Smart Wearable Sensors Research Articles

Temperature-Stress Bimodal Sensing Conductive Hydrogel-Liquid Metal by Facile Synthesis for Smart Wearable Sensor.

Conductive hydrogels have attracted great attention due to their promising applications in wearable sensors. However, developing conductive hydrogels with excellent sensor properties and multiple stimuli responsiveness for smart wearable devices is still a challenge. This paper presents a facile synthetic method of a crosslinked chitosan quaternary ammonium salt and liquid metal (CHACC-LM) composite hydrogel with temperature-stress bimodal sensing for smart wearable sensor. LM as liquid fillers toughen the hydrogel matrix (stress: 1.11MPa) and enhance the hydrogel extensibility (strain: 233%). The CHACC-LM hydrogel exhibits conductivity , excellent antibacterial properties (> 99%), an electrical self-healing property, and strain sensitivity (GF = 1.6). In addition, the CHACC-LM hydrogel can be used as wearable flexible sensors with the ability of monitoring human activities directly and the distinguished ability of discerning subtle motions (handwriting). It also shows sensitivity in the external environment such as low temperature, thermal response, and water solution. Importantly, the composite hydrogel simultaneous response to different stress and temperature stimuli. Furthermore, the CHACC-LM hydrogel can be used for gesture recognition and to control the manipulator in human-computer interaction. All these properties provide a great scope for researchers to achieve practical advances in smart wearable sensors.

Hyperledger blockchain based secure storage of electronic health record system in edge nodes

An Electronic Health Record (EHR) is a database for storing patients medical information collected from different sources such as smart wearable devices, smart sensors and diagnostic imaging equipment. An EHR contains sensitive private information for the patients and the treatment of their diseases. Furthermore, it’s often shared among different members consists of healthcare providers, insurance companies, medical researchers, and others. The main difficulty for EHR information management is the result of gathering, storing, and sharing patient healthcare without affecting privacy and security. Blockchain has recently proposed an efficient way to manage EHR data. This paper provides hybrid architecture for EHR data management by using both Hyperledger blockchain network in on-chain and edge node in off-chain. In this architecture is used to authenticate user without affecting sensitive patient’s information and also used to authenticate the encrypted EHR information in edge node. In the on-chain approach EHR activities and Patient authentication activities are recorded in the blockchain for the purpose of accountability and traceability. Edge nodes stored the encrypted EHR data in the off-chain method. So the combination of on-chain and off-chain approaches only allows the authorized data user who has to meet the EHR access activities and to decrypt the EHR data. If the EHR information is alter by an unauthorized user, the hash code is newly generated which is different from old hash code stored in the blockchain. As the result the user can easily detect that their EHR information has been hacked.

Wearable biomolecule smart sensor based on Au@PB NPs with high electrochemical activity

Wearable biosensors have aroused great interest because of their potential to provide continuous and real-time physiological information through dynamic and fast response to biochemical markers in biological fluids. When the wearable sensor is used for fluid detection, required to sensors own high sensitivity and low detection limit since the complex substrate and trace amount of target. Therefore, prepared high response nanomaterials play a crucial for fabrication of wearable sensors. In our work, Prussian blue nanomaterials with core-shell structure was successfully synthesized via using Au NPs as catalyst, as prepared core-shell structural Au@PB NPs exhibited high electrochemical activity, wonderful water solubility and high stability. Then combined with screen printing technology to construct highly stable flexible sensors for the detection of biomarkers with sensitivities 301.5 μA·mM−1·cm−2 and 29.3 μA·mM−1·cm−2 respectively. Furthermore, combined with microfluidic technology, it is expected to be used in health management monitoring and clinical application on a large scale.

Biotemplated Fabrication of a Multifunctional Superwettable Shape Memory Film for Wearable Sensing Electronics and Smart Liquid Droplet Manipulation.

Wearable superwettable surfaces with dynamic tunable wettability and self-healability are promising for advanced wearable electronics, whereas have been rarely reported. Herein, a flexible superhydrophobic shape memory film (SSMF) with switchable surface wettability and high strain sensitivity has been conveniently fabricated. The surface topography of the SSMF can be finely adjusted by a reversible stretching (bending)/recovery way, which makes it feasible to control the surface-switchable adhesive superhydrophobicity by simple body movements, demonstrating great advantages in selective droplet manipulation and smart control of droplet movement. Moreover, benefitting from the hierarchical micro/nanostructures and outstanding sensing performance, the flexible SSMFs with good adaptivity and durability can serve as smart wearable sensors attached to human skin to achieve full-range and real-time detection of human motions and intelligent control of Internet of Things. More interestingly, the unique dynamic dewetting property enables the sensors to work in a humid environment or rainy days. Overall, this work successfully integrates dynamic tunable superwettability into design of intelligent wearable electronics with multifunctions. The obtained SSMF-based wearable surface with dynamic dewetting properties reveals great potential in versatile application fields such as liquid-repellent electronics, wearable droplet manipulators, and all-weather intelligent actuators.

Animal Silk-Derived Amorphous Carbon Fibers for Electricity Generation and Solar Steam Evaporation.

Animal silk-derived carbon materials are of interest to various applications, such as smart cloth and wearable sensors. However, it remains a challenge to massively transform silks into continuous carbon fibers. In this work, carbon fibers based on two kinds of animal silks, i.e., Bombyx mori (B. mori) silk and Antheraea pernyi (A. pernyi) silk, are prepared by using a large-scale-capable one-step heating process without any additives or activation process. These carbon fibers and yarns are electroconductive and mechanically robust. To expand the application of these carbonized silks, we further weaved them with cotton yarns to obtain composite fabrics with different textures and evaluated their performance for solar steam evaporation. Our results confirmed that the advantages of these composite fabrics in light absorption, large surface area, and hierarchical liquid transport channels allowed them to be used as a solar steam generation for desalination and sewage treatment. In addition, we reported that these conductive carbon fibers could be assembled into fluidic nanogenerators to generate electricity from the water flow. This work is expected to guide a large-scale preparation and use of animal silk-derived amorphous carbon fibers.

Low Cost Wearable Multi-Gas Chemical Sensor Badges for First Responders

IntroductionToxic Industrial Chemicals (TICs) are chemical substances which produce toxic effects if encountered in sufficient quantities and/or long durations. TICs are a great concern for the U.S. Department of Homeland Security (DHS) since they would be easier for terrorists to obtain and use than Chemical Warfare Agents (CWAs). Therefore, first responders need a small, rugged, wearable, monitor that alerts them when they are facing TICs, and if so, what is present and at what levels. While there are many commercially available handheld and personal gas monitors for TICs, most are bulky (more than 0.75” in thickness) and expensive ($500 or more). These monitors are primarily used in cities having a population of 100k or more. Hence, there is a need for a low cost (less than $100) wearable multi-gas sensor. Such wearable sensors will also find use in the much bigger application of industrial worker safety monitoring.To address this need TDA Research (TDA) has developed a simple, low cost wearable smart chemical sensor badge that has high sensitivity and selectivity to multiple TICs at trace (low parts per million) concentrations and triggers distinct alarms at permissible exposure limit (PEL), and Immediately Dangerous to Life or Health (IDLH) concentrations. Our wearable smart sensor badge uses a Lab-on-a-chip, is smaller than 2” x 2” x 0.75”, and can detect multiple TICs ( 4 to 6) of interest, and has a low rate of false alarms so that easy and widespread deployment can be achieved.TDA's sensorWe have demonstrated the operation of the sensor in a fully integrated prototype to detect four to six TICs among the H2S, NH3, PH3, HCN, Cl2, and NO2 at ppm levels and produce an electrical signal that can be used to identify and quantify them. We then obtained performance data that included results for these TICs both separately and together and in the presence of interferents, such as diesel and bleach. The sensor response was correlated and corrected based on ambient temperature, relative humidity levels and the air flow rate through the sensor. This low cost sensor is built using commercial-off-the-shelf (COTS) components along with TDA proprietary media and detection/quantification algorithms to detect and quantify TICs, eliminating false positives and false negatives. The sensors also have the ability to generate multiple alarms (audible, visual, and vibrate) at high (IDLH) and low (PEL) concentrations for four or more of the TICs in a small wearable form factor. TDA’s sensor has the unique ability to distinguish between different volatile organic compounds (VOCs) and oxidizes (Cl2, NO2 and even ozone). The attached figure is a photo of the TDA sensor. The results from the TDA sensor development and their performance under different threats at different humidity and temperature levels will be presented at the meeting. Figure 1

Digital Transformation in Personalized Medicine with Artificial Intelligence and the Internet of Medical Things.

Digital transformation is impacting every facet of science and society, not least because there is a growing need for digital services and products with the COVID-19 pandemic. But the need for digital transformation in diagnostics and personalized medicine field cuts deeper. In the past, personalized/precision medicine initiatives have been unable to capture the patients' experiences and clinical outcomes in real-time and in real-world settings. The availability of wearable smart sensors, wireless connectivity, artificial intelligence, and the Internet of Medical Things is changing the personalized/precision medicine research and implementation landscape. Digital transformation in poised to accelerate personalized/precision medicine and systems science in multiple fronts such as deep real-time phenotyping with patient-reported outcomes, high-throughput association studies between omics and highly granular phenotypic variation, digital clinical trials, among others. The present expert review offers an analysis of these systems science frontiers with a view to future applications at the intersection of digital health and personalized medicine, or put in other words, signaling the rise of "digital personalized medicine."

Dilated causal convolution with multi-head self attention for sensor human activity recognition

Systems of sensor human activity recognition are becoming increasingly popular in diverse fields such as healthcare and security. Yet, developing such systems poses inherent challenges due to the variations and complexity of human behaviors during the performance of physical activities. Recurrent neural networks, particularly long short-term memory have achieved promising results on numerous sequential learning problems, including sensor human activity recognition. However, parallelization is inhibited in recurrent networks due to sequential operation and computation that lead to slow training, occupying more memory and hard convergence. One-dimensional convolutional neural network processes input temporal sequential batches independently that lead to effectively executed operations in parallel. Despite that, a one-dimensional Convolutional Neural Network is not sensitive to the order of the time steps which is crucial for accurate and robust systems of sensor human activity recognition. To address this problem, we propose a network architecture based on dilated causal convolution and multi-head self-attention mechanisms that entirely dispense recurrent architectures to make efficient computation and maintain the ordering of the time steps. The proposed method is evaluated for human activities using smart home binary sensors data and wearable sensor data. Results of conducted extensive experiments on eight public and benchmark HAR data sets show that the proposed network outperforms the state-of-the-art models based on recurrent settings and temporal models.

Smart Wearable Sensors Based on Triboelectric Nanogenerator for Personal Healthcare Monitoring.

Accurate monitoring of motion and sleep states is critical for human health assessment, especially for a healthy life, early diagnosis of diseases, and medical care. In this work, a smart wearable sensor (SWS) based on a dual-channel triboelectric nanogenerator was presented for a real-time health monitoring system. The SWS can be worn on wrists, ankles, shoes, or other parts of the body and cloth, converting mechanical triggers into electrical output. By analyzing these signals, the SWS can precisely and constantly monitor and distinguish various motion states, including stepping, walking, running, and jumping. Based on the SWS, a fall-down alarm system and a sleep quality assessment system were constructed to provide personal healthcare monitoring and alert family members or doctors via communication devices. It is important for the healthy growth of the young and special patient groups, as well as for the health monitoring and medical care of the elderly and recovered patients. This work aimed to broaden the paths for remote biological movement status analysis and provide diversified perspectives for true-time and long-term health monitoring, simultaneously.

Wet-spinning of ionic liquid@elastomer coaxial fibers with high stretchability and wide temperature resistance for strain sensors

Integration of high wearing comfort, excellent conductivity and superior stretchability is required for wearable strain sensors. The construction of ionic conductors into elastic fibers and woven cloths can simultaneously realize wearing comfort and high elasticity. However, the fabrication of ionic conductive fibers with mechanical elasticity and high conductivity for fibrous strain sensors is challenging. Herein, a wet-spun encapsulation strategy is proposed for preparing a highly stretchable and fatigue-resistant ionic conductive fiber with a unique coaxial structure of ionic liquid core and thermoplastic elastomer sheath (IL@TPE). Particularly, the IL@TPE fiber exhibited a high stretchability of >250% and excellent ionic conductivity under a wide temperature range of −50 to 50 °C and long-term storage of >6 months. The as-assembled IL@TPE fibrous strain sensor exhibited excellent stability of >3000 cycles, low hysteresis, fast response time and high linearity for detecting human motions. The wet-spun encapsulation strategy for the fabrication of coaxial ionic fibers holds great promises for applications in smart wearable sensors with large stretchability, wide temperature resistance and high linearity.

Modeling, Fabrication and Integration of Wearable Smart Sensors in a Monitoring Platform for Diabetic Patients.

The monitoring of some parameters, such as pressure loads, temperature, and glucose level in sweat on the plantar surface, is one of the most promising approaches for evaluating the health state of the diabetic foot and for preventing the onset of inflammatory events later degenerating in ulcerative lesions. This work presents the results of sensors microfabrication, experimental characterization and FEA-based thermal analysis of a 3D foot-insole model, aimed to advance in the development of a fully custom smart multisensory hardware–software monitoring platform for the diabetic foot. In this system, the simultaneous detection of temperature-, pressure- and sweat-based glucose level by means of full custom microfabricated sensors distributed on eight reading points of a smart insole will be possible, and the unit for data acquisition and wireless transmission will be fully integrated into the platform. Finite element analysis simulations, based on an accurate bioheat transfer model of the metabolic response of the foot tissue, demonstrated that subcutaneous inflamed lesions located up to the muscle layer, and ischemic damage located not below the reticular/fat layer, can be successfully detected. The microfabrication processes and preliminary results of functional characterization of flexible piezoelectric pressure sensors and glucose sensors are presented. Full custom pressure sensors generate an electric charge in the range 0–20 pC, proportional to the applied load in the range 0–4 N, with a figure of merit of 4.7 ± 1 GPa. The disposable glucose sensors exhibit a 0–6 mM (0–108 mg/dL) glucose concentration optimized linear response (for sweat-sensing), with a LOD of 3.27 µM (0.058 mg/dL) and a sensitivity of 21 µA/mM cm2 in the PBS solution. The technical prerequisites and experimental sensing performances were assessed, as preliminary step before future integration into a second prototype, based on a full custom smart insole with enhanced sensing functionalities.

Monitoring Body Fluids in Textiles: Combining Impedance and Thermal Principles in a Printed, Wearable, and Washable Sensor

This work explores the feasibility of coupling two different techniques, the impedance and the transient plane source (TPS) principle, to quantify the moisture content and its compositional parameters simultaneously. The sensor is realized directly on textiles with the use of printing and coating technology. Impedance measurements use the fluid's electrical properties, while the TPS measurements are based on the thermal effusivity of the liquid. Impedance and TPS measurements show equal competency in measuring the fluid volume with a lowest measurable quantity of 0.5 μL, enabling ultralow volume passive measurements for sweat analysis. Both sensor principles were tested by monitoring the drying of a wet cloth and the measurements show perfect repeatability and accuracy. Nevertheless, when the biofluid property changes, the TPS sensor does not reflect this information on its readings, whereas, on the other hand, impedance can provide information on compositional changes. However, since the volume of the fluid changes simultaneously, one cannot differentiate between a volume change and a compositional change from impedance measurements alone. Therefore, we show in this work that we can apply impedance to measure the compositional properties; meanwhile, the TPS measurements accurately carry out volume measurements irrespective of the interferences from its compositional variations. To prove this, both of these techniques are applied for the quantification and composition monitoring of sweat, showing the capability to measure moisture content and compositional parameters simultaneously. TPS measurements can also be an indicator of the local temperature of the medium confined by the sensor, and it does not influence the fluid parameters. Compiling both impedance and thermal sensors in a single platform triggers smart wearable prospects of metering the liquid volume and simultaneously analyzing other compositional changes and body temperature. Finally, the repeatability and stability of the sensor readings and the washability of the device are tested. This device could be a potential sensing tool in real-life applications, such as wound monitoring and sweat analysis, and could be a promising addition toward future smart wearable sensors.

Flexible, Robust, and Durable Aramid Fiber/CNT Composite Paper as a Multifunctional Sensor for Wearable Applications.

Flexible paper-based sensors may be applied in numerous fields, but this requires addressing their limitations related to poor thermal and water resistance, which results in low service life. Herein, we report a paper-based composite sensor composed of carboxylic carbon nanotubes (CCNTs) and poly-m-phenyleneisophthalamide (PMIA), fabricated by a facile papermaking process. The CCNT/PMIA composite sensor exhibits an ability to detect pressures generated by various human movements, attributed to the sensor's conductive network and the characteristic "mud-brick" microstructure. The sensor exhibits the capability to monitor human motions, such as bending of finger joints and elbow joints, speaking, blinking, and smiling, as well as temperature variations in the range of 30-90 °C. Such a capability to sensitively detect pressure can be realized at different applied frequencies, gradient sagittas, and multiple twists with a short response time (104 ms) even after being soaked in water, acid, and alkali solutions. Moreover, the sensor demonstrates excellent mechanical properties and hence can be folded up to 6000 times without failure, can bear 5 kg of load without breaking, and can be cycled 2000 times without energy loss, providing a great possibility for a long sensing life. Additionally, the composite sensor shows exceptional Joule heating performance, which can reach 242 °C in less than 15 s even when powered by a low input voltage (25 V). From the perspective of industrialization, low-cost and large-scale roll-to-roll production of the paper-based sensor can be achieved, with a formed length of thousands of meters, showing great potential for future industrial applications as a wearable smart sensor for detecting pressure and temperature, with the capability of electric heating.

Preparation of silver nanoparticles by solid-state redox route from hydroxyethyl cellulose for antibacterial strain sensor hydrogel

As a smart wearable sensor device, the mildew of the biocompatible hydrogel limits its application. In this paper, silver nanoparticles were prepared by solid-state reduction of hydroxyethyl cellulose and compounded into a chemically cross-linked hydrogel as an antibacterial, flexible strain sensor. Because the high surface energy of silver nanoparticles can quench free radicals, we designed three initiators to synthesize hydrogels: ammonium persulfate (APS), 2,2'-Azobis(2-methylpropionitrile) (AIBN) and 2,2'-azobis(2-methylpropionamidine) dihydrochloride (AIBA). Impressively, silver nanoparticles composite hydrogel could only be successfully fabricated and triggered by the AIBN. The mechanical property of the composite hydrogel (0.12 MPa at 704.33 % strain) was significantly improved because of dynamic crosslinking point by HEC. Finally, the composite hydrogels are applied to the field of antibacterial strain sensor and the highest Gauge Factor (GF) reached 4.07. This article proposes a novel, green and simple strategy for preparing silver nanoparticles and compounding them into a hydrogel system for antibacterial strain sensor.

Wearable Porous Au Smartsensors for On-Site Detection of Multiple Metal Ions.

Owing to advantages of miniaturization, convenient integration, flexibility, and real-time monitoring, wearable smartsensors have received numerous attention and greatly developed in various fields. However, there usually appears a contradiction between sensing behaviors and simple fabricated methods, seriously limiting on-site detection of actual samples. In this work, a porous Au-based smartsensor has been in situ prepared by combining screen printing technology and sacrificial template electrodeposition. Thanks to abundant active adsorption sites, multiple metal ions (Pb, Cu, and Hg) can be easily achieved on-site detection by this smart platform with a low limit of detection as well as high sensitivity, excellent selectivity, good stability, repeatability, and bending performance. Significantly, it also exhibits a reliable detective capability in actual liquid cosmetic samples with a portable cellphone, which identically corresponds to standard inductively coupled plasma-mass spectrometry (ICP--MS) evaluation. Therefore, this wearable smartsensor provides a promising platform for artificial intelligence application in future daily life.

RETRACTED ARTICLE: Error-less data fusion for posture detection using smart healthcare systems and wearable sensors for patient monitoring

Smart healthcare applications rely on frequent monitoring information from the patient’s body using wearable sensors (WSs). Healthcare systems rely on multi-level information for identifying the diseases and hence providing a proper diagnosis. The challenging task is the acquisition of WS information and assimilation of the same for diagnosis purposes. This proposal introduces an error-less data fusion (EDF) technique for improving the accuracy of posture identification. In a smart healthcare system, WS data fusion requires intense care for providing sensitive analysis of the diagnosed disease. Therefore, it depends on the WS data and collectively analyzes the same frequency for improving diagnosis efficiency. Errors due to the sensors’ failure, invariant time factor, aggregation, and analysis result in failed or inappropriate recommendations. This proposal addresses this issue using EDF relevant for posture detection of patients through healthcare monitoring systems. The WS data features are intensively analyzed using active and repeated learning for identifying the errors in different fusion processes. This technique is keen on improving the precision of posture detection, time of analysis, and error rate, irrespective of the users’ movement actions.

Wireless Sensor Networks with Elements of Artificial Intelligence for Medicine

The integrated digitalization of medicine, the use of the AIIoT (Artificial Intelligence IoT), and networks of medical wireless sensors offers new opportunities to remotely support the quality of life of chronically ill patients, the elderly, post-corona pandemic people and athletes and professionals with heavy physical or mental workloads. Wireless sensor networks and computer remote means of maintaining quality of life include smart wearable medical sensors and analog interface. The sensors are intended to monitor heart rate, respiration and blood pressure, determine skin moisture, patient's position and other medical parameters in real time. Miniature interfaces are intended for data acquisition, analog-to-digital conversion, data preprocessing of medical parameters received from wearable medical sensors and data communication to remote diagnostic center. The current state and prospects for the development of these tools are discussed in the publication.

Development of smart wearable sensors for life healthcare

Abstract Wearable biosensors are gaining tremendous interest in the clinical and biological medical fields for their potential in providing patients with real-time diagnostic tools and time-sensitive information, non-invasive measurements of biochemical markers distributed in body fluids throughout the body. These sensors replace part of the equipment that can only be installed in hospitals and become a new choice for assessing human health. Herein, critical perspectives are put forward regarding wearable sensors in the future digital health monitoring industry. Moreover, the materials and processing technologies involved have also been discussed in recent years, along with their monitoring schemes and system-level integration technologies. Finally, the probability of wearable sensors being used in early disease detection is considerable. Thus, despite potential challenges, it is still quite promising for wearable sensors to come into production.

Preparation and Application of Wearable Smart-Sensors for Vapor/Liquid Amphibious Methanol Monitoring

Preparation and Application of Wearable Smart-Sensors for Vapor/Liquid Amphibious Methanol Monitoring

Facile Fabrication of MoSe2 on Paper as an Electromechanical Piezoresistive Pressure–Strain Sensor

Over the last decade, the rapid development in health care industry and security has driven innovations in the field of wearable electronics and smart sensors. This report demonstrates the fabrication of molybdenum diselenide (MoSe2) by a facile, low-cost and scalable hydrothermal synthesis method followed by a vacuum filter deposition on cellulose paper. The fabricated device is utilized as a pressure and strain sensor. The fabricated device is passivated using polydimethylsiloxane (PDMS), and also, PDMS film enhances the flexibility of the device. The calculated sensitivity of pressure sensor is ~0.3 kPa <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , which is comparable and relatively better than the pressure sensor fabricated using sophisticated techniques. Furthermore, the device exhibited significant of merit for strain sensing with a gauge factor of ~6.84. The fabricated device showed excellent resilience in 500 cycle bending tests, which promises the robustness of the device. As an application, the fabricated sensor is used to develop a wireless keyboard by integrating the sensor on fingertips of an individual and a real-time human motion monitoring application in which the data are transmitted wirelessly to a smartphone with a dedicated android application. The successful fabrication of MoSe2/paper-based cost-effective, flexible, and biodegradable pressure-strain sensor shows tremendous applications in future intelligent user interface systems, human security, and personal health care monitoring.