Health Monitoring Applications Research Articles

Electrochemical sensor based on three-dimensional Skeleton/Skin ink for the ultrasensitive detection of dopamine released from neural cells

The development of a miniaturized and low-cost platform for the simultaneous and high-sensitive detection of the nervous system metabolites has gained great interest for early diagnosis and prognostic monitoring of some diseases. Herein, a three-dimensional (3D) interpenetrating hydrogel ink synthesized through a simple hydrothermal assembly way was utilized for the sensitive detection of dopamine released from neural cells in vivo. Carbon nanotubes (CNTs) were utilized as the structural framework, while reduced graphene nanosheets were employed as the outer covering, resulting in the formation of a “skeleton/skin” structure. The attachment of hydrophilic IL molecules onto the graphene nanosheets confers distinctive hydrophilic traits on the resulting nanocomposite material. Consequently, this facilitates its effortless conversion into freestanding paper-like material via printing. The modified electrode was proved to be efficient in electrochemical detection of dopamine by means of DPV, despite the existence of interfering substances like ascorbic and uric acid. With a sensitivity of 41.86 μA μM−1 cm−2 and a low detection limit of 2 nM (S/N = 3), the paper electrode exhibited a linear response ranging from 0.01 to 50 μM. Furthermore, the rGO-CNT-IL was used to determine dopamine levels in the neural cells. The modified paper electrode can also be potentially applied for further drug detection, health monitoring and other biological applications.

Personal Health Monitoring with Bluetooth Sensors and Android Smartphones

The objective of this research project is to utilize a multi-modal biosensor platform incorporating multiple MEMS sensors for continuous vital signs monitoring, and integrate it with a smartphone unit for cellular network connectivity and communication.Using Bluetooth interface, sensor data is transferred to smartphone devices equipped with Android operating system. This enables real time data analysis with smartphone devices. Smartphones are used for displaying incoming sensor data and performing signal processing. With the use of internet enabled smartphones, patients and doctors can be more in touch, and doctors can monitor/keep track of their patients’ health status. The miniature size of the sensors allows them to be practical and convenient to be carried around in everyday activities. Since most patients already carry personal smartphones, health monitoring technology can be deemed affordable, requiring only Bluetooth enabled sensors and an application software.Although several patient health monitoring applications exist, these solutions typically have a separate device to process the data and send it wirelessly. The goal of this research is to replace this extra hardware device with the patient’s personal phone to do the processing and the sharing of the data. The proposed solution makes it easier for the patients to carry the device around, since it is comprised of only the sensors and an application software running on the host smartphone platform that we all carry in our daily lives.

A Battery-free Breathing monitoring System for post-COVID education

Embedded systems have more and more designs and applications in wearable health monitoring and the Internet of Things (IoT) recently. In this work, a battery-free human breathing monitoring system based on soft polyvinylidene fluoride (PVDF) piezoelectric thin film for pressure sensing and solid lead zirconate titanate (PZT) pads for powering energy harvest is designed and fabricated. Proper material and structure are chosen by the mechanical analysis to fabricate the systems. Then the device is used to measure the real breathing signal while studying (the memory test is used to represent the study procedure). The breathing frequency and intensity data are extracted and used in correction analysis with the testing score. And the results show that peaceful, slow, and deep breathing will have a positive influence on the testing score. This study opens the mind to combine the engineering designed device with education research, showing the broad application of mechanical and electronic systems.

Scalable Manufacturing of Large-Area Pressure Sensor Array for Sitting Posture Recognition in Real Time.

Pressure sensors are considered the key technology for potential applications in real-time health monitoring, artificial electronic skins, and human-machine interfaces. Despite the significant progress in developing novel sensitive materials and constructing unique sensor structures, it remains challenging to fabricate large-area pressure sensor arrays due to the involvement of complex procedures including photolithography, laser writing, or coating. Herein, a scalable manufacturing approach for the realization of pressure sensor arrays with substantially enlarged sensitive areas is proposed using a versatile screen-printing technique. A compensation mechanism is introduced into the printing process to ensure the precise alignment of conductive electrodes, insulation layers, and sensitive microstructures with an alignment error of less than 4 μm. The fully screen-printed sensors exhibit excellent collective sensing performance, such as a reasonable pressure sensitivity of -2.2 kPa-1, a fast response time of 40 ms, and superior durability over 3000 consecutive pressures. Additionally, an integrated 16 × 32 pressure sensor array with a sensing area of 190 × 380 mm2 is demonstrated to precisely recognize the sitting postures and the body weights, showing great potential in continuous and real-time health status monitoring.

Vibration Energy Harvesting for Aerospace Applications

The aerospace industry is actively seeking to employ wireless sensors for various data collection and structural health monitoring applications. While continued development of wireless sensors and sensor networks holds promise in the aerospace industry, the challenge of powering these sensors must still be addressed. Vibration energy harvesting is one possible approach in this regard, and is particularly promising for aerospace applications due to the ubiquitous presence of vibration energy. In this review paper the generic design considerations and challenges involved in harvesting energy from vibration energy are described. The paper also highlights recent progress addressing current challenges in adapting a vibration energy harvesting approach in a practical application, including resonance frequency tuning and design considerations for optimal energy harvesting performance. Recent progress in adapting this approach to the microscale using MEMS technology is also highlighted.

Evaluation of Atmospheric Phase Correction Performance in 79 GHz Ground-Based Radar Interferometry: A Comparison with 17 GHz Ground-Based SAR Data

Ground-based radar interferometry is capable of measuring target displacement to sub-mm accuracy. W-band ground-based radar has recently been investigated as a potential application for structural health monitoring. On the other hand, the application of W-band ground-based radar for natural slope monitoring is considered in this study due to its advantages in portability and recent cost-effective solutions. In radar interferometry, atmospheric phase screen (APS) is the most relevant phase disturbance that should be corrected for accurate displacement measurement. However, the APS effects in W-band radar interferometry have rarely been discussed. In this context, we study and evaluate the impacts of APS and its potential correction methods for 79 GHz ground-based radar interferometry using multiple-input and multiple-output (MIMO) radar. This paper presents an experimental investigation of a 79 GHz radar system using two types of field experiments conducted in an open flat field and a quarry site. In addition to the W-band radar system, a Ku-band (17 GHz) ground-based synthetic aperture radar (GB-SAR) system was jointly tested to compare different operating frequency bands. The result confirmed the accurate displacement estimation capability of the 79 GHz radar with an appropriate APS correction.

Study on damage identification of High-Speed railway truss bridge based on statistical steady-state strain characteristic function

The localization and quantification of damage in bridge structures have always been a challenging problem, especially for high-speed railway truss bridges with intricate structural forms and complex load conditions. To address this issue, this paper proposes a novel damage indicator based on strain characteristic functions, and integrated it into an automated framework for real-time damage detection which is particularly suitable for in-service structural health monitoring applications. To evaluate the effectiveness of this approach, a study is conducted using a finite element digital twin model to simulate the structural responses at critical areas of high-speed railway truss bridges under train-induced loads. The proposed damage indicator of the model was used to investigate and evaluate different types of bridge damage, reflecting both the location and severity of damage in high-speed railway truss bridges. The results suggest that the proposed damage indicator, when used with a distributed sensor system, shows promise in accurately locating and measuring damage in high-speed railway truss bridges. In addition, for the bridge health monitoring system with sparse sensor distribution, this indicator can provide a probability assessment of various damage conditions of a high-speed railway truss bridge. This information could potentially assist in the process of conducting in-service maintenance of a high-speed railway truss bridge.

Recent Advances in Gas Detection Methodologies with a Special Focus on Environmental Sensing and Health Monitoring Applications─A Critical Review.

With the expansion of the Internet-of-Things (IoT), the use of gas sensors in the field of wearable technology, smart devices, and smart homes has increased manifold. These gas sensors have two key applications─one is the detection of gases present in the environment and the other is the detection of Volatile Organic Compounds (VOCs) that are found in the breath. In this review, we focus systematically on the advancements in the field of various spectroscopic methods such as mass spectrometry-based analysis and point-of-care approach to detect VOCs and gases for environmental monitoring and disease diagnosis. Additionally, we highlight the development of smart sensors that work on the principle of electrochemical detection and provide examples of the same through an extensive literature review. At the end of this review, we highlight various challenges and future perspectives.

Multi-rate Kalman filtering for structural dynamic response reconstruction by fusing multi-type sensor data with different sampling frequencies

In this paper, a novel dynamic response reconstruction method based on multi-rate Kalman filtering (MRKF) is presented. The proposed method starts with representing the structural system by the state-space equation. Then, different observation equations are defined, and that selection is based on the availability of sensor types at a specific time. Not only can the multi-type sensor data sampled at different rates be fused directly, but the presented method also relaxes the collocated monitoring requirement. In addition, future observations are used to benefit the current state estimation by the Rauch, Tung, and Striebel smoothing procedure. The unobserved structural dynamic responses are estimated using the MRKF virtual sensing technique with multi-rate sensor data. Several demonstrative numerical tests are performed to verify the superiority and robustness of the presented MRKF method on one benchmark shear frame model. The experimental test employed a computer-vision-based displacement tracking technique. Results show that the proposed method surmounts the obstacle to deploying consumer-grade cameras in structural health monitoring applications, which provide a low-cost sensing solution without sacrificing response estimation accuracies.

A Fully Wireless and Batteryless Localization System with 50 Micrometre Motion Detection Capability and Adaptive Transmitter Power Control for Point-of-Care Biomedical Applications.

Localization has varied applications in biomedicine, such as wireless capsule endoscopy (WCE), detection of cancerous tissue, drug delivery, robotic surgeries, and brain mapping. Currently, most localization systems are battery-powered and suffer from issues regarding battery leakage and limited battery life, resulting in potential health hazards and inconveniences when using them for continuous health monitoring applications. This paper proposes an entirely wireless and battery-less 2D localization system consisting of an integrated circuit (IC) that is wirelessly powered at a distance of 4 cm by a 40.68 MHz radio frequency (RF) power of only 2 W. The proposed localization system wirelessly transmits a locked sub-harmonic 13.56 MHz signal generated from the wirelessly received 40.68 MHz RF power signal, eliminating the need for a power-hungry oscillator. Additionally, the system, having a measurement latency of 11.3 ms, has also been verified to sense motion as small as 50 μm as well as measure the rate of motion up to 10 beats per minute, therefore extending its application to the detection of physiological motions such as diaphragm motion during breathing. The localizer has a small form factor of 17 mm × 12 mm × 0.2 mm and consumes an average power of 6 μW. Ex vivo measurements using the localizer inside the porcine intestine demonstrate a localization accuracy of less than 5 mm.

On monitoring fretting fatigue damage in solid railway axles by acoustic emission with unsupervised machine learning and comparison to non-destructive testing techniques

Railway axles are safety-critical components of the rolling stock and the consequences of possible in-service failures can have dramatic effects. Although this element is traditionally designed against such failures, the initiation and propagation of service cracks are still occasionally observed, requiring an effective application of non-destructive testing and structural health monitoring approaches. This paper investigates the application of structural health monitoring by acoustic emission to the case of solid railway axles subject to fretting fatigue damage. A full-scale test was performed on a specimen in which artificial notches were suitably manufactured in order to cause the initiation and evolution of fretting fatigue damage up to the stage of relevant propagating fatigue cracks. During the test, both periodical phased array ultrasonic inspections and continuous acquisition of acoustic emission data have been carried out. Moreover, at the end of the test, the specimen was inspected, analyzed and evaluated by visual inspection and magnetic particles testing, while acoustic emission raw data were post-processed by a special unsupervised machine learning algorithm based on an Artificial Neural Network. It is demonstrated that the proposed methodology is very effective to detect the onset of crack initiation in a non-invasive and safe way.

Tremella polysaccharide-based conductive hydrogel with anti-freezing and self-healing ability for motion monitoring and intelligent interaction

The application of conductive hydrogels in flexible wearable devices has garnered significant attention. In this study, a self-healing, anti-freezing, and fire-resistant hydrogel strain sensor is successfully synthesized by incorporating sustainable natural biological materials, viz. Tremella polysaccharide and silk fiber, into a polyvinyl alcohol matrix with borax cross-linking. The resulting hydrogel exhibits excellent transparency, thermoplasticity, and remarkable mechanical properties, including a notable elongation (1107.3 %) and high self-healing rate (91.11 %) within 5 min, attributed to the dynamic cross-linking effect of hydrogen bonds and borax. A strain sensor based on the prepared hydrogel sensor can be used to accurately monitor diverse human movements, while maintaining exceptional sensing stability and durability under repeated strain cycles. Additionally, a hydrogel touch component is designed that can successfully interact with intelligent electronic devices, encompassing functions like clicking, writing, and drawing. These inherent advantages make the prepared hydrogel a promising candidate for applications in human health monitoring and intelligent electronic device interaction.

Laser micro-fabricated multifunctional sensing layer for structural health monitoring

Multifunctional sensing layer (MSL) integrated with multiple sensors and transducers has diverse applications in structural health monitoring (SHM). However, current MSLs still suffer from using rigid sensing units and complicated fabrication methods. To address this, a type of MSL integrated with flexible multi-functional sensing units and stretchable interconnectors was developed in this study by a laser-microfabrication (LMF) method with high efficiency and scalability. The flexible strain sensor has a sensitivity of 8.5, a minimum detection limit of 0.01%, and a durability of up to 12 000 cycles. The flexible temperature sensor has a temperature coefficient of resistance up to 2.75 × 10−3 °C–1 and a response time of 2.5 s. The flexible comb transducer provides a damage detection accuracy of over 90%. Further investigation proves the capability of the MSL for real-time monitoring of pressure vessels. The high efficiency and scalability of the LMF as well as the multi-functional sensing ability pave the potential application of the MSL in SHM.

Ordinary state-based peri‑ultrasound modeling for monitoring crack propagation in plate structures using sideband peak count-index technique

This work presents a nonlocal theory based on ordinary state-based (OSB) peridynamics for numerical modeling of crack propagation under loadings and peri‑ultrasound modeling for elastic wave propagation that interacts with generated cracks in 3-D plate structures. Propagating cracks in plate structures for two types of loading – tensile and shear are considered. For both loading cases, at six different times after the start of the loading the analysis is carried out to investigate how the results vary for these six different stages of crack propagation. An ultrasonic excitation is applied at a point on the surface of the plate to generate guided waves propagating through the plate structure and interacting with generated cracks at different stages of crack formation. The out-of-plane velocity fields are recorded at the receiving material point for further analysis. A relatively new and promising nonlinear ultrasonic (NLU) technique called sideband peak count-index (or SPC-I) is adopted to extract the nonlinear response due to the generated cracks. Results show that when the crack propagation is at its early stage the SPC-I value first increases, and then decreases with time at the advanced stages of crack propagation. This is because for both types of loading (tensile or shear) the newly generated cracks are small cracks (or micro-cracks) in the beginning and then they evolve into macro-cracks under increasing loadings. The observation for SPC-I trends is consistent with other numerical modelings and experimental observations reported in the literature, and such qualitative matching gives confidence in the proposed method. The proposed method combining the peri‑ultrasound modeling and SPC-I technique presented in this paper can provide a new insight for monitoring crack propagation and give some guidance for experimental structural health monitoring (SHM) applications.

Characterization and performance evaluation of fully biocompatible gelatin-based humidity sensor for health and environmental monitoring

Biomaterials will have far reaching effects on solving the ever-rising environmental problems caused by the presently used toxic materials. Disposal of hazardous materials based electronic devices is causing a huge environmental problem in the form of electronic wastes (E-wastes). This study is an effort to contribute in the reduction of E-wastes by presenting the design, fabrication, and performance evaluation of a fully biocompatible humidity sensor with gelatin as the active layer. Gelatin is an excellent choice for humidity sensing because it is a hygroscopic material with the natural ability to attract and absorb water molecules, allowing for an effective detection and measurement of humidity levels in its surrounding environment. We patterned highly conductive (2.05 μΩ-cm) gold (Au) interdigitated electrode (IDE) (50 nm thickness) structure on a glass substrate by using thermal vacuum evaporation followed by the deposition of gelatin thin film by using cost effective technique of spin coating (at 1,500 rpm for 15 s). The device size was 25 mm × 22 mm with a line spacing of 1.50 mm between IDEs. Electrical characterisation was carried out to determine necessary performance parameters of the fully biocompatible humidity sensor and the obtained results exhibited a fast response/recovery time (4/6.30 s), wide operating range (15 %RH to 86 %RH), highly repeatable behaviour of 3 different samples and long lifetime (1 month). Wide variation in the resistance of sensor was observed at extreme humidity sensors ranging from 2.45 MΩ (15 %RH) to 0.12 MΩ (86 %RH). The fabricated sensor was used for the practical applications of health monitoring, touch less sensing, and environment monitoring. Moisture content of multiple commercially available moisturizers was determined by using gelatin based fully biocompatible humidity sensor and the obtained results showed that moisturizing cream had highest moisture content of 87 %RH (0.1 MΩ), followed by 80 %RH (0.30 MΩ) of rose water, and 72 %RH (0.67 MΩ) of baby oil.

Multitasking FBG sensors for condition monitoring with a wideband (DC-MHz) interrogation system.

We demonstrate diagnosis of several machine-condition failures using wide-frequency-band interrogation of fiber Bragg grating (FBG) sensors. In collaboration with Israel's national water company Mekorot Ltd., a scaled-down version of a semi-submerged pumping system was constructed. By monitoring broadband signals from DC to ultrasound (>M H z), at different points of the engine and the submersed pump, the system was able to diagnose incipient cavitation, faulty bearings, and submerged dynamic water-level measurements. In addition, a metal embedded FBG sensor was investigated, revealing the potential of using FBGs in applications where bonding is problematic such as bearing housing. These results prove that wideband data acquisition, together with advanced analytics, could open a variety of new applications in the fields of structural health and machine-condition monitoring.

Spatiotemporally Responsive Hydrogel Dressing with Self-Adaptive Antibacterial Activity and Cell Compatibility for Wound Sealing and Healing.

Adhesive hydrogels containing quaternary ammonium salt (QAS) moieties have shown attractive advantages in treatment for acute wounds, attributed to their high performances in wound sealing and sterilization. However, the introduction of QAS commonly leads to high cytotoxicity and adhesive deterioration. Herein, aimed to solve these two issues, a self-adaptive dressing with delicate spatiotemporal responsiveness is developed by employing cellulose sulfate (CS) as dynamic layers to coat QAS-based hydrogel. In detail, due to the acid environment of wound in the early stages of healing, the CS coating will quickly detach to expose the active QAS groups for maximum disinfectant efficacy; meanwhile, as the wound gradually heals and recovers to a neutral pH, the CS will remain stable to keep QAS screened, realizing a high cell growth-promoting activity for epithelium regeneration. Additionally, attributed to the synergy of temporary hydrophobicity by CS and slow water absorption kinetics of the hydrogel, the resultant dressing possesses outstanding wound sealing and hemostasis performance. At last, this work anticipates this approach to intelligent wound dressings based on dynamic and responsive intermolecular interaction can also be applied to a wide range of self-adaptive biomedical materials employing different chemistries for applications in medical therapy and health monitoring.

Chiral MOF Derived Wearable Logic Sensor for Intuitive Discrimination of Physiologically Active Enantiomer.

Chiral sensors have attracted growing interest due to their application in health monitoring. However, rational design of wearable logic chiral sensors remains a great challenge. In this work, a dual responsive chiral sensor RT@CDMOF is prepared through in situ self-assembly of chiral γ-cyclodextrin metal-organic framework (CDMOF), rhodamine 6G hydrazide (RGH), and tetracyanovinylindane (TCN). The embedded RGH and TCN inherit the chirality of host CDMOF, producing dual changes both in fluorescence and reflectance. RT@CDMOF is explored as a dual channel sensor for chiral discrimination of lactate enantiomers. Comprehensive mechanistic studies reveal the chiral binding process, and carboxylate dissociation is confirmed by impedance and solid-state 1 H nuclear magnetic resonance (NMR). A flexible membrane sensor is successfully fabricated based on RT@CDMOF for wearable health monitoring. Practical evaluation confirms the potential of fabricated membrane sensor in point-of-care health monitoring by indexing the exercise intensity. Based on above, a chiral IMPLICATION logic unit can be successfully achieved, demonstrating the promising potential of RT@CDMOF in design and assembly of novel smart devices. This work may open a new avenue to the rational design of logic chiral sensors for wearable health monitoring applications.

PRIviLY: Private Remote Inference over fulLY connected deep networks for pervasive health monitoring with constrained client-side

Remote deep learning paradigm enables to better leverage the power of deep neural networks in pervasive health monitoring (PHM) applications, especially by addressing the constrained client-side environment. However, remote deep learning in the context of PHM requires to ensure three properties: (1) meet the high accuracy requirement of the healthcare domain, (2) satisfy the client-side constraints, and (3) cope with the privacy requirements related to the high sensitivity of health data. Different privacy-preserving solutions for remote deep learning exit in the literature but many of them fail to fully address the PHM requirements especially with respect to constrained client-side environments. To that end, we propose PRIviLY, a novel privacy-preserving remote inference solution, designed specifically for the popular Fully Connected Deep Networks (FCDNs). PRIviLY avoids the use of encryption for privacy preservation of sensitive information, in order to fully prevent accuracy loss, and to alleviate the server-side hardware requirements. Besides, PRIviLY adopts a non-colluding two-server architecture, and leverages the linear computations of FCDNs along with reversible random perturbation and permutation techniques in order to preserve privacy of sensitive information, while meeting low overhead requirement of constrained client-sides. At the cloud server, efficiency evaluation shows that PRIviLY achieves an improvement ratio of 4 to more than 15 times for communication, and a minimum improvement ratio of 135 times for computation overhead. At the intermediate server, the minimum improvement ratio is at least more than 10,900 for computation, while for communication, the improvement ratio varies from 5 to more than 21 times. As for the client-side, PRIviLY incurs an additional overhead of about 27% in terms of communication, and between 16% and at most 27% in terms of computation.

High-Linearity Flexible Pressure Sensor Based on the Gaussian-Curve-Shaped Microstructure for Human Physiological Signal Monitoring.

Flexible pressure sensors with high-performance show broad application prospects in health monitoring, wearable electronic devices, intelligent robot sensing, and other fields. Although flexible pressure sensors have made significant progress in sensitivity and detection range, most of them still exhibit strong nonlinearity, which leads to significant troubles in signal acquisition and thus limits their popularity in practical applications. It remains a serious challenge for the flexible pressure sensor to achieve high linearity while maintaining high sensitivity. Herein, a doped sensing membrane with a uniformly distributed Gaussian-curve-shaped micropattern array was developed using the micro-electromechanical systems (MEMS) process, and a flexible sensor structure with the doped film as the core was designed and constructed. The prototype sensor has a high sensitivity of 1.77 kPa-1 and a linearity of 0.99 in the full detection range of 20 Pa to 30 kPa. In addition, its excellent performance also includes fast response/recovery times (∼25/50 ms) and long-term endurance (>10,000 cycles at 15 kPa). The prototype sensor has been successfully demonstrated in human pulse monitoring, speech recognition, and gesture recognition. The 2 × 6 sensor array can detect the spatial pressure distribution. Thus, such a microstructure shape design will open a new way to fabricate a high-linearity pressure sensor for potential applications in health monitoring, human-machine interaction, etc.