoring using a flexible perovskite solar cell

Powering a wearable sweat biosensor for continuous and non-invasive monitoring using a flexible perovskite solar cell

Wearable Flexible Perspiration Biosensors Using Laser-Induced Graphene and Polymeric Tape Microfluidics.

Wearable biosensors promise real-time measurements of chemicals in human sweat, with the potential for dramatic improvements in medical diagnostics and athletic performance through continuous metabolite and electrolyte monitoring. However, sweat sensing is still in its infancy, and questions remain about whether sweat can be used for medical purposes. Wearable sensors are focused on proof-of-concept designs that are not scalable for multisubject trials, which could elucidate the utility of sweat sensing for health monitoring. Moreover, many wearable sensors do not include the microfluidics necessary to protect and channel consistent and clean sweat volumes to the sensor surface or are not designed to be disposable to prevent sensor biofouling and inaccuracies due to repeated use. Hence, there is a need to produce low-cost and single-use wearable sensors with integrated microfluidics to ensure reliable sweat sensing. Herein, we demonstrate the convergence of laser-induced graphene (LIG) based sensors with soft tape polymeric microfluidics to quantify both sweat metabolites (glucose and lactate) and electrolytes (sodium) for potential hydration and fatigue monitoring. Distinct LIG-electrodes were functionalized with glucose oxidase and lactate oxidase for selective sensing of glucose and lactate across physiological ranges found in sweat with sensitivities of 26.2 and 2.47 × 10-3 μA mM-1 cm-2, detection limits of 8 and 220 μM, and linear response ranges of 0-1 mM and 0-32 mM, respectively. LIG-electrodes functionalized with a sodium-ion-selective membrane displayed Nernstian sensitivity of 58.8 mV decade-1 and a linear response over the physiological range in sweat (10-100 mM). The sensors were tested in a simulated sweating skin microfluidic system and on-body during cycling tests in a multisubject trial. Results demonstrate the utility of LIG integrated with microfluidics for real-time, continuous measurements of biological analytes in sweat and help pave the way for the development of personalized wearable diagnostic tools.

Identification and quantitative assessment of motor complications in Parkinson's disease using the Parkinson's KinetiGraph™.

Effective management and therapies for the motor complications of Parkinson's disease (PD) require appropriate clinical evaluation. The Parkinson's KinetiGraph™ (PKG) is a wearable biosensor system that can record the motion characteristics of PD objectively and remotely. The study aims to investigate the value of PKG in identifying and quantitatively assessing motor complications including motor fluctuations and dyskinesia in the Chinese PD population, as well as the correlation with the clinical scale assessments. Eighty-four subjects with PD were recruited and continuously wore the PKG for 7 days. Reports with 7-day output data were provided by the manufacturer, including the fluctuation scores (FS) and dyskinesia scores (DKS). Specialists in movement disorders used the Movement Disorder Society-Unified Parkinson's Disease Rating Scale-IV (MDS-UPDRS IV), the wearing-off questionnaire 9 (WOQ-9), and the unified dyskinesia rating scale (UDysRS) for the clinical assessment of motor complications. Spearman correlation analyses were used to evaluate the correlation between the FS and DKS recorded by the PKG and the clinical scale assessment results. Receiver operating characteristic (ROC) curves were generated to analyze the sensitivity and specificity of the FS and DKS scores in the identification of PD motor complications. The FS was significantly positively correlated with the MDS-UPDRS IV motor fluctuation (items 4.3-4.5) scores (r = 0.645, p < 0.001). ROC curve analysis showed a maximum FS cut-off value of 7.5 to identify motor fluctuation, with a sensitivity of 74.3% and specificity of 87.8%. The DKS was significantly positively correlated with the UDysRS total score (r = 0.629, p < 0.001) and the UDysRS III score (r = 0.634, p < 0.001). ROC curve analysis showed that the maximum DKS cut-off value for the diagnosis of dyskinesia was 0.7, with a sensitivity of 83.3% and a specificity of 83.3%. The PKG assessment of motor complications in the PD population analyzed in this study has a significant correlation with the clinical scale assessment, high sensitivity, and high specificity. Compared with clinical evaluations, PKG can objectively, quantitatively, and remotely identify and assess motor complications in PD, providing a good objective recording for managing motor complications.

Uncovering personalized glucose responses and circadian rhythms from multiple wearable biosensors with Bayesian dynamical modeling

Wearable biosensors and smartphone applications can measure physiological variables over multiple days in free-living conditions. We measure food and drink ingestion, glucose dynamics, physical activity, heart rate (HR), and heart rate variability (HRV) in 25 healthy participants over 14days. We develop a Bayesian inference framework to learn personal parameters that quantify circadian rhythms and physiological responses to external stressors. Modeling the effects of ingestion events on glucose levels reveals that slower glucose decay kinetics elicit larger postprandial glucose spikes, and we uncover a circadian baseline rhythm for glucose with high amplitudes in some individuals. Physical activity and circadian rhythms explain as much as 40%-65% of the HR variance, whereas the variance explained for HRV is more heterogeneous across individuals. A more complex model incorporating activity, HR, and HRV explains up to 15% of additional glucose variability, highlighting the relevance of integrating multiple biosensors to better predict glucose dynamics.

A Wearable Artificial Intelligence Feedback Tool (Wrist Angel) for Treatment and Research of Obsessive Compulsive Disorder: Protocol for a Nonrandomized Pilot Study.

Obsessive compulsive disorder (OCD) in youth is characterized by behaviors, emotions, physiological reactions, and family interaction patterns. An essential component of therapy involves increasing awareness of the links among thoughts, emotions, behaviors, bodily sensations, and family interactions. An automatic assessment tool using physiological signals from a wearable biosensor may enable continuous symptom monitoring inside and outside of the clinic and support cognitive behavioral therapy for OCD. The primary aim of this study is to evaluate the feasibility and acceptability of using a wearable biosensor to monitor OCD symptoms. The secondary aim is to explore the feasibility of developing clinical and research tools that can detect and predict OCD-relevant internal states and interpersonal processes with the use of speech and behavioral signals. Eligibility criteria for the study include children and adolescents between 8 and 17 years of age diagnosed with OCD, controls with no psychiatric diagnoses, and one parent of the participating youths. Youths and parents wear biosensors on their wrists that measure pulse, electrodermal activity, skin temperature, and acceleration. Patients and their parents mark OCD episodes, while control youths and their parents mark youth fear episodes. Continuous, in-the-wild data collection will last for 8 weeks. Controlled experiments designed to link physiological, speech, behavioral, and biochemical signals to mental states are performed at baseline and after 8 weeks. Interpersonal interactions in the experiments are filmed and coded for behavior. The films are also processed with computer vision and for speech signals. Participants complete clinical interviews and questionnaires at baseline, and at weeks 4, 7, and 8. Feasibility criteria were set for recruitment, retention, biosensor functionality and acceptability, adherence to wearing the biosensor, and safety related to the biosensor. As a first step in learning the associations between signals and OCD-related parameters, we will use paired t tests and mixed effects models with repeated measures to assess associations between oxytocin, individual biosignal features, and outcomes such as stress-rest and case-control comparisons. The first participant was enrolled on December 3, 2021, and recruitment closed on December 31, 2022. Nine patient dyads and nine control dyads were recruited. Sixteen participating dyads completed follow-up assessments. The results of this study will provide preliminary evidence for the extent to which a wearable biosensor that collects physiological signals can be used to monitor OCD severity and events in youths. If we find the study to be feasible, further studies will be conducted to integrate biosensor signals output into machine learning algorithms that can provide patients, parents, and therapists with actionable insights into OCD symptoms and treatment progress. Future definitive studies will be tasked with testing the accuracy of machine learning models to detect and predict OCD episodes and classify clinical severity. ClinicalTrials.gov NCT05064527; https://clinicaltrials.gov/ct2/show/NCT05064527. DERR1-10.2196/45123.

Use of Wearable Sensor Device and Mobile Application for Objective Assessment of Pain in Post-surgical Patients: A Preliminary Study.

Effective post-operative pain management requires an accurate and frequent assessment of the pain experienced by the patients. The current gold-standard of pain assessment is through patient self-evaluation (e.g., numeric rating scale, NRS) which is subjective, prone to recall-bias, and does not provide comprehensive information of the pain intensity and its trends. We conducted a study to explore the potential of wearable biosensors and machine learning-based analysis of physiological parameters to estimate the pain intensity. The results from our study of post-operative knee surgery patients monitored over a period of 30 days demonstrate the feasibility of the system in ambulatory setting, with a substantial agreement (Cohen's Kappa = 0.70, 95% CI 0.68-0.72) between the pain intensity estimation and the patient reported numerical rating scale. Therefore, the wearable biosensors coupled with the machine learning-derived pain estimation are capable of remotely assessing the pain intensity.

Epidermal Wearable Biosensors for the Continuous Monitoring of Biomarkers of Chronic Disease in Interstitial Fluid.

Healthcare technology has allowed individuals to monitor and track various physiological and biological parameters. With the growing trend of the use of the internet of things and big data, wearable biosensors have shown great potential in gaining access to the human body, and providing additional functionality to analyze physiological and biochemical information, which has led to a better personalized and more efficient healthcare. In this review, we summarize the biomarkers in interstitial fluid, introduce and explain the extraction methods for interstitial fluid, and discuss the application of epidermal wearable biosensors for the continuous monitoring of markers in clinical biology. In addition, the current needs, development prospects and challenges are briefly discussed.

An autonomous wearable biosensor powered by a perovskite solar cell.

Wearable sweat sensors can potentially be used to continuously and non-invasively monitor physicochemical biomarkers that contain information related to disease diagnostics and fitness tracking. However, the development of such autonomous sensors faces a number of challenges including achieving steady sweat extraction for continuous and prolonged monitoring, and addressing the high power demands of multifunctional and complex analysis. Here we report an autonomous wearable biosensor that is powered by a perovskite solar cell and can provide continuous and non-invasive metabolic monitoring. The device uses a flexible quasi-two-dimensional perovskite solar cell module that provides ample power under outdoor and indoor illumination conditions (power conversion efficiency exceeding 31% under indoor light illumination). We show that the wearable device can continuously collect multimodal physicochemical data - glucose, pH, sodium ions, sweat rate, and skin temperature - across indoor and outdoor physical activities for over 12 hours.

Wearable and Mobile Technologies for the Evaluation and Treatment of Obsessive-Compulsive Disorder: Scoping Review.

Smartphones and wearable biosensors can continuously and passively measure aspects of behavior and physiology while also collecting data that require user input. These devices can potentially be used to monitor symptom burden; estimate diagnosis and risk for relapse; predict treatment response; and deliver digital interventions in patients with obsessive-compulsive disorder (OCD), a prevalent and disabling psychiatric condition that often follows a chronic and fluctuating course and may uniquely benefit from these technologies. Given the speed at which mobile and wearable technologies are being developed and implemented in clinical settings, a continual reappraisal of this field is needed. In this scoping review, we map the literature on the use of wearable devices and smartphone-based devices or apps in the assessment, monitoring, or treatment of OCD. In July 2022 and April 2023, we conducted an initial search and an updated search, respectively, of multiple databases, including PubMed, Embase, APA PsycINFO, and Web of Science, with no restriction on publication period, using the following search strategy: ("OCD" OR "obsessive" OR "obsessive-compulsive") AND ("smartphone" OR "phone" OR "wearable" OR "sensing" OR "biofeedback" OR "neurofeedback" OR "neuro feedback" OR "digital" OR "phenotyping" OR "mobile" OR "heart rate variability" OR "actigraphy" OR "actimetry" OR "biosignals" OR "biomarker" OR "signals" OR "mobile health"). We analyzed 2748 articles, reviewed the full text of 77 articles, and extracted data from the 25 articles included in this review. We divided our review into the following three parts: studies without digital or mobile intervention and with passive data collection, studies without digital or mobile intervention and with active or mixed data collection, and studies with a digital or mobile intervention. Use of mobile and wearable technologies for OCD has developed primarily in the past 15 years, with an increasing pace of related publications. Passive measures from actigraphy generally match subjective reports. Ecological momentary assessment is well tolerated for the naturalistic assessment of symptoms, may capture novel OCD symptoms, and may also document lower symptom burden than retrospective recall. Digital or mobile treatments are diverse; however, they generally provide some improvement in OCD symptom burden. Finally, ongoing work is needed for a safe and trusted uptake of technology by patients and providers.

High performance zwitterionic hydrogels for ECG/EMG signals monitoring

Zwitterionic hydrogel is receiving growing interest due to their unique structure and properties. Nevertheless, overcoming the problem of weak mechanical strength and integrating multiple functions into one zwitterionic hydrogel is still a great challenge. Herein, we have designed an ion-conductive (3.0 S/m) zwitterionic hydrogel featured with many desirable properties of high strength (134 kPa), transparency (the average light transmission closes to 90%), flexibility (up to 287%), self-adhesion (187.5 N/m), low contact impedance (79 Ω) and fatigue resistance. The zwitterionic hydrogel exhibits good flexibility and fatigue resistance due to the combination of chemical cross-linking and hydrogen bonding in the structure. Additionally, it can adhere extensively to various substrate surfaces through intermolecular forces and detect small physical movements, simultaneously capturing the ECG and EMG signals of the body with precision and stability. It also demonstrates higher sensitivity (0.46) and lower RMS noise (6.83 μV) in static measurements than those of current commercial electrodes. We believe that the designed zwitterionic hydrogel has promising applications in the field of wearable biosensors.

Uninterrupted real-time cerebral stress level monitoring using wearable biosensors: A review.

Stress is the major unseen bug for the health of humans with the increasing workaholic era. Long periods of avoidance are the main precursor for chronic disorders that are quite tough to treat. As precaution is better than cure, stress detection and monitoring are vital. Although there are ways to measure stress clinically, there is still a constant need and demand for methods that measure stress personally and in an ex vitro manner for the convenience of the user. The concept of continuous stress monitoring has been introduced to tackle the issue of unseen stress accumulating in the body simultaneously with being user-friendly and reliable. Stress biosensors nowadays provide real-time, noninvasive, and continuous monitoring of stress. These biosensors are innovative anthropogenic creations that are a combination of biomarkers and indicators like heart rate variation, electrodermal activity, skin temperature, galvanic skin response, and electroencephalograph of stress in the body along with machine learning algorithms and techniques. The collaboration of biological markers, artificial intelligence techniques, and data science tools makes stress biosensors a hot topic for research. These attributes have made continuous stress detection a possibility with ease. The advancement in stress biosensing technologies has made a great impact on the lives of human beings so far. This article focuses on the comprehensive study of stress-indicating biomarkers and the techniques along with principles of the biosensors used for continuous stress detection. The precise overview of wearable stress monitoring systems is also sectioned to pave a pathway for possible future research studies.

A Flexible and Wearable Chemiresistive Biosensor Fabricated by Laser Inducing for Real‐Time Glucose Analysis of Sweat

AbstractIn this study, a flexible and wearable chemiresistive biosensor (FWCB) is developed for the real‐time analysis of glucose in sweat on the human skin surface based on a novel detection strategy of p‐type reduced graphene oxide (rGO) sensing film, which met the requirements of rapid, nondestructive testing. The proposed FWCB is fabricated in the form of interdigital electrodes (IEs) made of laser‐induced graphene (LIG) synthesized by the laser inducing of a polyimide (PI) film. Additionally, a semiconducting rGO sensing film modified on the surface of IEs is synthesized by thermal reduction of graphene oxide (GO), which is functionalized with glucose oxidase (GOx) by chemical cross‐linking to obtain GOx/FWCB. Moreover, the key parameters for FWCB fabrication are optimized, and the sensing strategy of the proposed GOx/FWCB is also investigated. The results show that the proposed GOx/FWCB can be used for the detection of glucose in the range of 0.01–3.0 mM with satisfactory selectivity, and the limit of detection (LOD) is calculated to be as low as 0.8 µM (S/N = 3). These dramatic advantages endow the proposed FWCB with broad application prospects in the field of portable, wearable, and real‐time detection of glucose in human sweat for health monitoring.

Non-Invasive Wearable Sweat and Tear-Based Biosensors for Continuous Health Monitoring

Over the years, the continuous advancements within the wearable biosensor field have raised public awareness in exploring new strategies for people's personalized point-of-care testing. Biosensors are multifunctional devices that allow people to quantify a range of biological signals through highly sensitive and small-scale sensing platforms, thus providing users convenience when limiting the need for clinical check-ups and laboratory diagnosis. Via dynamic, non-invasive evaluation of biomarkers in bodily fluids, biosensors are able to provide users with a nearly instant numerical result of the targeted biomarker's level (e.g., glucose, chloride) within their body. Here, this paper mainly focuses on exploring the wide range of sweat and tear-based biosensors' applications and methods to some extent. New generations of sweat-based wearable biosensors have been developed to better monitor one's health status by introducing techniques such as microfluidic sweat collection and Iontophoresis sweat induction methods. Additionally, much investment and effort have been put into developing tear-based wearable biosensors. For example, contact lens-based sensors are the most commonly adopted method for tear analysis, providing a minimally invasive detection of biomarkers.

Wearable Insulin Biosensors for Diabetes Management: Advances and Challenges.

We present a critical review of the current progress in wearable insulin biosensors. For over 40 years, glucose biosensors have been used for diabetes management. Measurement of blood glucose is an indirect method for calculating the insulin administration dosage, which is critical for insulin-dependent diabetic patients. Research and development efforts aiming towards continuous-insulin-monitoring biosensors in combination with existing glucose biosensors are expected to offer a more accurate estimation of insulin sensitivity, regulate insulin dosage and facilitate progress towards development of a reliable artificial pancreas, as an ultimate goal in diabetes management and personalised medicine. Conventional laboratory analytical techniques for insulin detection are expensive and time-consuming and lack a real-time monitoring capability. On the other hand, biosensors offer point-of-care testing, continuous monitoring, miniaturisation, high specificity and sensitivity, rapid response time, ease of use and low costs. Current research, future developments and challenges in insulin biosensor technology are reviewed and assessed. Different insulin biosensor categories such as aptamer-based, molecularly imprinted polymer (MIP)-based, label-free and other types are presented among the latest developments in the field. This multidisciplinary field requires engagement between scientists, engineers, clinicians and industry for addressing the challenges for a commercial, reliable, real-time-monitoring wearable insulin biosensor.

Personal healthcare data records analysis and monitoring using the internet of things and cloud computing

This paper presents a proposed system for cloud-based and Internet o Things (IoT)-based electronic health records (EHRs) that utilize wearable body biosensors to collect biometric data in real time and analyze it to provide personalized therapy recommendations. The study aims to ensure the confidentiality and integrity of patient data through security authorizations, device authentication, and encrypted communication channels. The research method involves describing the proposed system’s communication environment and trust boundary and illustrating the communication mechanisms, including the “starting” and “authentication” procedures. The proposed communication protocols are also explained in detail, and a complete illustration of the symbols and abbreviations used throughout the work is provided. The initialization process involves contacting a body sensor network (BSN) server to register, generating a secret key, and assigning a track sequence number. The proposed systematic verification involves a dependable authentication solution to maintain secure communication between the biosensors, local processing unit (LPU), and BSN server. The verification process involves figuring out the values and generating a random integer, establishing communication with the receiver, and validating the data by searching for a corresponding tuple in the required database. The study emphasizes the importance of a robust IoT communication architecture to ensure secure data transfer between devices, networks, and individuals.

A Computationally Assisted Approach for Designing Wearable Biosensors toward Non-Invasive Personalized Molecular Analysis.

Wearable sweat sensors have the potential to revolutionize precision medicine as they can non-invasively collect molecular information closely associated with an individual's health status. However, the majority of clinically relevant biomarkers cannot be continuously detected in situ using existing wearable approaches. Molecularly imprinted polymers (MIPs) are a promising candidate to address this challenge but haven't yet gained widespread use due to their complex design and optimization process yielding variable selectivity. Here, QuantumDock is introduced, an automated computational framework for universal MIP development toward wearable applications. QuantumDock utilizes density functional theory to probe molecular interactions between monomers and the target/interferent molecules to optimize selectivity, a fundamentally limiting factor for MIP development toward wearable sensing. A molecular docking approach is employed to explore a wide range of known and unknown monomers, and to identify the optimal monomer/cross-linker choice for subsequent MIP fabrication. Using an essential amino acid phenylalanine as the exemplar, experimental validation of QuantumDock is performed successfully using solution-synthesized MIP nanoparticles coupled with ultraviolet-visible spectroscopy. Moreover, a QuantumDock-optimized graphene-based wearable device is designed that can perform autonomous sweat induction, sampling, and sensing. For the first time, wearable non-invasive phenylalanine monitoring is demonstrated in human subjects toward personalized healthcare applications.

Screen-Printed Textile-Based Electrochemical Biosensor for Noninvasive Monitoring of Glucose in Sweat.

Wearable sweat biosensors for noninvasive monitoring of health parameters have attracted significant attention. Having these biosensors embedded in textile substrates can provide a convenient experience due to their soft and flexible nature that conforms to the skin, creating good contact for long-term use. These biosensors can be easily integrated with everyday clothing by using textile fabrication processes to enhance affordable and scalable manufacturing. Herein, a flexible electrochemical glucose sensor that can be screen-printed onto a textile substrate has been demonstrated. The screen-printed textile-based glucose biosensor achieved a linear response in the range of 20-1000 µM of glucose concentration and high sensitivity (18.41 µA mM-1 cm-2, R2 = 0.996). In addition, the biosensors show high selectivity toward glucose among other interfering analytes and excellent stability over 30 days of storage. The developed textile-based biosensor can serve as a platform for monitoring bio analytes in sweat, and it is expected to impact the next generation of wearable devices.