Wearable Biosensors Research Articles

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.

A Digital Mental Health App Incorporating Wearable Biosensing for Teachers of Children on the Autism Spectrum to Support Emotion Regulation: Protocol for a Pilot Randomized Controlled Trial.

As much as 80% of children on the autism spectrum exhibit challenging behaviors (ie, behaviors dangerous to the self or others, behaviors that interfere with learning and development, and behaviors that interfere with socialization) that can have a devastating impact on personal and family well-being, contribute to teacher burnout, and even require hospitalization. Evidence-based practices to reduce these behaviors emphasize identifying triggers (events or antecedents that lead to challenging behaviors); however, parents and teachers often report that challenging behaviors surface with little warning. Exciting recent advances in biometric sensing and mobile computing technology allow the measurement of momentary emotion dysregulation using physiological indexes. We present the framework and protocol for a pilot trial that will test a mobile digital mental health app, the KeepCalm app. School-based approaches to managing challenging behaviors in children on the autism spectrum are limited by 3 key factors: children on the autism spectrum often have difficulties in communicating their emotions; it is challenging to implement evidence-based, personalized strategies for individual children in group settings; and it is difficult for teachers to track which strategies are successful for each child. KeepCalm aims to address those barriers by communicating children's stress to their teachers using physiological signaling (emotion dysregulation detection), supporting the implementation of emotion regulation strategies via smartphone pop-up notifications of top strategies for each child according to their behavior (emotion regulation strategy implementation), and easing the task of tracking outcomes by providing the child's educational team with a tool to track the most effective emotion regulation strategies for that child based on physiological stress reduction data (emotion regulation strategy evaluation). We will test KeepCalm with 20 educational teams of students on the autism spectrum with challenging behaviors (no exclusion based on IQ or speaking ability) in a pilot randomized waitlist-controlled field trial over a 3-month period. We will examine the usability, acceptability, feasibility, and appropriateness of KeepCalm as primary outcomes. Secondary preliminary efficacy outcomes include clinical decision support success, false positives or false negatives of stress alerts, and the reduction of challenging behaviors and emotion dysregulation. We will also examine technical outcomes, including the number of artifacts and the proportion of time children are engaged in high physical movement based on accelerometry data; test the feasibility of our recruitment strategies; and test the response rate and sensitivity to change of our measures, in preparation for a future fully powered large-scale randomized controlled trial. The pilot trial will begin by September 2023. Results will provide key data about important aspects of implementing KeepCalm in preschools and elementary schools and will provide preliminary data about its efficacy to reduce challenging behaviors and support emotion regulation in children on the autism spectrum. ClinicalTrials.gov NCT05277194; https://www.clinicaltrials.gov/ct2/show/NCT05277194. PRR1-10.2196/45852.

A wireless patch for the monitoring of C-reactive protein in sweat.

The quantification of protein biomarkers in blood at picomolar-level sensitivity requires labour-intensive incubation and washing steps. Sensing proteins in sweat, which would allow for point-of-care monitoring, is hindered by the typically large interpersonal and intrapersonal variations in its composition. Here we report the design and performance of a wearable and wireless patch for the real-time electrochemical detection of the inflammatory biomarker C-reactive (CRP) protein in sweat. The device integrates iontophoretic sweat extraction, microfluidic channels for sweat sampling and for reagent routing and replacement, and a graphene-based sensor array for quantifying CRP (via an electrode functionalized with anti-CRP capture antibodies-conjugated gold nanoparticles), ionic strength, pH and temperature for the real-time calibration of the CRP sensor. In patients with chronic obstructive pulmonary disease, with active or past infections or who had heart failure, the elevated concentrations of CRP measured via the patch correlated well with the protein's levels in serum. Wearable biosensors for the real-time sensitive analysis of inflammatory proteins in sweat may facilitate the management of chronic diseases.

A transparent, anti-fatigue, flexible multifunctional hydrogel with self-adhesion and conductivity for biosensors

Hydrogels have received increasing interest as a flexible material. However, the integration of multiple functions into one hydrogel remains a great challenge. For the application of hydrogels in the field of biosensors, both rigidity and toughness of hydrogels are usually necessary. Herein, a kind of multifunctional hydrogel with a combination of ideal properties, including transparency, fatigue resistance, flexibility, conductivity, self-adhesive and low contact impedance is designed. The hydrogel is prepared via a one-pot method by polymerizing the monomers acrylic acid (AA), acrylamide (AM) and [2-(Methacryloyloxy) ethyl] dimethyl-(3-sulfo-propyl) ammonium hydroxide (SBMA) under UV light to form a copolymer. The chemical cross-linking and hydrogen bonding jointly impart the hydrogel with good fatigue resistance and flexibility. Furthermore, the hydrogel can adhere to a wide range of substrate surfaces through intermolecular forces and give precise, stable and clear ECG signals when detecting small physical movements. It is believed that the designed hydrogel will show great prospects in wearable devices and biosensors.

Gold-decorated laser-induced graphene for wearable biosensing and joule heating applications

BackgroundLaser-induced graphene from polyimide (PI) substrate has attracted much attention due to its controllable morphology and patternable structure. However, the property of polyimide-derived graphene (PDG) should be further improved to meet the requirement of next-generation flexible devices. MethodsIn this article, Au-modified PDG has been fabricated via the CO2 laser scribing on the common PI substrate. The obtained PDG with a foam-like morphology can provide large accessible sites for the deposition of Au nanoparticles (ANPs) by thermal evaporation. Significant findingsThe ANP-PDG devices have been utilized as electrochemical and surface-enhanced Raman scattering (SERS) sensors for the detection of dopamine (DA), Rhodamine 6G (R6G), and Adenine (Ad), respectively. The properties of the electrothermal heater based on the ANP-PDG films are also investigated. Due to the high conductivity and unique plasmonic effect of ANPs, the ANP-PDG displays higher electrocatalytic activity and stronger SERS signals than that of bare PDG film. The ANP-PDG also can reach higher steady-state temperatures (Ts) compared to the bare PDG heater. Furthermore, the ANP-PDG devices precisely defined on the PI substrate reveal great mechanical flexibility and the sheet resistance is very stable without obvious change, which can be applied for next-generation flexible electronics.

Updates of Wearing Devices (WDs) In Healthcare, And Disease Monitoring

With the rising pervasiveness of growing populace, aging and chronic illnesses consistently rising medical services costs, the health care system is going through a crucial change from the conventional hospital focused system to an individual-focused system. Since the twentieth century, wearable sensors are becoming widespread in medical care and biomedical monitoring systems, engaging consistent estimation of biomarkers for checking of the diseased condition and wellbeing, clinical diagnostics and assessment in biological fluids like saliva, blood, and sweat. Recently, the improvements have been centered around electrochemical and optical biosensors, alongside advances with the non-invasive monitoring of biomarkers, bacteria and hormones, etc. Wearable devices have created with a mix of multiplexed biosensing, microfluidic testing and transport frameworks incorporated with flexible materials and body connections for additional created wear ability and effortlessness. These wearables hold guarantee and are fit for a higher understanding of the relationships between analyte focuses inside the blood or non-invasive biofluids and feedback to the patient, which is fundamentally significant in ideal finding, therapy, and control of diseases. In any case, cohort validation studies and execution assessment of wearable biosensors are expected to support their clinical acceptance. In the current review, we discussed the significance, highlights, types of wearables, difficulties and utilizations of wearable devices for biological fluids for the prevention of diseased conditions and real time monitoring of human wellbeing. In this, we sum up the different wearable devices that are developed for health care monitoring and their future potential has been discussed in detail.

Current Innovations in Intraocular Pressure Monitoring Biosensors for Diagnosis and Treatment of Glaucoma—Novel Strategies and Future Perspectives

Biosensors are devices that quantify biologically significant information required for diverse applications, such as disease diagnosis, food safety, drug discovery and detection of environmental pollutants. Recent advancements in microfluidics, nanotechnology and electronics have led to the development of novel implantable and wearable biosensors for the expedient monitoring of diseases such as diabetes, glaucoma and cancer. Glaucoma is an ocular disease which ranks as the second leading cause for loss of vision. It is characterized by the increase in intraocular pressure (IOP) in human eyes, which results in irreversible blindness. Currently, the reduction of IOP is the only treatment used to manage glaucoma. However, the success rate of medicines used to treat glaucoma is quite minimal due to their curbed bioavailability and reduced therapeutic efficacy. The drugs must pass through various barriers to reach the intraocular space, which in turn serves as a major challenge in glaucoma treatment. Rapid progress has been observed in nano-drug delivery systems for the early diagnosis and prompt therapy of ocular diseases. This review gives a deep insight into the current advancements in the field of nanotechnology for detecting and treating glaucoma, as well as for the continuous monitoring of IOP. Various nanotechnology-based achievements, such as nanoparticle/nanofiber-based contact lenses and biosensors that can efficiently monitor IOP for the efficient detection of glaucoma, are also discussed.