Health Monitoring Technology Research Articles

Factors Affecting Clinician Readiness to Adopt Smart Home Technology for Remote Health Monitoring: A Systematic Review.

Background: The population of older adults across the world continues to increase, placing higher demands on primary health care and long-term care. The costs of housing older people in care facilities have economic and societal impacts which are unsustainable without innovative solutions. Many older people wish to remain independent in their homes and age-in-place. Assistive technology such as health-assistive smart homes with clinician monitoring could be a widely adopted alternative to aged care facilities in the future. Whilst studies have found that older persons have demonstrated a readiness to adopt health-assistive smart homes, little is known about clinician readiness to adopt this technology to support older adults to age as independently as possible. Objective: The purpose of this systematic review was to identify the factors that affect clinician readiness to adopt smart home technology for remote health monitoring. Methods: The review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO, CRD42020195989) prior to the commencement of the database searches. This review was conducted in accordance with the Joanna Briggs Institute Methodology for Systematic Reviews and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for reporting. Results: Several factors affected clinicians' perspectives on their readiness to adopt smart home technology for remote health monitoring including challenges such as patient privacy and dignity, data security, and ethical use of 'invasive' technologies. Perceived benefits included enhancing the quality of care and outcomes. Conclusion: Clinicians including nurses reported both challenges and benefits to adopt smart home technology for remote health monitoring. Clear strategies and frameworks to allay fears and overcome professional concerns and misconceptions form key parts of the Readiness to Adoption Pathway proposed. The use of more rigorous scientific methods and reporting is needed to advance the state of the science. The review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO, CRD42020195989) prior to the commencement of the database searches.

Wearable Technology for Health Monitoring and Diagnostics

Purpose: The general objective of this study was to investigate wearable technology for health monitoring and diagnostics. Methodology: The study adopted a desktop research methodology. Desk research refers to secondary data or that which can be collected without fieldwork. Desk research is basically involved in collecting data from existing resources hence it is often considered a low cost technique as compared to field research, as the main cost is involved in executive’s time, telephone charges and directories. Thus, the study relied on already published studies, reports and statistics. This secondary data was easily accessed through the online journals and library. Findings: The findings reveal that there exists a contextual and methodological gap relating to wearable technology for health monitoring and diagnostics. The advancement of wearable technology in health monitoring and diagnostics transformed the healthcare landscape by enabling continuous, real-time data collection and analysis, empowering individuals to manage their health proactively. Despite its benefits, challenges such as data privacy, device accuracy, and user adherence needed addressing. Ensuring robust data protection, validating device accuracy in diverse environments, and understanding barriers to sustained use were essential. Addressing the digital divide was also vital for equitable access. Overall, wearable technology held significant promise for preventive care and early diagnosis, but required ongoing research and collaboration to maximize its impact. Unique Contribution to Theory, Practice and Policy: The Technology Acceptance Model (TAM), Health Belief Model (HBM) and Unified Theory of Acceptance and Use of Technology (UTAUT) may be used to anchor future studies on wearable technology for health monitoring and diagnostics. The study recommended integrating technology acceptance and health behavior theories to provide a comprehensive understanding of adoption factors, emphasizing user-centered design for enhanced engagement, and advocating for stringent data privacy and security standards. It highlighted the importance of integrating wearable technology into healthcare systems for better clinical decisions, promoting equitable access, and using wearables in public health initiatives. The study also called for collaboration between technology developers, healthcare providers, and policymakers to address challenges and maximize the benefits of wearable health technology.

Fiber bragg grating accelerometer based on asymmetric shaped flexible hinge structure

To achieve real-time health monitoring in civil engineering, a FBG acceleration sensor with asymmetric shaped flexible hinge structure is proposed. Combined with the theoretical analysis, the expressions of resonance frequency and sensitivity are deduced, and the effects of the structural parameters of the sensor on the resonance frequency and sensitivity are analyzed and simulated. Based on the optimization results, a fiber Bragg grating acceleration sensor was designed and fabricated. The amplitude-frequency response, linear sensitivity response, and lateral anti-interference ability were tested experimentally. The experimental data shows a sensor resonance frequency of 350 Hz, a sensitivity of 90.96 pm/g, and a linear correlation of 99.59%. These results closely align with the theoretical predictions of a resonance frequency of 344 Hz, and a sensitivity of 98 pm/g, confirming the accuracy of the sensor's theoretical analysis.The transverse interference immunity of the sensor is 3.7%, and an operating frequency band of 10–250 Hz. It indicates that the sensor is promising for medium and low frequency vibration measurement.

Review of AI in Civil Engineering

This paper reviews the transformative impact of Artificial Intelligence (AI) on civil engineering. It explores AI's fundamental concepts and its applications across structural analysis, construction management, transportation, geotechnical engineering, and sustainability. The review highlights AI's role in automating tasks, predicting outcomes, and optimizing designs throughout project lifecycles. Recent advancements in AI-driven technologies for structural health monitoring, predictive maintenance, and risk assessment are discussed, along with challenges like data quality and model interpretability. Future trends such as autonomous construction and digital twins are examined, emphasizing the need for continued research and interdisciplinary collaboration. In conclusion, this paper offers insights for leveraging AI to address evolving challenges and opportunities in civil engineering, fostering innovation, sustainability, and resilience in infrastructure development.

High‐performance resistive/capacitive pressure sensor applied on smart insoles detecting abnormal activity

AbstractThis paper presents an approach to pressure sensors with dual‐function resistors and capacitors implemented with interdigitated capacitors fabricated on a flexible substrate for detecting abnormal actions during walking. In this study, we proposed a highly sensitive, broad‐range pressure sensor achieved through a combination of porous Ecoflex, carbon nanotubes (CNTs), coating single‐wall carbon nanotubes (SWCNTs), and interdigitated electrodes. First, characterizations of the capacitor and resistor sensor applied onto cotton fabric are completed by precision LCR meter across the frequency at 50 kHz. Subsequently, the presence of volume fraction CNTs enhances the bond strength of composites, and coating SWCNT improves sensor sensitivity. The robustness of our presented sensor is validated through testing under high pressure (50 kPa) for more than 1000 cycles. Furthermore, the combination of CNTs and porous dielectric, along with SWCNT coating, achieves a broad detection range (500 kPa) with a sensitivity range from 0.018 (at 500 kPa) to 0.15 (at 5 kPa). Finally, our high‐performance resistive/capacitive pressure sensor applied on smart insoles represents a significant advancement in wearable technology for health monitoring and safety applications. Leveraging an advanced autoencoder, our proposed sensor accurately detects abnormal activity patterns, such as sudden stops or irregular gait, thereby alerting users to potential safety concerns. Its ability to detect abnormal activity patterns enhances user safety and well‐being, making it a tool for various healthcare and fitness applications.

One-Step process of wrinkle microstructured iontronic pressure sensor with all fabric wearable electrode

The advancement of pressure sensors featuring high sensitivity, wide detection ranges, and linear response characteristics is important for advancing tactile sensing technologies for robots and human health monitoring. However, conventional preparation suffers from nonlinear signal output, non-environmental friendliness, and poor comfort, which affects the sensor’s ability to accurately detect different pressure levels, which is critical for subtle haptic feedback systems. To overcome these challenges, our research introduces a breakthrough in flexible capacitive pressure sensors by ingeniously integrating thermal stress-induced microstructured ionic gels with textile electrodes within a band-aid-like form factor. This method prevents the requirement for external templates or intricate manufacturing procedures, streamlining the production process while enhancing the sensor’s performance. The pivotal role of the ionic gel lies in its ability to form an efficient electric double layer (EDL), which adeptly transforms ion–electron interface interactions into measurable pressure-sensing outputs. Notably, this band-aid-based iontronic sensor showcases exceptional performance metrics. It exhibits an extraordinarily high sensitivity, reaching 5652.41 kPa−1, and demonstrates a broad pressure detection range (from 0-400 kPa). Additionally, the sensor responds rapidly to pressure changes, maintains robust long-term cycle stability, and boasts an impressively low detection threshold. Its potential applications, particularly in the precise monitoring of human muscle movements, open exciting new avenues for both health monitoring and the broader field of flexible electronic devices. This showcases the potential of everyday materials in advanced technological applications.

Recent Innovations in Footwear and the Role of Smart Footwear in Healthcare-A Survey.

Smart shoes have ushered in a new era of personalised health monitoring and assistive technologies. Smart shoes leverage technologies such as Bluetooth for data collection and wireless transmission, and incorporate features such as GPS tracking, obstacle detection, and fitness tracking. As the 2010s unfolded, the smart shoe landscape diversified and advanced rapidly, driven by sensor technology enhancements and smartphones' ubiquity. Shoes have begun incorporating accelerometers, gyroscopes, and pressure sensors, significantly improving the accuracy of data collection and enabling functionalities such as gait analysis. The healthcare sector has recognised the potential of smart shoes, leading to innovations such as shoes designed to monitor diabetic foot ulcers, track rehabilitation progress, and detect falls among older people, thus expanding their application beyond fitness into medical monitoring. This article provides an overview of the current state of smart shoe technology, highlighting the integration of advanced sensors for health monitoring, energy harvesting, assistive features for the visually impaired, and deep learning for data analysis. This study discusses the potential of smart footwear in medical applications, particularly for patients with diabetes, and the ongoing research in this field. Current footwear challenges are also discussed, including complex construction, poor fit, comfort, and high cost.

Advancing Structural Health Monitoring in Civil Engineering with Greybox Modelling: A Review

This review explores the application of Greybox Modelling, specifically Physics-Informed Machine Learning (PIML), in the context of Structural Health Monitoring (SHM) within civil engineering. SHM is vital for ensuring the safety and durability of infrastructure namely bridges. Greybox Modelling, merging physics-based principles with data-driven techniques, offers a functional approach for understanding complex structural behaviors. By leveraging existing knowledge of physical laws, this method extracts valuable insights from sensor data. The integration of Greybox Modelling takes SHM a step further. By applying machine learning algorithms with the physics that govern civil engineering structures, Greybox Modelling bridges the gap between data-driven models and physics-based models. This leads more accurate predictions and interpretable outcomes. This review discusses findings from literature, recent studies, that illustrate the effectiveness of Greybox Modelling, specifically PIML in various civil engineering applications. These case studies highlight how this approach can adapt to different structural designs and environmental conditions typically encountered in civil engineering. Moreover, they indicate its potential for damage detection and early warning systems, which enhance the safety and efficiency of infrastructure. The paper also addresses current challenges and outlining future directions in the field.

On-line Updated Guided Wave-Based Gaussian Process Method for Damage Evaluation of Aircraft Structure

Accurate damage evaluation for an aircraft by using guided wave-based structural health monitoring (SHM) technology is in critical demand. An aircraft experiences various uncertainties during its service life, such as the time-varying environment, operational conditions, and different flight missions. Additionally, the actual aircraft structure involves complex structural forms such as varied thickness, stiffeners, ribs, and large cutouts, leading to a strong uncertainty of damage initiation and propagation. Consequently, when applied to an individual aircraft structure, the diagnosis model trained by prior data inevitably introduces errors, and the errors accumulate as the damage propagates, which brings challenges in engineering application. To achieve individual aircraft structure’s more accurate damage evaluation, an on-line updated guided wave-based Gaussian process (GP) damage evaluation method is investigated in this paper. Multi-source data from the design, service, and maintenance stages are utilized to continuously update and evolve the GP model, enabling its accurate evaluation throughout the long-term service life.

The damage localization method in the composite panel by multifrequency guided wave signal using parallel network

Fiber-reinforced composite materials find extensive application in the realm of aeronautics; nonetheless, during operational phases, structural integrity may be compromised due to external impacts or extreme environmental conditions. Consequently, the imperative is the necessity of vigilantly monitoring the structural state. The structural health monitoring (SHM) technology based on ultrasonic guided waves can achieve long-distance and wide-ranging damage detection utilizing a minimal number of cost-effective piezoelectric transducers. However, traditional damage identification methods necessitate the analysis of the multi-modal characteristics of guided waves and the deployment of dense sensor arrays. In this paper, we propose a network model that leverages multi-frequency guided-wave signals to achieve spatiotemporal parallelism, incorporating residual blocks and convolutional attention mechanisms (GCRA). This model accurately estimates damage locations with an $R^2$ value of 0.9147. The average error for the six randomly selected damage locations is 0.4275 cm. The results demonstrate that the GCRA facilitates the identification of damaged positions with a high degree of confidence.

POD methodologies for SHM application in civil engineering

Structural Health Monitoring (SHM) technologies can significantly enhance safety through continuous monitoring. The successful deployment of real-time SHM systems rely mainly on reliability evaluations. While numerous standards exist for reliability assessments or Probability of Detection (POD) of NDT techniques, there are no specific standards guiding reliability procedures for SHM data. Consequently, NDT POD methods are often applied to SHM data, despite the time and statistically dependent nature of SHM data. Especially in the field of civil engineering, there is the need for a SHM specific POD approach able to perform a meaningful reliability evaluation. Hence, in the current work, different SHM POD models including random intercept model and length at detection (LaD) model are implemented for a vibration-based monitoring data relevant for civil engineering. Unlike the linear response observed in NDT inspection data concerning damage, SHM data can exhibit non-linear characteristics in certain cases. In those cases, an advanced LaD model suitable for non-linear data is also developed in the current study. In addition, a comparison between the results obtained using NDT POD models and SHM POD models is demonstrated using an open-source GUI (TU1402) based on FE model. Moreover, the results obtained by the application of modified LaD POD model to a real test bridge data is demonstrated. Keywords: SHM, reliability, POD, civil engineering.

Viscoelastic Adhesive, Super-Conformable, and Semi-Flowable Liquid Metal Eutectogels for High-Fidelity Electrophysiological Monitoring.

Integrating gels with human skin through wearables provides unprecedented opportunities for health monitoring technology and artificial intelligence. However, most conductive hydrogels, organogels, and ionogels lack essential environmental stability, biocompatibility, and adhesion for reliable epidermal sensing. In this study, we have developed a liquid metal eutectogel simultaneously possessing superior viscoelasticity, semiflowability, and mechanical rigidity for low interfacial skin impedance, high skin adhesion, and durability. Liquid metal particles (LMPs) are employed to generate free radicals and gallium ions to accelerate the polymerization of acrylic acid monomers in a deep eutectic solvent (DES), obtaining highly viscoelastic polymer networks via physical cross-linking. In particular, graphene oxide (GO) is utilized to encapsulate the LMPs through a sonication-assisted electrostatic assembly to stabilize the LMPs in DES, which also enhances the mechanical toughness and regulates the rheological properties of the eutectogels. Our optimized semi-flowable eutectogel exhibits viscous fluid behavior at low shear rates, facilitating a highly conformable interface with hairy skin. Simultaneously, it demonstrates viscoelastic behavior at high shear rates, allowing for easy peel-off. These distinctive attributes enable the successful applications of on-skin adhesive strain sensing and high-fidelity human electrophysiological (EP) monitoring, showcasing the versatility of these ionically conductive liquid metal eutectogels in advanced personal health monitoring.

Review and Prospect of Research on Structural Health Monitoring Technology for Bridges

As a crucial infrastructure in the transport system, the safe operation of bridges is directly related to all aspects of people’s daily lives. The development of bridge structural health monitoring technology and its application play an important role in ensuring the safety and extending the service life of bridges. This paper carries out in-depth research and analysis on the related technology of bridge structural health monitoring. Firstly, the existing monitoring technologies at home and abroad are sorted out, and the advantages and problems of various methods are compared and analyzed, including nondestructive testing, stress measurement, vibration characteristic identification, and other commonly used monitoring technologies. Secondly, the key technologies and equipment in the bridge health monitoring system, such as sensor technology, data acquisition, and processing technology, are introduced in detail. Finally, the development trend in the field of bridge health monitoring is prospected from both theoretical research and technical application. In the future, with the development of emerging technologies such as big data, cloud computing, and the Internet of Things, it is expected that bridge health monitoring with intelligent and systematic features will be more widely applied to provide a stronger guarantee for the safe and efficient operation of bridges.

Broiler health monitoring technology based on sound features and random forest

The existing broiler health monitoring technology has problems such as low automation, unstable monitoring results, and low practical value, making it difficult to provide timely and reliable broiler health monitoring results. The broiler sound signal can provide feedback on their health. A widely validated and correct experience is to analyze the frequency of coughs in a segment of broiler sound signal to determine the health of the broiler group. Based on this, in this paper, the authors proposed a new broiler health monitoring technology based on sound detection. The broiler health monitoring problem is cleverly transformed into a multi-classification problem, which can be solved by identifying the sound types in broiler sound signals. Specifically, the audio signal collection system was designed to complete signal collection and preliminary signal filtering. Wiener filtering was used for deep signal filtering. The 60-dimensional sound features with good performance from three aspects, time-frequency domain, Mel-Frequency Cepstral Coefficients, and sparse representation were extracted, and a preliminary data set was created. Min-max normalization was used to align the numerical distribution of the data set, and a high-quality data set was created. Multi-classification models based on different classification algorithms and neural networks were trained, and the best-performing Random Forest was obtained, thus parameter optimization was carried out, and the optimal multi-classification model was obtained, achieving a classification accuracy of 91.14%. The visualization platform was built to process the classification results of the multi-classification model, completing majority voting processing and cough rate calculation, thereby achieving broiler health monitoring. In addition, the definitions of cough rate and prediction accuracy were newly proposed. A large number of experiments have verified the feasibility of the broiler health monitoring technology proposed in this paper, with an average prediction accuracy of 98.97% achieved.

A machine learning-based approach for constructing remote photoplethysmogram signals from video cameras

BackgroundAdvancements in health monitoring technologies are increasingly relying on capturing heart signals from video, a method known as remote photoplethysmography (rPPG). This study aims to enhance the accuracy of rPPG signals using a novel computer technique.MethodsWe developed a machine-learning model to improve the clarity and accuracy of rPPG signals by comparing them with traditional photoplethysmogram (PPG) signals from sensors. The model was evaluated across various datasets and under different conditions, such as rest and movement. Evaluation metrics, including dynamic time warping (to assess timing alignment between rPPG and PPG) and correlation coefficients (to measure the linear association between rPPG and PPG), provided a robust framework for validating the effectiveness of our model in capturing and replicating physiological signals from videos accurately.ResultsOur method showed significant improvements in the accuracy of heart signals captured from video, as evidenced by dynamic time warping and correlation coefficients. The model performed exceptionally well, demonstrating its effectiveness in achieving accuracy comparable to direct-contact heart signal measurements.ConclusionsThis study introduces a novel and effective machine-learning approach for improving the detection of heart signals from video. The results demonstrate the flexibility of our method across various scenarios and its potential to enhance the accuracy of health monitoring applications, making it a promising tool for remote healthcare.

Implantable sensing technology for civil engineering structures

Providing crack location and stiffness variation information using practical structural health monitoring technology is highly significant for in-service structures. Inspired by the fast development of implantable devices, this paper introduced an implantable sensing technology (IST) for health monitoring of a typical concrete member known as shear wall undergoing loading progression. Two collaborative monitoring steps were developed to acquire targeted damage information at different loading stages, the one was, a cylindrical concrete implantable module (CCIM) sensor-enabled IST was applied for monitoring and imaging the early cracks of the shear wall at the initial loading stage; and the other was, a concrete implantable bar (CIB) sensor-enabled IST was employed for monitoring and assessing the damage evolution at the mid-late loading stage. Experimental results showed that these two collaborative ISTs can accurately identify the early cracking regions of the shear wall and reveal its stiffness degradation processes throughout the loading.

25b - Wearable technologies for occupational health and exposure monitoring

25b - Wearable technologies for occupational health and exposure monitoring

A scientometric analysis of drone-based structural health monitoring and new technologies

Critical global challenges, such as climate change and the insufficient availability of resources, mean that it is a pivotal time to make cities more intelligent, efficient, and sustainable in a drive towards a net-zero carbon future. This requires intelligent, interactive, and responsive structural health monitoring (SHM) to assure the longevity and safety of ageing infrastructure. Drones have the potential to revolutionise SHM. Drone-based SHM (as a potential fly-by technique) involves equipping drones with various sensors, or using inbuilt sensors, to capture data and images of structures from different angles and perspectives. The data is then processed and analysed to facilitate accurate assessment of the structure’s health and early diagnosis of damage. Although the use of fly-by is relatively new, the speedy advances in various technologies that could be integrated with it, such as computer vision with artificial intelligence, deep learning, and links to digital twins, put these systems on the verge of a potential breakthrough. This paper provides an overview of fly-by SHM technique using both scientometric and qualitative systematic literature review processes, in order to provide a distinct understanding of the state of the art of research. As an original contribution, our research identified four main clusters of research within the field of fly-by SHM: (1) the application of UAV-enabled vision-based monitoring; (2) the integration of drones, advanced sensor technologies, and artificial intelligence; (3) drone-based SHM integrating modal analysis, energy harvesting, and deep learning; and (4) automation and robotics in drone-based SHM. The paper highlights the integration of new technologies such as artificial intelligence, machine learning, and sensors with the fly-by technique for SHM, identifies the gaps in current fly-by SHM research, and suggests new directions for research.

Exploring the Potential of Promising Sensor Technologies for Concrete Structural Health Monitoring.

Structural health monitoring (SHM) is crucial for maintaining concrete infrastructure. The data collected by these sensors are processed and analyzed using various analysis tools under different loadings and exposure to external conditions. Sensor-based investigation on concrete has been carried out for technologies used for designing structural health monitoring sensors. A Sensor-Infused Structural Analysis such as interfacial bond-slip model, corroded steel bar, fiber-optic sensors, carbon black and polypropylene fiber, concrete cracks, concrete carbonation, strain transfer model, and vibrational-based monitor. The compressive strength (CS) and split tensile strength (STS) values of the analyzed material fall within a range from 26 to 36 MPa and from 2 to 3 MPa, respectively. The material being studied has a range of flexural strength (FS) and density values that fall between 4.5 and 7 MPa and between 2250 and 2550 kg/m3. The average squared difference between the predicted and actual compressive strength values was found to be 4.405. With cement ratios of 0.3, 0.4, and 0.5, the shear strength value ranged from 4.4 to 5.6 MPa. The maximum shear strength was observed for a water-cement ratio of 0.4, with 5.5 MPa, followed by a water-cement ratio of 0.3, with 5 MPa. Optimizing the water-cement ratio achieves robust concrete (at 0.50), while a lower ratio may hinder strength (at 0.30). PZT sensors and stress-wave measurements aid in the precise structural monitoring, enhanced by steel fibers and carbon black, for improved sensitivity and mechanical properties. These findings incorporate a wide range of applications, including crack detection; strain and deformation analysis; and monitoring of temperature, moisture, and corrosion. This review pioneers sensor technology for concrete monitoring (Goal 9), urban safety (Goal 11), climate resilience (Goal 13), coastal preservation (Goal 14), and habitat protection (Goal 15) of the United Nations' Sustainable Development Goals.

Privacy Barriers in Health Monitoring: Scoping Review.

Health monitoring technologies help patients and older adults live better and stay longer in their own homes. However, there are many factors influencing their adoption of these technologies. Privacy is one of them. The aim of this study was to provide an overview of the privacy barriers in health monitoring from current research, analyze the factors that influence patients to adopt assisted living technologies, provide a social psychological explanation, and propose suggestions for mitigating these barriers in future research. A scoping review was conducted, and web-based literature databases were searched for published studies to explore the available research on privacy barriers in a health monitoring environment. In total, 65 articles met the inclusion criteria and were selected and analyzed. Contradictory findings and results were found in some of the included articles. We analyzed the contradictory findings and provided possible explanations for current barriers, such as demographic differences, information asymmetry, researchers' conceptual confusion, inducible experiment design and its psychological impacts on participants, researchers' confirmation bias, and a lack of distinction among different user roles. We found that few exploratory studies have been conducted so far to collect privacy-related legal norms in a health monitoring environment. Four research questions related to privacy barriers were raised, and an attempt was made to provide answers. This review highlights the problems of some research, summarizes patients' privacy concerns and legal concerns from the studies conducted, and lists the factors that should be considered when gathering and analyzing people's privacy attitudes.