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Herein we introduce a novel water-based graphite ink modified with multiwalled carbon nanotubes, designed for the development of the first wearable self-powered biosensor enabling alcohol abuse detection through sweat analysis. The stencil-printed graphite (SPG) electrodes, printed onto a flexible substrate, were modified by casting multiwalled carbon nanotubes (MWCNTs), electrodepositing polymethylene blue (pMB) at the anode to serve as a catalyst for nicotinamide adenine dinucleotide (NADH) oxidation, and hemin at the cathode as a selective catalyst for H2O2 reduction. Notably, alcohol dehydrogenase (ADH) was additionally physisorbed onto the anodic electrode, and alcohol oxidase (AOx) onto the cathodic electrode. The self-powered biosensor was assembled using the ADH/pMB-MWCNTs/SPG||AOx/Hemin-MWCNTs/SPG configuration, enabling the detection of ethanol as an analytical target, both at the anodic and cathodic electrodes. Its performance was assessed by measuring polarization curves with gradually increasing ethanol concentrations ranging from 0 to 50 mM. The biosensor demonstrated a linear detection range from 0.01 to 0.3 mM, with a detection limit (LOD) of 3 ± 1 µM and a sensitivity of 64 ± 2 μW mM−1, with a correlation coefficient of 0.98 (RSD 8.1%, n = 10 electrode pairs). It exhibited robust operational stability (over 2800 s with continuous ethanol turnover) and excellent storage stability (approximately 93% of initial signal retained after 90 days). Finally, the biosensor array was integrated into a wristband and successfully evaluated for continuous alcohol abuse monitoring. This proposed system displays promising attributes for use as a flexible and wearable biosensor employing biocompatible water-based inks, offering potential applications in forensic contexts. A novel water-based graphite ink modified with multiwalled carbon nanotubes designed for the development of a wearable self-powered biosensor enabling alcohol abuse detection through sweat analysis. [ABSTRACT FROM AUTHOR]

With the rapid development of wearable biosensor technology, the combination of head-mounted displays and augmented reality (AR) technology has shown great potential for health monitoring and biomedical diagnosis applications. However, further optimizing its performance and improving data interaction accuracy remain crucial issues that must be addressed. In this study, we develop smart glasses based on augmented reality and eye tracking technology. Through real-time information interaction with the server, the smart glasses realize accurate scene perception and analysis of the user's intention and combine with mixed-reality display technology to provide dynamic and real-time intelligent interaction services. A multi-level hardware architecture and optimized data processing process are adopted during the research process to enhance the system's real-time accuracy. Meanwhile, combining the deep learning method with the geometric model significantly improves the system's ability to perceive user behavior and environmental information in complex environments. The experimental results show that when the distance between the subject and the display is 1 m, the eye tracking accuracy of the smart glasses can reach 1.0° with an error of no more than ±0.1°. This study demonstrates that the effective integration of AR and eye tracking technology dramatically improves the functional performance of smart glasses in multiple scenarios. Future research will further optimize smart glasses' algorithms and hardware performance, enhance their application potential in daily health monitoring and medical diagnosis, and provide more possibilities for the innovative development of wearable devices in medical and health management. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs) are pivotal in health care and wearable technologies, enabling seamless communication between miniature sensors and devices on or within the human body. These biosensors capture critical physiological parameters, ranging from body temperature and blood oxygen levels to real-time electrocardiogram readings. However, WBANs face significant challenges during and after deployment, including energy conservation, security, reliability, and failure vulnerability. Sensor nodes, which are often battery-operated, expend considerable energy during sensing and transmission due to inherent spatiotemporal patterns in biomedical data streams. This paper provides a comprehensive survey of data-driven approaches that address these challenges, focusing on device placement and routing, sampling rate calibration, and the application of machine learning (ML) and statistical learning techniques to enhance network performance. Additionally, we validate three existing models (statistical, ML, and coding-based models) using two real datasets, namely the MIMIC clinical database and biomarkers collected from six subjects with a prototype biosensing device developed by our team. Our findings offer insights into strategies for optimizing energy efficiency while ensuring security and reliability in WBANs. We conclude by outlining future directions to leverage approaches to meet the evolving demands of healthcare applications. [ABSTRACT FROM AUTHOR]

Out-of-hospital cardiac arrest (OHCA) is a global health problem affecting approximately 4.4 million individuals yearly. OHCA has a poor survival rate, specifically when unwitnessed (accounting for up to 75% of cases). Rapid recognition can significantly improve OHCA survival, and consumer wearables with continuous cardiopulmonary monitoring capabilities hold potential to "witness" cardiac arrest and activate emergency services. In this study, we used an arterial occlusion model to simulate cardiac arrest and investigated the ability of infrared photoplethysmogram (PPG) sensors, often utilized in consumer wearable devices, to differentiate normal cardiac pulsation, pulseless cardiac (i.e., resembling a cardiac arrest), and non-physiologic (i.e., off-body) states. Across the classification models trained and evaluated on three anatomical locations, higher classification performances were observed on the finger (macro average F1-score of 0.964 on the fingertip and 0.954 on the finger base) compared to the wrist (macro average F1-score of 0.837). The wrist-based classification model, which was trained and evaluated using all PPG measurements, including both high- and low-quality recordings, achieved a macro average precision and recall of 0.922 and 0.800, respectively. This wrist-based model, which represents the most common form factor in consumer wearables, could only capture about 43.8% of pulseless events. However, models trained and tested exclusively on high-quality recordings achieved higher classification outcomes (macro average F1-score of 0.975 on the fingertip, 0.973 on the finger base, and 0.934 on the wrist). The fingertip model had the highest performance to differentiate arterial occlusion pulselessness from normal cardiac pulsation and off-body measurements with macro average precision and recall of 0.978 and 0.972, respectively. This model was able to identify 93.7% of pulseless states (i.e., resembling a cardiac arrest event), with a 0.4% false positive rate. All classification models relied on a combination of time-, power spectral density (PSD)-, and frequency-domain features to differentiate normal cardiac pulsation, pulseless cardiac, and off-body PPG recordings. However, our best model represented an idealized detection condition, relying on ensuring high-quality PPG data for training and evaluation of machine learning algorithms. While 90.7% of our PPG recordings from the fingertip were considered of high quality, only 53.2% of the measurements from the wrist passed the quality criteria. Our findings have implications for adapting consumer wearables to provide OHCA detection, involving advancements in hardware and software to ensure high-quality measurements in real-world settings, as well as development of wearables with form factors that enable high-quality PPG data acquisition more consistently. Given these improvements, we demonstrate that OHCA detection can feasibly be made available to anyone using PPG-based consumer wearables. [ABSTRACT FROM AUTHOR]

Migraine is one of the most common neurological disorders, characterized by moderate-to-severe headache episodes. Autonomic nervous system (ANS) alterations can occur at phases of migraine attack. This study investigates patterns of ANS changes during the pre-ictal night of migraine, utilizing wearable biosensor technology in ten individuals. Various physiological, activity-based, and signal processing metrics were examined to train predictive models and understand the relationship between specific features and migraine occurrences. Data were filtered based on specified criteria for nocturnal sleep, and analysis frames ranging from 5 to 120 min were used to improve the diversity of the training sample and investigate the impact of analysis frame duration on feature significance and migraine prediction. Several models, including XGBoost (Extreme Gradient Boosting), HistGradientBoosting (Histogram-Based Gradient Boosting), Random Forest, SVM, and KNN, were trained on unbalanced data and using cost-sensitive learning with a 5:1 ratio. To evaluate the changes in features during pre-migraine nights and nights before migraine-free days, an analysis of variance (ANOVA) was performed. The results showed that the features of electrodermal activity, skin temperature, and accelerometer exhibited the highest F-statistic values and the most significant p-values in the 5 and 10 min frames, which makes them particularly useful for the early detection of migraines. The generalized prediction model using XGBoost and a 5 min analysis frame achieved 0.806 for accuracy, 0.638 for precision, 0.595 for recall, and 0.607 for F1-score. Despite identifying distinguishing features between pre-migraine and migraine-free nights, the performance of the current model suggests the need for further improvements for clinical application. [ABSTRACT FROM AUTHOR]

Abstract Out-of-hospital cardiac arrest (OHCA) is a global health problem affecting approximately 4.4 million individuals yearly. OHCA has a poor survival rate, specifically when unwitnessed (accounting for up to 75% of cases). Rapid recognition can significantly improve OHCA survival, and consumer wearables with continuous cardiopulmonary monitoring capabilities hold potential to “witness” cardiac arrest and activate emergency services. In this study, we used an arterial occlusion model to simulate cardiac arrest and investigated the ability of infrared photoplethysmogram (PPG) sensors, often utilized in consumer wearable devices, to differentiate normal cardiac pulsation, pulseless cardiac (i.e., resembling a cardiac arrest), and non-physiologic (i.e., off-body) states. Across the classification models trained and evaluated on three anatomical locations, higher classification performances were observed on the finger (macro average F1-score of 0.964 on the fingertip and 0.954 on the finger base) compared to the wrist (macro average F1-score of 0.837). The wrist-based classification model, which was trained and evaluated using all PPG measurements, including both high- and low-quality recordings, achieved a macro average precision and recall of 0.922 and 0.800, respectively. This wrist-based model, which represents the most common form factor in consumer wearables, could only capture about 43.8% of pulseless events. However, models trained and tested exclusively on high-quality recordings achieved higher classification outcomes (macro average F1-score of 0.975 on the fingertip, 0.973 on the finger base, and 0.934 on the wrist). The fingertip model had the highest performance to differentiate arterial occlusion pulselessness from normal cardiac pulsation and off-body measurements with macro average precision and recall of 0.978 and 0.972, respectively. This model was able to identify 93.7% of pulseless states (i.e., resembling a cardiac arrest event), with a 0.4% false positive rate. All classification models relied on a combination of time-, power spectral density (PSD)-, and frequency-domain features to differentiate normal cardiac pulsation, pulseless cardiac, and off-body PPG recordings. However, our best model represented an idealized detection condition, relying on ensuring high-quality PPG data for training and evaluation of machine learning algorithms. While 90.7% of our PPG recordings from the fingertip were considered of high quality, only 53.2% of the measurements from the wrist passed the quality criteria. Our findings have implications for adapting consumer wearables to provide OHCA detection, involving advancements in hardware and software to ensure high-quality measurements in real-world settings, as well as development of wearables with form factors that enable high-quality PPG data acquisition more consistently. Given these improvements, we demonstrate that OHCA detection can feasibly be made available to anyone using PPG-based consumer wearables.

In recent years, flexible and wearable biosensor technologies have gained significant attention due to their potential to revolutionize healthcare monitoring. Among the various components involved in these biosensors, the electrode material plays a crucial role in ensuring accurate and reliable detection. In this regard, polymer electrodes, such as Poly(3,4 ethylenedioxythiophene): poly(styrenesulfonate), combined with graphene (PEDOT:PSS/graphene), have emerged as promising candidates due to their unique mechanical properties and excellent electrical conductivity. Understanding the mechanical behavior of these polymer electrodes on flexible substrates is essential to ensure the stability and durability of wearable biosensors. In this paper, PEDOT:PSS/graphene composite was spray-coated on flexible substrates at different growth conditions to explore the effect of the deposition parameters and mode of mechanical loading (longitudinal or transversal) on the electrical and mechanical behavior of the fabricated samples. It was found that the coating grown at lower temperatures and higher spraying pressure exhibited stable behavior no matter the applied stress type. [ABSTRACT FROM AUTHOR]

Although batteries are a highly popular energy source in biosensors, batteries can be an economic limitation for low‐cost sensing applications and pose significant challenges in miniaturization, biocompatibility, and disposal. To surmount such issues, "self‐powered sensors" gain the spotlight thanks to their energy harvesting from the environment through embedded miniaturized systems. In this review, the recent developments in self‐powered devices are summarized with a specific focus on the integration of supercapacitors with sensors and biosensors. The working principles of microsupercapacitors, fabrication methods, their integration with biosensors, and their ultimate applications (i.e., biomedical monitoring and analytical biomarker detection) are described. Different energy harvesting systems are summarized and their integration with self‐powered sensors or biosensors is highlighted. The limitations and challenges of the existing approaches and the future of supercapacitor‐integrated sensing systems are also critically discussed. [ABSTRACT FROM AUTHOR]

Objective: This study was undertaken to develop and evaluate a machine learning‐based algorithm for the detection of focal to bilateral tonic–clonic seizures (FBTCS) using a novel multimodal connected shirt. Methods: We prospectively recruited patients with epilepsy admitted to our epilepsy monitoring unit and asked them to wear the connected shirt while under simultaneous video‐electroencephalographic monitoring. Electrocardiographic (ECG) and accelerometric (ACC) signals recorded with the connected shirt were used for the development of the seizure detection algorithm. First, we used a sliding window to extract linear and nonlinear features from both ECG and ACC signals. Then, we trained an extreme gradient boosting algorithm (XGBoost) to detect FBTCS according to seizure onset and offset annotated by three board‐certified epileptologists. Finally, we applied a postprocessing step to regularize the classification output. A patientwise nested cross‐validation was implemented to evaluate the performances in terms of sensitivity, false alarm rate (FAR), time in false warning (TiW), detection latency, and receiver operating characteristic area under the curve (ROC‐AUC). Results: We recorded 66 FBTCS from 42 patients who wore the connected shirt for a total of 8067 continuous hours. The XGBoost algorithm reached a sensitivity of 84.8% (56/66 seizures), with a median FAR of.55/24 h and a median TiW of 10 s/alarm. ROC‐AUC was.90 (95% confidence interval =.88–.91). Median detection latency from the time of progression to the bilateral tonic–clonic phase was 25.5 s. Significance: The novel connected shirt allowed accurate detection of FBTCS with a low false alarm rate in a hospital setting. Prospective studies in a residential setting with a real‐time and online seizure detection algorithm are required to validate the performance and usability of this device. [ABSTRACT FROM AUTHOR]

Although the flourishing of the Internet of Things and artificial intelligence has accelerated the development of wearable smart bioelectronics, heavy reliance on external power remains a problem that needs to be solved. Thermoelectric materials have emerged as a promising solution, efficiently converting body heat into electrical energy to provide a stable and unrestricted power supply for wearables. Moreover, in the field of wearable thermoelectric biosensing, where flexibility is highly demanded, hydrogels with excellent electrical conductivity, flexibility and biocompatibility through structural and compositional optimization have become ideal materials for constructing biosensors and meet the diverse application needs of wearable bioelectronics. This article systematically reviews the latest research progress on thermoelectric gels for self-powered wearable biosensing, including the principles of thermoelectric operation, as well as the preparation, design, and application of thermoelectric hydrogels. The current state of thermoelectric gel-based wearable applications in the fields of temperature sensing, strain sensing, temperature-strain synergistic sensing, respiratory monitoring, and sweat analysis are displayed in the article. Finally, the paper summarizes the current challenges and prospects of thermoelectric gels in self-powered wearable bioelectronics, encouraging the rapid application and realization of thermoelectric gel-based smart wearables.

Wearable biosensors, in conjunction with machine learning, have been employed to develop less invasive monitoring techniques for assessing stress among construction workers during fieldwork. However, existing techniques face limitations in terms of scalable field application due to their subject and context dependency; it is difficult to apply them to new people in new contexts without additional labeled data collection. Therefore, this study developed a stress detection technique that incorporates domain adaptation, simultaneously learning a classifier and a subject- and context-independent features, in this way advancing generalizability. The proposed technique consistently demonstrated superior accuracy compared with benchmarks in classifying stress levels within a testing data set whose subjects and contexts were different from those of training data sets. Thus, the technique can advance generalizability across subjects and contexts. This finding can help us to reliably detect stress for new people in new contexts without additional labeled data collection, thereby contributing to scalable field application of wearable-based stress monitoring at construction sites. [ABSTRACT FROM AUTHOR]

Individual grip strength provides a functional window into somatosensory processes and their effects on motor behaviour in healthy, impaired, and ageing individuals. Variations in grip strength during hand–tool interaction are therefore exploited in a variety of experimental tasks to study the effects of pathology or ageing-related changes on sensory, motor, and cognitive ability. However, many different factors may influence individual grip strength systematically in a given task context without being explicitly identified and controlled for. Grip strength may vary as a function of the location of the measurement device (sensor) on the fingers/hand, the shape, weight and size of object(s) being gripped, the type of grip investigated (static versus dynamic grip), and the hand (dominant versus non-dominant) used for gripping. This study tests for additional factors such as sight, sound, and interactions with/between any of the other factors in a complex task context. A wearable biosensor system, designed for measuring grip strength variations in operators gripping cylindrical objects bi-manually, was used. Grip force signals were recorded from all sensors of the wearable (glove) system, including three directly task-relevant sensors for bi-manually gripping cylindrical objects with the dominant and non-dominant hands. Five young male participants were tested for the effects of sound, movement, and sight on grip strength. The participants had to pick up two cylindrical objects of identical size and weight, then hold them still (static grip) or move them upwards and downwards (dynamic grip) for ten seconds while listening to soft or hard music, with their eyes open or blindfolded. Significant effects of sensor location, hand, movement, sight, and sound on bi-manual grip strength were found. Stronger grip force signals were produced by task-relevant sensors in the dominant hand when moving the cylindrical handles (dynamic grip) in comparison with the static grip condition, depending, as expected, on whether grip signals were measured from the dominant or the non-dominant hand. Significantly weaker grip strength was produced blindfolded (sight condition), and grips were significantly stronger with exposure to harder music (sound factor). It is concluded that grip strength is significantly influenced by sensory factors and interactions between the other factors tested for, pointing towards the need for identifying and systematically controlling such potential sources of variation in complex study task contexts. [ABSTRACT FROM AUTHOR]

Although batteries are a highly popular energy source in biosensors, batteries can be an economic limitation for low‐cost sensing applications and pose significant challenges in miniaturization, biocompatibility, and disposal. To surmount such issues, “self‐powered sensors” gain the spotlight thanks to their energy harvesting from the environment through embedded miniaturized systems. In this review, the recent developments in self‐powered devices are summarized with a specific focus on the integration of supercapacitors with sensors and biosensors. The working principles of microsupercapacitors, fabrication methods, their integration with biosensors, and their ultimate applications (i.e., biomedical monitoring and analytical biomarker detection) are described. Different energy harvesting systems are summarized and their integration with self‐powered sensors or biosensors is highlighted. The limitations and challenges of the existing approaches and the future of supercapacitor‐integrated sensing systems are also critically discussed.

Wireless Body Area Networks (WBANs) are pivotal in health care and wearable technologies, enabling seamless communication between miniature sensors and devices on or within the human body. These biosensors capture critical physiological parameters, ranging from body temperature and blood oxygen levels to real-time electrocardiogram readings. However, WBANs face significant challenges during and after deployment, including energy conservation, security, reliability, and failure vulnerability. Sensor nodes, which are often battery-operated, expend considerable energy during sensing and transmission due to inherent spatiotemporal patterns in biomedical data streams. This paper provides a comprehensive survey of data-driven approaches that address these challenges, focusing on device placement and routing, sampling rate calibration, and the application of machine learning (ML) and statistical learning techniques to enhance network performance. Additionally, we validate three existing models (statistical, ML, and coding-based models) using two real datasets, namely the MIMIC clinical database and biomarkers collected from six subjects with a prototype biosensing device developed by our team. Our findings offer insights into strategies for optimizing energy efficiency while ensuring security and reliability in WBANs. We conclude by outlining future directions to leverage approaches to meet the evolving demands of healthcare applications.

With the rapid development of wearable biosensor technology, the combination of head-mounted displays and augmented reality (AR) technology has shown great potential for health monitoring and biomedical diagnosis applications. However, further optimizing its performance and improving data interaction accuracy remain crucial issues that must be addressed. In this study, we develop smart glasses based on augmented reality and eye tracking technology. Through real-time information interaction with the server, the smart glasses realize accurate scene perception and analysis of the user’s intention and combine with mixed-reality display technology to provide dynamic and real-time intelligent interaction services. A multi-level hardware architecture and optimized data processing process are adopted during the research process to enhance the system’s real-time accuracy. Meanwhile, combining the deep learning method with the geometric model significantly improves the system’s ability to perceive user behavior and environmental information in complex environments. The experimental results show that when the distance between the subject and the display is 1 m, the eye tracking accuracy of the smart glasses can reach 1.0° with an error of no more than ±0.1°. This study demonstrates that the effective integration of AR and eye tracking technology dramatically improves the functional performance of smart glasses in multiple scenarios. Future research will further optimize smart glasses’ algorithms and hardware performance, enhance their application potential in daily health monitoring and medical diagnosis, and provide more possibilities for the innovative development of wearable devices in medical and health management.

Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model (n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10−3|0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10−3, RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction. [ABSTRACT FROM AUTHOR]

Notably, 3D-printed flexible and wearable biosensors have immense potential to interact with the human body noninvasively for the real-time and continuous health monitoring of physiological parameters. This paper comprehensively reviews the progress in 3D-printed wearable biosensors. The review also explores the incorporation of nanocomposites in 3D printing for biosensors. A detailed analysis of various 3D printing processes for fabricating wearable biosensors is reported. Besides this, recent advances in various 3D-printed wearable biosensors platforms such as sweat sensors, glucose sensors, electrocardiography sensors, electroencephalography sensors, tactile sensors, wearable oximeters, tattoo sensors, and respiratory sensors are discussed. Furthermore, the challenges and prospects associated with 3D-printed wearable biosensors are presented. This review is an invaluable resource for engineers, researchers, and healthcare clinicians, providing insights into the advancements and capabilities of 3D printing in the wearable biosensor domain. [ABSTRACT FROM AUTHOR]

Wearable biosensors are attracting great interest thanks to their high potential for providing clinical-diagnostic information in real time, exploiting non-invasive sampling of biofluids. In this context, sweat has been demonstrated to contain physiologically relevant biomarkers, even if it has not been exhaustively exploited till now. This biofluid has started to gain attention thanks to the applications offered by wearable biosensors, as it is easily collectable and can be used for continuous monitoring of some parameters. Several studies have reported electrochemical and optical biosensing strategies integrated with flexible, biocompatible, and innovative materials as platforms for biospecific recognition reactions. Furthermore, sampling systems as well as the transport of fluids by microfluidics have been implemented into portable and compact biosensors to improve the wearability of the overall analytical device. In this review, we report and discuss recent pioneering works about the development of sweat sensing technologies, focusing on opportunities and open issues that can be decisive for their applications in routine-personalized healthcare practices. [ABSTRACT FROM AUTHOR]

The human body is a source of multiple types of energy, such as mechanical, thermal and biochemical, which can be scavenged through appropriate technological means. Mechanical vibrations originating from contraction and expansion of the radial artery represent a reliable source of displacement to be picked up and exploited by a harvester. The continuous monitoring of physiological biomarkers is an essential part of the timely and accurate diagnosis of a disease with subsequent medical treatment, and wearable biosensors are increasingly utilized for biomedical data acquisition of important biomarkers. However, they rely on batteries and their replacement introduces a discontinuity in measured signals, which could be critical for the patients and also causes discomfort. In the present work, the research into a novel 3D-printed wearable energy harvesting platform for scavenging energy from arterial pulsations via a piezoelectric material is described. An elastic thermoplastic polyurethane (TPU) film, which forms an air chamber between the skin and the piezoelectric disc electrode, was introduced to provide better adsorption to the skin, prevent damage to the piezoelectric disc and electrically isolate components in the platform from the human body. Computational fluid dynamics in the framework of COMSOL Multiphysics 6.1 software was employed to perform a series of coupled time-varying simulations of the interaction among a number of associated physical phenomena. The mathematical model of the harvester was investigated computationally, and quantification of the output energy and power parameters was used for comparisons. A prototype wearable platform enclosure was designed and manufactured using fused filament fabrication (FFF). The influence of the piezoelectric disc material and its diameter on the electrical output were studied and various geometrical parameters of the enclosure and the TPU film were optimized based on theoretical and empirical data. Physiological data, such as interdependency between the harvester skin fit and voltage output, were obtained. [ABSTRACT FROM AUTHOR]

Over the last decade, both scientific and commercial communities have focused on developing wearable sensors for biomedical use. These sensors monitor vital signs in various individuals, including patients, athletes, infants, and the elderly. They contribute to mobile health technologies, offering real-time health recommendations and management. Wearable and implantable devices are reshaping healthcare, driven by sensor advancements. Biosensors, known for their simplicity and adaptability, hold significant potential. This review focuses on categorizing wearable biosensors, including classifying biological elements, nanomaterials, and transducers. It also examines the various types of wearable sensors, specialized sensor designs, applications in textile materials, wearable medical devices, and the advantages of biosensors in medicine. Comprehensive analysis of the various applications of wearable biotechnology while addressing the challenges and possible remedies associated with wearable technology were reviewed.

Abstract Herein, this work reports the first example of second‐generation wearable biosensor arrays based on a printed electrode technology involving a water‐based graphite ink, for the simultaneous detection of l‐lactate and d‐glucose. The water‐based graphite ink is deposited onto a flexible polyethylene terephthalate sheet, namely stencil‐printed graphite (SPG) electrodes, and further modified with [Os(bpy)2(Cl)(PVI)10] as an osmium redox polymer to shuttle the electrons from the redox center of lactate oxidase from Aerococcus viridans (LOx) and gluocose oxidase from Aspergillus niger (GOx). The proposed biosensor array exhibits a limit of detection as low as (9.0 ± 1.0) × 10−6 m for LOx/SPG‐[Os(bpy)2(Cl)(PVI)10] and (3.0 ± 0.5) × 10−6 m for GOx/SPG‐[Os(bpy)2(Cl)(PVI)10], a sensitivity as high as 1.32 μA mm−1 for LOx/SPG‐[Os(bpy)2(Cl)(PVI)10] and 28.4 μA mm−1 for GOx/SPG‐[Os(bpy)2(Cl)(PVI)10]. The technology is also selective when tested in buffer and artificial sweat and is endowed with an operational/storage stability of ≈80% of the initial signal retained after 20 days. Finally, the proposed array is integrated in a wristband and successfully tested for the continuous monitoring of l‐lactate and d‐glucose in a healthy volunteer during daily activity. This is foreseen as a real‐time wearable device for sport‐medicine and healthcare applications.

Migraine is one of the most common neurological disorders, characterized by moderate-to-severe headache episodes. Autonomic nervous system (ANS) alterations can occur at phases of migraine attack. This study investigates patterns of ANS changes during the pre-ictal night of migraine, utilizing wearable biosensor technology in ten individuals. Various physiological, activity-based, and signal processing metrics were examined to train predictive models and understand the relationship between specific features and migraine occurrences. Data were filtered based on specified criteria for nocturnal sleep, and analysis frames ranging from 5 to 120 min were used to improve the diversity of the training sample and investigate the impact of analysis frame duration on feature significance and migraine prediction. Several models, including XGBoost (Extreme Gradient Boosting), HistGradientBoosting (Histogram-Based Gradient Boosting), Random Forest, SVM, and KNN, were trained on unbalanced data and using cost-sensitive learning with a 5:1 ratio. To evaluate the changes in features during pre-migraine nights and nights before migraine-free days, an analysis of variance (ANOVA) was performed. The results showed that the features of electrodermal activity, skin temperature, and accelerometer exhibited the highest F-statistic values and the most significant p-values in the 5 and 10 min frames, which makes them particularly useful for the early detection of migraines. The generalized prediction model using XGBoost and a 5 min analysis frame achieved 0.806 for accuracy, 0.638 for precision, 0.595 for recall, and 0.607 for F1-score. Despite identifying distinguishing features between pre-migraine and migraine-free nights, the performance of the current model suggests the need for further improvements for clinical application.

In recent years, flexible and wearable biosensor technologies have gained significant attention due to their potential to revolutionize healthcare monitoring. Among the various components involved in these biosensors, the electrode material plays a crucial role in ensuring accurate and reliable detection. In this regard, polymer electrodes, such as Poly(3,4 ethylenedioxythiophene): poly(styrenesulfonate), combined with graphene (PEDOT:PSS/graphene), have emerged as promising candidates due to their unique mechanical properties and excellent electrical conductivity. Understanding the mechanical behavior of these polymer electrodes on flexible substrates is essential to ensure the stability and durability of wearable biosensors. In this paper, PEDOT:PSS/graphene composite was spray-coated on flexible substrates at different growth conditions to explore the effect of the deposition parameters and mode of mechanical loading (longitudinal or transversal) on the electrical and mechanical behavior of the fabricated samples. It was found that the coating grown at lower temperatures and higher spraying pressure exhibited stable behavior no matter the applied stress type.

The emergence of pandemics poses a persistent threat to both global health and economic stability. While zoonotic spillovers and local outbreaks may not be fully preventable, early detection of infections in individuals before they spread to communities can make a major difference in containing an infectious disease and stopping it from becoming an epidemic and then a pandemic.In this paper, we propose a novel Artificial Intelligence (AI)-based pandemic prediction framework called iPREDICT—a concept framework designed to leverage the power of AI and crowd-sensed data for accurate and timely pandemic prediction. The core idea of iPREDICT is to leverage the deluge of data that can be harnessed from connected and wearable biosensing devices. iPREDICT system then works by monitoring anomalies in the biomarkers at the individual level and correlating them with similar anomalies observed in other members of the community. Using AI-based anomaly detection in conjunction with analysis of the spatiotemporal growth of the correlated anomalies, iPREDICT thus can potentially detect and monitor the emergence of a local outbreak in near real-time to predict a potential pandemic.However, not every outbreak has the potential to become a pandemic. We illustrate how tools like graph neural networks can be leveraged to determine optimal thresholds as a function of a large number of demographical, social, and geographical factors that determine the spatiotemporal spread of an outbreak, thus quantifying the risk of it becoming an epidemic or pandemic.We also identify essential technological and social challenges that require attention to transform iPREDICT from an idea into a globally deployable solution for future pandemic prediction and management. To provide deeper insights into iPREDICT design challenges and trigger research towards possible solutions we present a COVID-19 based case study. The results signify the impact of variation in biosensing hardware, data sampling rate, and compression rate on the performance of AI models that underpin various components of the iPREDICT system.

Biosensors have the potential to revolutionize healthcare by providing rapid and accurate diagnosis of diseases. Biosensors are analytical devices that convert molecular recognition of a target analyte into a measurable signal. Older diagnostic techniques, such as immunoaffinity column assays, fluorometric, and enzyme-linked immunosorbent assays, are laborious, require qualified personnel, and can be time consuming. In contrast, biosensors offer improved accuracy, sustainability, and rapidness due to their ability to detect specific biomarkers with high sensitivity and specificity. The review covers various bacterial cellulose (BC)-based biosensors, from SARS-CoV-2 detection to wearable health monitoring and interaction with human-computer interfaces. BC's integration into ionic thermoelectric hydrogels for wearable health monitoring shows its potential for real-time health tracking. Incorporating BC in biosensors for low-noise electrodes, and wearable sensors has been elaborated. The invention of a phage-immobilized BC biosensor for S. aureus detection is a significant contribution to the field, highlighting the biosafety and efficiency of BC in pathogen identification and demonstrating BC's versatility across multiple sensing platforms. Palladium nanoparticle-bacterial cellulose hybrid nanofibers show excellent electrocatalytic activity for dopamine detection, whereas Au-BC nanocomposite biosensors show efficacy in glucose detection, with potential therapeutic applications. The “lab-on-nanopaper” device, utilizing BC nanopaper, not only visually detects human serum albumin but also establishes itself as a new-generation optical biosensing platform with superiority over conventional substrates. This review contributes to the ongoing advancements in biosensor technology, highlighting the potential of BC as a versatile material for developing innovative biosensors. This is crucial for improving the accuracy, sensitivity, and efficiency of diagnostic tools in healthcare.

Human activity recognition (HAR) has recently become a popular research field in the wearable sensor technology scene. By analyzing the human behavior data, some disease risks or potential health issues can be detected, and patients' rehabilitation progress can be evaluated. With the excellent performance of Transformer in natural language processing and visual tasks, researchers have begun to focus on its application in time series. The Transformer model models long-term dependencies between sequences through self-attention mechanisms, capturing contextual information over extended periods. In this paper, we propose a hybrid model based on the channel attention mechanism and Transformer model to improve the feature representation ability of sensor-based HAR tasks. Extensive experiments were conducted on three public HAR datasets, and the results show that our network achieved accuracies of 98.10%, 97.21%, and 98.82% on the HARTH, PAMAP2, and UCI-HAR datasets, respectively, The overall performance is at the level of the most advanced methods. [ABSTRACT FROM AUTHOR]

Terahertz metamaterials have shown promises in biomolecules detection, however, aqueous solvents such as blood serum strongly absorb the terahertz waves, interfering with the detection. To avoid such interference, an ultrathin, flexible, biogel‐based metamaterial that can specifically detect the transmission of terahertz waves through biomolecules in aqueous solution is proposed and demonstrated. The glucose in water can be detected with a sensitivity of 0.0446 dL mg−1 and a detection limit of 1.64 mg dL−1, as well as glucose in human serum. The metamaterial that detects glucose in human sweat is part of a wearable device. The biosensor functions well for more than 100 detection cycles. The results suggest a strategy for real‐time detection of glucose and potentially other biomolecules. [ABSTRACT FROM AUTHOR]

Recent advancements in electrochemical sensors for the detection of vitamins, particularly vitamin D, have drawn a lot of attention due to their outstanding advantages of simplicity and high sensitivity. For the purpose of detecting vitamin D in this circumstance, recent research has focused on developing electrochemical sensors. Although there is always space for improvement, electrochemical sensors for vitamin D detection and its transformation into point‐of‐care devices have made great strides lately. For example, the development of innovative electrode materials that can increase sensitivity and selectivity continues to garner a lot of interest. New suggestions on adsorptive detections using vitamin D carriers like nanoclays or hydroxyapatite‐clay composites are being developed. These biosensors hold huge potential for the detection of cheap, disposable, and biodegradable solutions. Also, the biosensor could monitor the depletion of vitamin levels, providing a real‐time platform for the Internet of Medical Things, fifth‐generation wireless communications, and smartphone‐based electrochemical sensors. Currently, electrochemical sensors based on smartphones have been proposed to detect using various biomarkers for monitoring several changes in glucose, etc. We believe smartphone‐based electrochemical sensors and adsorptive sensing platforms provides a novel way toward point‐of‐care tests for identifying especially vitamin D deficiency and real‐time monitoring [ABSTRACT FROM AUTHOR]

With the innovation of wearable technology and the rapid development of biosensors, wearable biosensors based on flexible textile materials have become a hot topic. Such textile-based wearable biosensors promote the development of health monitoring, motion detection and medical management, and they have become an important support tool for human healthcare monitoring. Textile-based wearable biosensors not only non-invasively monitor various physiological indicators of the human body in real time, but they also provide accurate feedback of individual health information. This review examines the recent research progress of fabric-based wearable biosensors. Moreover, materials, detection principles and fabrication methods for textile-based wearable biosensors are introduced. In addition, the applications of biosensors in monitoring vital signs and detecting body fluids are also presented. Finally, we also discuss several challenges faced by textile-based wearable biosensors and the direction of future development. [ABSTRACT FROM AUTHOR]

Binary operating (YES/NO) biosensors based on unconventional biocomputing concept represent a novel approach to the analysis of various biomarkers and environmental signals. The binary biosensors with implemented Boolean logic process analyte input signals using chemical means without electronic computers. They can be used for extension of the analytical output signals to chemical actuation and can be integrated with wearable analytical devices. The progress in this novel biosensing approach is strongly dependent on achievements in biomolecular computing, particularly based on enzyme-catalyzed reactions. The easy interpretation of the analytical results represented in the binary YES/NO form allows convenient adaptation of the biosensors for end-user and point-ofcare within various biomedical and bioelectronic applications. [ABSTRACT FROM AUTHOR]

Individual grip strength provides a functional window into somatosensory processes and their effects on motor behaviour in healthy, impaired, and ageing individuals. Variations in grip strength during hand–tool interaction are therefore exploited in a variety of experimental tasks to study the effects of pathology or ageing-related changes on sensory, motor, and cognitive ability. However, many different factors may influence individual grip strength systematically in a given task context without being explicitly identified and controlled for. Grip strength may vary as a function of the location of the measurement device (sensor) on the fingers/hand, the shape, weight and size of object(s) being gripped, the type of grip investigated (static versus dynamic grip), and the hand (dominant versus non-dominant) used for gripping. This study tests for additional factors such as sight, sound, and interactions with/between any of the other factors in a complex task context. A wearable biosensor system, designed for measuring grip strength variations in operators gripping cylindrical objects bi-manually, was used. Grip force signals were recorded from all sensors of the wearable (glove) system, including three directly task-relevant sensors for bi-manually gripping cylindrical objects with the dominant and non-dominant hands. Five young male participants were tested for the effects of sound, movement, and sight on grip strength. The participants had to pick up two cylindrical objects of identical size and weight, then hold them still (static grip) or move them upwards and downwards (dynamic grip) for ten seconds while listening to soft or hard music, with their eyes open or blindfolded. Significant effects of sensor location, hand, movement, sight, and sound on bi-manual grip strength were found. Stronger grip force signals were produced by task-relevant sensors in the dominant hand when moving the cylindrical handles (dynamic grip) in comparison with the static grip condition, depending, as expected, on whether grip signals were measured from the dominant or the non-dominant hand. Significantly weaker grip strength was produced blindfolded (sight condition), and grips were significantly stronger with exposure to harder music (sound factor). It is concluded that grip strength is significantly influenced by sensory factors and interactions between the other factors tested for, pointing towards the need for identifying and systematically controlling such potential sources of variation in complex study task contexts.

Introduction: Recent changes in diagnostics criteria have contributed to the broadening of the autism spectrum disorders and left clinicians ill-equipped to treat the highly heterogeneous spectrum that now includes toddlers and children with sensory and motor issues. Methods: To uncover the clinicians’ critical needs in the autism space, we conducted surveys designed collaboratively with the clinicians themselves. Board Certified Behavioral Analysts (BCBAs) and developmental model (DM) clinicians obtained permission from their accrediting boards and designed surveys to assess needs and preferences in their corresponding fields. Results: 92.6% of BCBAs are open to diversified treatment combining aspects of multiple disciplines; 82.7% of DMs also favor this diversification with 21.8% valuing BCBA-input and 40.6% neurologists-input; 85.9% of BCBAs and 85.3% of DMs advocate the use of wearables to objectively track nuanced behaviors in social exchange; 76.9% of BCBAs and 57.0% DMs feel they would benefit from augmenting their knowledge about the nervous systems of Autism (neuroscience research) to enhance treatment and planning programs; 50.0% of BCBAs feel they can benefit for more training to teach parents. Discussion: Two complementary philosophies are converging to a more collaborative, integrative approach favoring scalable digital technologies and neuroscience. Autism practitioners seem ready to embrace the Digital-Neuroscience Revolutions under a new cooperative model. [ABSTRACT FROM AUTHOR]

A phenylboronic acid-based, hydrogel-interlayer Radio-Frequency (RF) resonator is demonstrated as a highly-responsive, passive and wireless sensor for glucose monitoring. Constructs are composed of unanchored, capacitively-coupled split rings interceded by glucose-responsive hydrogels. Phenylboronic acid-hydrogels exhibit volumetric and dielectric variations in response to environmental glucose concentrations-these are efficiently converted to large shifts in the resonant response of interlayer-RF sensors. These tiny, stretchable and scalable sensors (5 mm × 5 mm x 250 μm) require no microelectronics or power at the sensing node and can be read-out remotely via near-field coupling. Sensors exhibit high sensitivities (~10% shift in resonant frequency-corresponding to 50 MHz-per 150 mg/dL of glucose), possess a limit of detection of 10 mg/dL, and a step response time of approximately 1 h to abrupt shifts in carbohydrate concentration. Notably, these sensors exhibited no signal drift or hysteresis over the time periods characterized herein (45 days at room temperature). We transform sensors into bioelectronic RF reporter-tags via the attachment of a single LED-these remotely report on glucose concentration via emitted light. We anticipate the non-degradative, long-term nature of both RF read-out and phenylboronic acid-based hydrogels will enable biosensors capable of long-term, remote read-out of glucose.

Wearable biosensors, which aim at providing continuous, real-time physiological information via monitoring and screening biomarkers in human body, are receiving increasing attention among various fields including disease treatment, diagnosis and self-health management. The ongoing development in this realm starves for the exploration of fully-integrated, non-invasive devices. In this paper, we review the latest achievements with breakthrough significance on the wearable biosensors. We start with the classification of different types of wearable electronic devices and analyze their characteristics and application values. Subsequently, we introduce a fully-integrated microneedle-based sensor and provide an in-depth look at its structure, subcomponents and in vivo performances. Finally, we put forward critical commentaries and clarify the direction of future researches.

With the increasing demand in recent years, the interest in vitamins has been growing rapidly. The vitamin group that attracts attention with the pandemic period is vitamin D. Vitamin D deficiency and insufficiency; It is associated with many chronic diseases including cancers, cardiovascular diseases, metabolic syndrome, infectious and autoimmune diseases. Therefore, the need to monitor exposure and the amount and amount of vitamins in foods, drugs, and biological fluids makes regular measurement critical. The importance of organic and inorganic (such as nanoclays, ceramics, bioactive glasses and metal oxide nanoparticles) carriers used in ensuring, increasing and protecting the stability of vitamins is of great importance. In this study, starting from vitamin D and its importance, by referring to the methods used in the determination of vitamin D, electrochemical methods will be examined in particular, by referring to the stabilizing inorganic nanocarriers in particular nanoclays, comprehensive reviews will be made about promising possibilities as (bio)detection platforms in the near future. [ABSTRACT FROM AUTHOR]

Sensors enable the detection of physiological indicators and pathological markers to assist in the diagnosis, treatment, and long-term monitoring of diseases, in addition to playing an essential role in the observation and evaluation of physiological activities. The development of modern medical activities cannot be separated from the precise detection, reliable acquisition, and intelligent analysis of human body information. Therefore, sensors have become the core of new-generation health technologies along with the Internet of Things (IoTs) and artificial intelligence (AI). Previous research on the sensing of human information has conferred many superior properties on sensors, of which biocompatibility is one of the most important. Recently, biocompatible biosensors have developed rapidly to provide the possibility for the long-term and in-situ monitoring of physiological information. In this review, we summarize the ideal features and engineering realization strategies of three different types of biocompatible biosensors, including wearable, ingestible, and implantable sensors from the level of sensor designing and application. Additionally, the detection targets of the biosensors are further divided into vital life parameters (e.g., body temperature, heart rate, blood pressure, and respiratory rate), biochemical indicators, as well as physical and physiological parameters based on the clinical needs. In this review, starting from the emerging concept of next-generation diagnostics and healthcare technologies, we discuss how biocompatible sensors revolutionize the state-of-art healthcare system unprecedentedly, as well as the challenges and opportunities faced in the future development of biocompatible health sensors. [ABSTRACT FROM AUTHOR]

IntroductionRecent changes in diagnostics criteria have contributed to the broadening of the autism spectrum disorders and left clinicians ill-equipped to treat the highly heterogeneous spectrum that now includes toddlers and children with sensory and motor issues.MethodsTo uncover the clinicians’ critical needs in the autism space, we conducted surveys designed collaboratively with the clinicians themselves. Board Certified Behavioral Analysts (BCBAs) and developmental model (DM) clinicians obtained permission from their accrediting boards and designed surveys to assess needs and preferences in their corresponding fields.Results92.6% of BCBAs are open to diversified treatment combining aspects of multiple disciplines; 82.7% of DMs also favor this diversification with 21.8% valuing BCBA-input and 40.6% neurologists-input; 85.9% of BCBAs and 85.3% of DMs advocate the use of wearables to objectively track nuanced behaviors in social exchange; 76.9% of BCBAs and 57.0% DMs feel they would benefit from augmenting their knowledge about the nervous systems of Autism (neuroscience research) to enhance treatment and planning programs; 50.0% of BCBAs feel they can benefit for more training to teach parents.DiscussionTwo complementary philosophies are converging to a more collaborative, integrative approach favoring scalable digital technologies and neuroscience. Autism practitioners seem ready to embrace the Digital-Neuroscience Revolutions under a new cooperative model.

Cortisol is a key biomarker, and its measurement has historically relied on intrusive and sporadic techniques like blood or saliva samples. The relatively recent innovation of electrochemical cortisol bio-wearables provides a revolutionary strategy by offering continuous, non-invasive monitoring. This Perspective examines the development, underlying ideas, scientific developments, and possible uses of electrochemical cortisol bio-wearables. The significance of these tools for stress research, clinical application, and individualized healthcare is also highlighted.

Hypovolemic shock is one of the leading causes of death in the military. The current methods of assessing hypovolemia in field settings rely on a clinician assessment of vital signs, which is an unreliable assessment of hypovolemia severity. These methods often detect hypovolemia when interventional methods are ineffective. Therefore, there is a need to develop real-time sensing methods for the early detection of hypovolemia. Previously, our group developed a random-forest model that successfully estimated absolute blood-volume status (ABVS) from noninvasive wearable sensor data for a porcine model (n = 6). However, this model required normalizing ABVS data using individual baseline data, which may not be present in crisis situations where a wearable sensor might be placed on a patient by the attending clinician. We address this barrier by examining seven individual baseline-free normalization techniques. Using a feature-specific global mean from the ABVS and an external dataset for normalization demonstrated similar performance metrics compared to no normalization (normalization: R2 = 0.82 ± 0.025|0.80 ± 0.032, AUC = 0.86 ± 5.5 × 10−3|0.86 ± 0.013, RMSE = 28.30 ± 0.63%|27.68 ± 0.80%; no normalization: R2 = 0.81 ± 0.045, AUC = 0.86 ± 8.9 × 10−3, RMSE = 28.89 ± 0.84%). This demonstrates that normalization may not be required and develops a foundation for individual baseline-free ABVS prediction.

The human body is a source of multiple types of energy, such as mechanical, thermal and biochemical, which can be scavenged through appropriate technological means. Mechanical vibrations originating from contraction and expansion of the radial artery represent a reliable source of displacement to be picked up and exploited by a harvester. The continuous monitoring of physiological biomarkers is an essential part of the timely and accurate diagnosis of a disease with subsequent medical treatment, and wearable biosensors are increasingly utilized for biomedical data acquisition of important biomarkers. However, they rely on batteries and their replacement introduces a discontinuity in measured signals, which could be critical for the patients and also causes discomfort. In the present work, the research into a novel 3D-printed wearable energy harvesting platform for scavenging energy from arterial pulsations via a piezoelectric material is described. An elastic thermoplastic polyurethane (TPU) film, which forms an air chamber between the skin and the piezoelectric disc electrode, was introduced to provide better adsorption to the skin, prevent damage to the piezoelectric disc and electrically isolate components in the platform from the human body. Computational fluid dynamics in the framework of COMSOL Multiphysics 6.1 software was employed to perform a series of coupled time-varying simulations of the interaction among a number of associated physical phenomena. The mathematical model of the harvester was investigated computationally, and quantification of the output energy and power parameters was used for comparisons. A prototype wearable platform enclosure was designed and manufactured using fused filament fabrication (FFF). The influence of the piezoelectric disc material and its diameter on the electrical output were studied and various geometrical parameters of the enclosure and the TPU film were optimized based on theoretical and empirical data. Physiological data, such as interdependency between the harvester skin fit and voltage output, were obtained.

Wearable biosensors are attracting great interest thanks to their high potential for providing clinical-diagnostic information in real time, exploiting non-invasive sampling of biofluids. In this context, sweat has been demonstrated to contain physiologically relevant biomarkers, even if it has not been exhaustively exploited till now. This biofluid has started to gain attention thanks to the applications offered by wearable biosensors, as it is easily collectable and can be used for continuous monitoring of some parameters. Several studies have reported electrochemical and optical biosensing strategies integrated with flexible, biocompatible, and innovative materials as platforms for biospecific recognition reactions. Furthermore, sampling systems as well as the transport of fluids by microfluidics have been implemented into portable and compact biosensors to improve the wearability of the overall analytical device. In this review, we report and discuss recent pioneering works about the development of sweat sensing technologies, focusing on opportunities and open issues that can be decisive for their applications in routine-personalized healthcare practices.

Notably, 3D-printed flexible and wearable biosensors have immense potential to interact with the human body noninvasively for the real-time and continuous health monitoring of physiological parameters. This paper comprehensively reviews the progress in 3D-printed wearable biosensors. The review also explores the incorporation of nanocomposites in 3D printing for biosensors. A detailed analysis of various 3D printing processes for fabricating wearable biosensors is reported. Besides this, recent advances in various 3D-printed wearable biosensors platforms such as sweat sensors, glucose sensors, electrocardiography sensors, electroencephalography sensors, tactile sensors, wearable oximeters, tattoo sensors, and respiratory sensors are discussed. Furthermore, the challenges and prospects associated with 3D-printed wearable biosensors are presented. This review is an invaluable resource for engineers, researchers, and healthcare clinicians, providing insights into the advancements and capabilities of 3D printing in the wearable biosensor domain.

Abstract Metabolic biomarkers facilitate pathological/physiological investigations and clinic decisions. Noninvasive quantification of metabolic biomarkers allows patients to monitor their health without time, location, and environmental constraints. Recently, the synergistic integration between high‐throughput screening strategy via mass spectrometry and noninvasive quantification techniques via wearable biosensors may provide fundamental insights into the application of metabolic biomarkers in clinical diagnostics. Here, mass spectrometry for the screening of metabolic biomarkers in several major clinical diseases (retinoblastoma, coronary heart disease, lung adenocarcinoma) is introduced. Then, three types of wearable biosensors based on electrochemistry, organic electrochemical transistors, and field effect transistors for the noninvasive quantification of metabolic biomarkers are summarized. The review may serve as a handbook for high‐throughput screening of metabolic biomarkers and wearable biosensors for the quantification of metabolites. It is anticipated that biomarker discovery and quantification will facilitate the next generation of wearable biosensors toward personalized, intelligent, and precise home–healthcare on a large‐scale.

New technologies for monitoring grip forces during hand and finger movements in non-standard task contexts have provided unprecedented functional insights into somatosensory cognition. Somatosensory cognition is the basis of our ability to manipulate and transform objects of the physical world and to grasp them with the right amount of force. In previous work, the wireless tracking of grip-force signals recorded from biosensors in the palm of the human hand has permitted us to unravel some of the functional synergies that underlie perceptual and motor learning under conditions of non-standard and essentially unreliable sensory input. This paper builds on this previous work and discusses further, functionally motivated, analyses of individual grip-force data in manual robot control. Grip forces were recorded from various loci in the dominant and non-dominant hands of individuals with wearable wireless sensor technology. Statistical analyses bring to the fore skill-specific temporal variations in thousands of grip forces of a complete novice and a highly proficient expert in manual robot control. A brain-inspired neural network model that uses the output metric of a self-organizing pap with unsupervised winner-take-all learning was run on the sensor output from both hands of each user. The neural network metric expresses the difference between an input representation and its model representation at any given moment in time and reliably captures the differences between novice and expert performance in terms of grip-force variability.Functionally motivated spatiotemporal analysis of individual average grip forces, computed for time windows of constant size in the output of a restricted amount of task-relevant sensors in the dominant (preferred) hand, reveal finger-specific synergies reflecting robotic task skill. The analyses lead the way towards grip-force monitoring in real time. This will permit tracking task skill evolution in trainees, or identify individual proficiency levels in human robot-interaction, which represents unprecedented challenges for perceptual and motor adaptation in environmental contexts of high sensory uncertainty. Cross-disciplinary insights from systems neuroscience and cognitive behavioral science, and the predictive modeling of operator skills using parsimonious Artificial Intelligence (AI), will contribute towards improving the outcome of new types of surgery, in particular the single-port approaches such as NOTES (Natural Orifice Transluminal Endoscopic Surgery) and SILS (Single-Incision Laparoscopic Surgery). [ABSTRACT FROM AUTHOR]

Fatigue causes deleterious effects to physical and mental health of human being and may cause loss of lives. Therefore, the adverse effects of fatigue on individuals and the society are massive. With the ever-increasing frequency of overtraining among modern military and sports personnel, timely, portable and accurate fatigue diagnosis is essential to avoid fatigue-induced accidents. However, traditional detection methods require complex sample preparation and blood sampling processes, which cannot meet the timeliness and portability of fatigue diagnosis. With the development of flexible materials and biosensing technology, wearable biosensors have attracted increased attention to the researchers. Wearable biosensors collect biomarkers from noninvasive biofluids, such as sweat, saliva, and tears, followed by biosensing with the help of biosensing modules continuously and quantitatively. The detection signal can then be transmitted through wireless communication modules that constitute a method for real-time understanding of abnormality. Recent developments of wearable biosensors are focused on miniaturized wearable electrochemistry and optical biosensors for metabolites detection, of which, few have exhibited satisfactory results in medical diagnosis. However, detection performance limits the wide-range applicability of wearable fatigue diagnosis. In this article, the application of wearable biosensors in fatigue diagnosis has been discussed. In fact, exploration of the composition of different biofluids and their potential toward fatigue diagnosis have been discussed here for the very first time. Moreover, discussions regarding the current bottlenecks in wearable fatigue biosensors and the latest advancements in biochemical reaction and data communication modules have been incorporated herein. Finally, the main challenges and opportunities were discussed for wearable fatigue diagnosis in the future. [ABSTRACT FROM AUTHOR]