Search Results (34)

The demand for non-invasive, real-time health monitoring has driven advancements in wearable sensors for tracking biomarkers in sweat. Ammonium ions (NH₄⁺) in sweat serve as indicators of metabolic function, muscle fatigue, and kidney health. Although current ion-selective all-solid-state printed sensors based on nanocomposites typically exhibit good sensitivity (~50 mV/log [NH₄⁺]), low detection limits (LOD ranging from 10⁻⁶ to 10⁻⁷ M), and wide linearity ranges (from 10⁻⁵ to 10⁻¹ M), few have reported the stability test results necessary for their integration into commercial products for future practical applications. This study presents a highly stable, wearable electrochemical sensor based on a composite of metal–organic frameworks (MOFs) and reduced graphene oxide (rGO) for monitoring NH₄⁺ in sweat. The synergistic properties of Ni-based MOFs and rGO enhance the sensor’s electrochemical performance by improving charge transfer rates and expanding the electroactive surface area. The MOF/rGO sensor demonstrates high sensitivity, with a Nernstian response of 59.2 ± 1.5 mV/log [NH₄⁺], an LOD of 10^−6.37 M, and a linearity range of 10⁻⁶ to 10⁻¹ M. Additionally, the hydrophobic nature of the MOF/rGO composite prevents water layer formation at the sensing interface, thereby enhancing long-term stability, while its high double-layer capacitance minimizes potential drift (7.2 µV/s (i = ±1 nA)) in short-term measurements. Extensive testing verified the sensor’s exceptional stability, maintaining consistent performance and stable responses across varying NH₄⁺ concentrations over 7 days under ambient conditions. On-body tests further confirmed the sensor’s suitability for the continuous monitoring of NH₄⁺ levels during physical activities. Further investigations are required to fully elucidate the impact of interference from other sweat components (such as K⁺, Na⁺, Ca²⁺, etc.) and the influence of environmental factors (including the subject’s physical activity, posture, etc.). With a clearer understanding of these factors, the sensor has the potential to emerge as a promising tool for wearable health monitoring applications. Full article

Motion is vital for life. Currently, the clinical assessment of motion abnormalities is largely qualitative. We previously developed methods to quantitatively assess motion using visual detection systems (around-body) and stretchable electronic sensors (on-body). Here we compare the efficacy of these methods across predefined motions, hypothesizing that the around-body system detects motion with similar accuracy as on-body sensors. Six human volunteers performed six defined motions covering three excursion lengths, small, medium, and large, which were analyzed via both around-body visual marker detection (MoCa version 1.0) and on-body stretchable electronic sensors (BioStamp version 1.0). Data from each system was compared as to the extent of trackability and comparative efficacy between systems. Both systems successfully detected motions, allowing quantitative analysis. Angular displacement between systems had the highest agreement efficiency for the bicep curl and body lean motion, with 73.24% and 65.35%, respectively. The finger pinch motion had an agreement efficiency of 36.71% and chest abduction/adduction had 45.55%. Shoulder abduction/adduction and shoulder flexion/extension motions had the lowest agreement efficiencies with 24.49% and 26.28%, respectively. MoCa was comparable to BioStamp in terms of angular displacement, though velocity and linear speed output could benefit from additional processing. Our findings demonstrate comparable efficacy for non-contact motion detection to that of on-body sensor detection, and offers insight as to the best system selection for specific clinical uses based on the use-case of the desired motion being analyzed. Full article

Sweat rate magnitude is a desired outcome for any wearable sensing patch dedicated to sweat analysis. Indeed, sweat rate values can be used two-fold: self-diagnosis of dehydration and correction/normalization of other physiological metrics, such as Borg scale, VO2, and different chemical species concentrations. Herein, a reliable sweat rate belt device for sweat rate monitoring was developed. The device measures sweat rates in the range from 1.0 to 5.0 µL min⁻¹ (2 to 10 µL min⁻¹ cm⁻²), which covers typical values for humans. The working mechanism is based on a new direct current (DC) step protocol activating a series of differential resistance measurements (spatially separated by 800 µm) that is gradually initiated by the action of sweat, which flows along a customized microfluidic track (~600 µm in width, 10 mm in length, and 235 µm in thickness). The device has a volumetric capacity of ~16 µL and an acquisition frequency between 0.010 and 0.043 Hz within the measured sweat rate range. Importantly, instead of using a typical and rather complex AC signal interrogation and acquisition, we put forward the DC approach, offering several benefits, such as simplified circuit design for easier fabrication and lower costs, as well as reduced power consumption and suitability for wearable applications. For the validation, either the commercial sweat collector (colorimetric) or the developed device was performed. In five on-body tests, an acceptable variation of ca. 10% was obtained. Overall, this study demonstrates the potential of the DC-based device for the monitoring of sweat rate and also its potential for implementation in any wearable sweat platform. Full article

In recent years, skin-mounted devices have gained prominence in personal wellness and remote patient care. However, the rigid components of many wearables often cause discomfort due to their mechanical mismatch with the skin. To address this, we extend the use of the solderable stretchable sensing system (S4) to develop a wireless skin temperature-sensing smart adhesive. This work introduces two novel types of progress in wearables: the first demonstration of Bluetooth-integration and development of a thin-film-based stretchable inverted-F antenna (SIFA). Characterized through RF simulations, vector network analysis under deformation, and anechoic chamber tests, SIFA demonstrated potential as a low-profile, on-body Bluetooth antenna with a resonant frequency of 2.45 GHz that helps S4 retain its thin overall profile. The final S4 system achieved high correlation (R = 0.95, p < 0.001, mean standard error = 0.04 °C) with commercial sensors during daily activities. These findings suggest that S4-based smart adhesives integrated with SIFAs could offer a promising platform for comfortable, efficient, and functional skin-integrated wearables, supporting a range of health monitoring applications. Full article

Owing to their potential to transform traditional medical diagnostics and health monitoring, wearable biosensors have become an alternative evolutionary technology in the field of medical care. However, it is still necessary to overcome some key technique challenges, such as the selectivity, sensitivity, and stability of biometric identification. Herein, a novel, wearable electrochemical sensor based on a molecularly imprinted polymer (MIP) integrated with a copper benzene-1,3,5-tricarboxylate metal–organic framework (MOF) was designed for the detection of stress through the on-body monitoring of cortisol in sweat. The MOF was used as the substrate for MIP deposition to enhance the stability and sensitivity of the sensor. The sensor consisted of two layers, with a microfluidic layer as the top layer for spontaneous sweating and a modified electrode as the bottom layer for sensing. The sensor measured cortisol levels by detecting the current change that occurred when the target molecules bound to the imprinted cavities, using Prussian blue nanoparticles embedded in the MIP framework as the REDOX probe. The proposed sensor exhibited a linear detection range of 0.01–1000 nM with a detection limit of 0.0027 nM, and favorable specificity over other analogies. This facile anti-body free sensor showed excellent stability, and can be successfully applied for in situ cortisol monitoring. Full article

Acquiring phenotypic data from livestock constitutes a crucial yet cumbersome phase in the breeding process. Traditionally, obtaining livestock phenotypic data primarily involves manual, on-body measurement methods. This approach not only requires extensive labor but also induces stress on animals, which leads to potential economic losses. Presently, the integration of next-generation Artificial Intelligence (AI), visual processing, intelligent sensing, multimodal fusion processing, and robotic technology is increasingly prevalent in livestock farming. The advantages of these technologies lie in their rapidity and efficiency, coupled with their capability to acquire livestock data in a non-contact manner. Based on this, we provide a comprehensive summary and analysis of the primary advanced technologies employed in the non-contact acquisition of livestock phenotypic data. This review focuses on visual and AI-related techniques, including 3D reconstruction technology, body dimension acquisition techniques, and live animal weight estimation. We introduce the development of livestock 3D reconstruction technology and compare the methods of obtaining 3D point cloud data of livestock through RGB cameras, laser scanning, and 3D cameras. Subsequently, we explore body size calculation methods and compare the advantages and disadvantages of RGB image calculation methods and 3D point cloud body size calculation methods. Furthermore, we also compare and analyze weight estimation methods of linear regression and neural networks. Finally, we discuss the challenges and future trends of non-contact livestock phenotypic data acquisition. Through emerging technologies like next-generation AI and computer vision, the acquisition, analysis, and management of livestock phenotypic data are poised for rapid advancement. Full article

The development of remote health monitoring systems has focused on enhancing healthcare services’ efficiency and quality, particularly in chronic disease management and elderly care. These systems employ a range of sensors and wearable devices to track patients’ health status and offer real-time feedback to healthcare providers. This facilitates prompt interventions and reduces hospitalization rates. The aim of this study is to explore the latest developments in the realm of remote health monitoring systems. In this paper, we explore a wide range of domains, spanning antenna designs, small implantable antennas, on-body wearable solutions, and adaptable detection and imaging systems. Our research also delves into the methodological approaches used in monitoring systems, including the analysis of channel characteristics, advancements in wireless capsule endoscopy, and insightful investigations into sensing and imaging techniques. These advancements hold the potential to improve the accuracy and efficiency of monitoring, ultimately contributing to enhanced health outcomes for patients. Full article

The rapid technological advancements in the current modern world bring the attention of researchers to fast and real-time healthcare and monitoring systems. Smart healthcare is one of the best choices for this purpose, in which different on-body and off-body sensors and devices monitor and share patient data with healthcare personnel and hospitals for quick and real-time decisions about patients’ health. Cognitive radio (CR) can be very useful for effective and smart healthcare systems to send and receive patient’s health data by exploiting the primary user’s (PU) spectrum. In this paper, tree-based algorithms (TBAs) of machine learning (ML) are investigated to evaluate spectrum sensing in CR-based smart healthcare systems. The required data sets for TBAs are created based on the probability of detection (${\mathcal{P}}_d$) and probability of false alarm (${\mathcal{P}}_f$). These data sets are used to train and test the system by using fine tree, coarse tree, ensemble boosted tree, medium tree, ensemble bagged tree, ensemble RUSBoosted tree, and optimizable tree. Training and testing accuracies of all TBAs are calculated for both simulated and theoretical data sets. The comparison of training and testing accuracies of all classifiers is presented for the different numbers of received signal samples. Results depict that optimizable tree gives the best accuracy results to evaluate the spectrum sensing with minimum classification error (MCE). Full article

A wideband low-profile radiating G-shaped strip on a flexible substrate is proposed to operate as biomedical antenna for off-body communication. The antenna is designed to produce circular polarization over the frequency range 5–6 GHz to communicate with WiMAX/WLAN antennas. Furthermore, it is designed to produce linear polarization over the frequency range 6–19 GHz for communication with the on-body biosensor antennas. It is shown that an inverted G-shaped strip produces circular polarization (CP) of the opposite sense to that produced by G-shaped strip over the frequency range 5–6 GHz. The antenna design is explained and its performance is investigated through simulation, as well as experimental measurements. This antenna can be viewed as composed of a semicircular strip terminated with a horizontal extension at its lower end and terminated with a small circular patch through a corner-shaped strip extension at its upper end to form the shape of “G” or inverted “G”. The purpose of the corner-shaped extension and the circular patch termination is to match the antenna impedance to 50 $\mathsf{\Omega }$ over the entire frequency band (5–19 GHz) and to improve the circular polarization over the frequency band (5–6 GHz). To be fabricated on only one face of the flexible dielectric substrate, the antenna is fed through a co-planar waveguide (CPW). The antenna and the CPW dimensions are optimized to obtain the most optimal performance regarding the impedance matching bandwidth, 3dB Axial Ratio (AR) bandwidth, radiation efficiency, and maximum gain. The results show that the achieved 3dB-AR bandwidth is 18% (5–6 GHz). Thus, the proposed antenna covers the 5 GHz frequency band of the WiMAX/WLAN applications within its 3dB-AR frequency band. Furthermore, the impedance matching bandwidth is 117% (5–19 GHz) which enables low-power communication with the on-body sensors over this wide range of the frequency. The maximum gain and radiation efficiency are 5.37 dBi and 98%, respectively. The overall antenna dimensions are 25 × 27 × 0.13 mm${}^3$ and the bandwidth-dimension ratio (BDR) is 1733. Full article

Human activity recognition has become an attractive research area with the development of on-body wearable sensing technology. Textiles-based sensors have recently been used for activity recognition. With the latest electronic textile technology, sensors can be incorporated into garments so that users can enjoy long-term human motion recording worn comfortably. However, recent empirical findings suggest, surprisingly, that clothing-attached sensors can actually achieve higher activity recognition accuracy than rigid-attached sensors, particularly when predicting from short time windows. This work presents a probabilistic model that explains improved responsiveness and accuracy with fabric sensing from the increased statistical distance between movements recorded. The accuracy of the comfortable fabric-attached sensor can be increased by $\mathrm{67\%}$ more than rigid-attached sensors when the window size is $0.5$$\mathrm{s}$. Simulated and real human motion capture experiments with several participants confirm the model’s predictions, demonstrating that this counterintuitive effect is accurately captured. Full article

This paper presents a conformal and biodegradable circularly polarized microwave sensor (CPMS) that can be utilized in several medical applications. The proposed textile sensor can be implemented in a Smart Bra system for breast cancer detection (BCD) and a wireless body area network (WBAN). The proposed sensor is composed of a wideband circularly polarized (CP) textile-based monopole antenna with an overall size of 33.5 × 33.5 mm² (0.2 λ_o × 0.2 λ_o) and CPW feed line. The radiating element and ground are fabricated using silver conductive fabric and stitched to a cotton substrate of thickness 2 mm. In the proposed design, a slot is etched in the radiating element to extend bandwidth from 1.8 to 8 GHz at |S₁₁| ≤ −10 dB. It realizes a circularly polarized output with AR ≤ 3 dB operation band from 1.8 to 4 GHz and an average gain of 6 dBi. The proposed CPMS’s performance is studied both off-body (air) and on-body in proximity to breast models with and without tumors using near-field microwave imaging. Moreover, the axial ratio is recorded as a feature for a circularly polarized antenna and adds another degree of freedom for cancer detection and data analysis. It assists in detecting tumors in the breast by analyzing the magnitude of the electric field components in vertical and horizontal directions. Finally, the radiation properties are recorded, as well as the specific absorption rate (SAR), to ensure safe operation. The proposed CPMS covers a bandwidth of 1.8–8 GHz with SAR values following the 1 g and 10 g standards. The proposed work demonstrates the feasibility of using textile antennas in wearables, microwave sensing systems, and wireless body area networks (WBANs). Full article

Sweat contains a broad range of important biomarkers, which may be beneficial for acquiring non-invasive biochemical information on human health status. Therefore, highly selective and sensitive electrochemical nanosensors for the non-invasive detection of sweat metabolites have turned into a flourishing contender in the frontier of disease diagnosis. A large surface area, excellent electrocatalytic behavior and conductive properties make nanomaterials promising sensor materials for target-specific detection. Carbon-based nanomaterials (e.g., CNT, carbon quantum dots, and graphene), noble metals (e.g., Au and Pt), and metal oxide nanomaterials (e.g., ZnO, MnO₂, and NiO) are widely used for modifying the working electrodes of electrochemical sensors, which may then be further functionalized with requisite enzymes for targeted detection. In the present review, recent developments (2018–2022) of electrochemical nanosensors by both enzymatic as well as non-enzymatic sensors for the effectual detection of sweat metabolites (e.g., glucose, ascorbic acid, lactate, urea/uric acid, ethanol and drug metabolites) have been comprehensively reviewed. Along with this, electrochemical sensing principles, including potentiometry, amperometry, CV, DPV, SWV and EIS have been briefly presented in the present review for a conceptual understanding of the sensing mechanisms. The detection thresholds (in the range of mM–nM), sensitivities, linear dynamic ranges and sensing modalities have also been properly addressed for a systematic understanding of the judicious design of more effective sensors. One step ahead, in the present review, current trends of flexible wearable electrochemical sensors in the form of eyeglasses, tattoos, gloves, patches, headbands, wrist bands, etc., have also been briefly summarized, which are beneficial for on-body in situ measurement of the targeted sweat metabolites. On-body monitoring of sweat metabolites via wireless data transmission has also been addressed. Finally, the gaps in the ongoing research endeavors, unmet challenges, outlooks and future prospects have also been discussed for the development of advanced non-invasive self-health-care-monitoring devices in the near future. Full article

The study of human activity recognition concentrates on classifying human activities and the inference of human behavior using modern sensing technology. However, the issue of domain adaptation for inertial sensing-based human activity recognition (HAR) is still burdensome. The existing requirement of labeled training data for adapting such classifiers to every new person, device, or on-body location is a significant barrier to the widespread adoption of HAR-based applications, making this a challenge of high practical importance. We propose the semi-supervised HAR method to improve reconstruction and generation. It executes proper adaptation with unlabeled data without changes to a pre-trained HAR classifier. Our approach decouples VAE with adversarial learning to ensure robust classifier operation, without newly labeled training data, under changes to the individual activity and the on-body sensor position. Our proposed framework shows the empirical results using the publicly available benchmark dataset compared to state-of-art baselines, achieving competitive improvement for handling new and unlabeled activity. The result demonstrates SAA has achieved a 5% improvement in classification score compared to the existing HAR platform. Full article

Sweat-rate measurement has received more and more attention, especially for specific groups, such as athletes, soldiers and manual workers, due to their excessive sweat loss under prolonged intense heat stress, which increases the risk of dehydration and electrolyte imbalance. The highly effective manufacture of a sweat-sensing device is essential to its wide range of applications in perspiration-related physiological information detection. In this work, we propose a simple and cost-effective strategy for the manufacture of a microfluidic sweat-rate-sensing patch via laser cutting and transfer printing technology. A copper foil tape is used as the electrode for in situ admittance based sweat-rate-sensing. The detection circuits and measurement conditions are optimized to prevent the negative effect of an electrochemical reaction between a copper electrode and sweat for precise admittance measurement. In vitro and on-body experiments demonstrate that the copper electrode is applicable for admittance-based sweat sensing and is capable of achieving equivalent sensing accuracy as a gold electrode and that the proposed sensor structure can perform consecutive and accurate sweat-rate-sensing and facilitates a significant increase in manufacturing efficiency. Full article

Wearable sweat sensors offer the possibility of continuous real-time health monitoring of an individual at a low cost without invasion. A variety of sweat glucose sensors have been developed thus far to help diabetes patients frequently monitor blood glucose levels through sweat glucose as a surrogate marker. The present study demonstrates the development and characterization of a three-dimensional paper-based microfluidic electrochemical integrated device (3D PMED) for measuring glucose concentration in sweat in real-time via simple, non-invasive, capillary-action-based sample collection. The device was selective for glucose, and it detected glucose accurately in the clinically relevant range (0~2 mM) in an off-body setup. To the best of our knowledge, this is the first time NEXAR™ has been used for biosensing applications. Further, the developed glucose sensor has acceptable sensitivity of 16.8 µA/mM/cm². Importantly, in an on-body setup, the device achieved a significant amperometric response to sweat glucose in a very short amount of time (a few seconds). With detailed investigations, this proof-of-concept study could help further the development of sensitive and selective sweat-based glucose sensing devices for real-time glucose monitoring in diabetes patients. Full article