Real-time Health Research Articles

Fabrication of Nanobioengineered Interfaces Utilizing Quaternary Nanocomposite for Highly Efficient and Selective Electrochemical Biosensing of Urea.

Nanobioengineered interfaces have gained attention owing to their small size and high surface area-to-volume ratio for utilization as a platform for highly selective and sensitive biosensing applications owing to the integration of biological molecules with engineered nanomaterials/nanocomposites. In this work, a novel Ag-complex, [(PPh3)2Ag(SCOf)]-based quaternary Ag-S-Zn-O nanocomposites (NCs), was synthesized through an environmentally-friendly process. The results revealed the formation of the NCs with an average crystallite size and particle size of 36.08 and 40.22 nm, respectively. In addition, this is the first study to utilize such NCs synthesized via a single-source precursor method, offering enhanced sensor performance due to their unique structural properties. Further, these NCs were used to fabricate a urease (Ur)/Ag-S-Zn-O NCs/ITO nanobioengineered electrode for precise and sensitive electrochemical biosensing of urea. The interfacial kinetic studies revealed quasi-reversible processes with high electron transfer rates and linear current responses, indicating efficient reaction dynamics. A high diffusion coefficient and low surface concentration suggested a fast diffusion-controlled process, affirming the electrode's potential for rapid and sensitive urea detection. The biosensor demonstrated notable sensing properties such as high sensitivity (12.56 μA mM-1 cm-2) and a low detection limit (0.54 mM). The fabricated bioelectrode was highly selective and reproducible and demonstrated stability for up to 60 days. These results validate the potential of this nanobioengineered interface for next-generation biosensing applications, paving the way for advanced point-of-care diagnostics and real-time health monitoring.

The Impact of Wearable Devices on Public Health Outcomes in The Treatment of Chronic Diseases Through Continuous Physiological Monitoring

Chronic illnesses contribute to elevated levels of disability and death. The active involvement of patients in the treatment of chronic diseases is a crucial element of healthcare systems that are focused on chronic diseases. Wearable devices provide real-time health information focused on the patient, enabling them to make informed decisions about self-management. Although wearables are believed to offer advantages in enhancing the self-management of chronic illnesses, their impact on healthcare outcomes still needs to be well comprehended. This study sought to investigate the effect of wearables on healthcare results in adults with chronic conditions by conducting a comprehensive analysis of existing evidence for physiological monitoring. A narrative systematic literature review was performed by searching six databases for randomized and observational research published from January 2018 to July 2023. These studies focused on utilizing a wearable intervention in a group of individuals with chronic diseases to evaluate its effect on a predetermined end measure. The outcomes were defined as any impact on patient or practitioner experience, cost-effectiveness, or healthcare outcomes resulting from the wearable intervention. The findings from the research included in the analysis were gathered according to 6 main themes, which were used as the foundation for a qualitative summary. This research adhered to the requirements outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) declaration.

Toward the Internet of Medical Things for Real-Time Health Monitoring Over Wi-Fi

Toward the Internet of Medical Things for Real-Time Health Monitoring Over Wi-Fi

MEDROBO: Automated Medicine Delivery and Patient Monitoring System

The research aims to develop an innovative solution to automate the delivery of medicine to patients and monitor their vital parameters in healthcare facilities. Traditional methods often rely on human interventions for medicine delivery and patient monitoring, leading to inefficiencies and potential errors. To address these challenges, the proposed study introduces a robotic system capable of line-following method and real-time health parameter monitoring. The core functionality of the system revolves around a robot equipped with various sensors. The robot navigates hospital corridors by following predefined lines, allowing it to reach patients’ rooms without human assistance. RFID tags attached to patient beds facilitate the robot in identifying and locating specific patients, ensuring accurate medicine delivery. Furthermore, the robot integrates vital sign monitoring capabilities, including heart rate, Spo2, blood pressure, and temperature.

Efficacy of PZT Sensors Network Different Configurations in Damage Detection of Fiber-Reinforced Concrete Prisms under Repeated Loading.

Real-time structural health monitoring (SHM) and accurate diagnosis of imminent damage are critical to ensure the structural safety of conventional reinforced concrete (RC) and fiber-reinforced concrete (FRC) structures. Implementations of a piezoelectric lead zirconate titanate (PZT) sensor network in the critical areas of structural members can identify the damage level. This study uses a recently developed PZT-enabled Electro-Mechanical Impedance (EMI)-based, real-time, wireless, and portable SHM and damage detection system in prismatic specimens subjected to flexural repeated loading plain concrete (PC) and FRC. Furthermore, this research examined the efficacy of the proposed SHM methodology for FRC cracking identification of the specimens at various loading levels with different sensor layouts. Additionally, damage quantification using values of statistical damage indices is included. For this reason, the well-known conventional static metric of the Root Mean Square Deviation (RMSD) and the Mean Absolute Percentage Deviation (MAPD) were used and compared. This paper addresses a reliable monitoring experimental methodology in FRC to diagnose damage and predict the forthcoming flexural failure at early damage stages, such as at the onset of cracking. Test results indicated that damage assessment is successfully achieved using RMSD and MAPD indices of a strategically placed network of PZT sensors. Furthermore, the Upper Control Limit (UCL) index was adopted as a threshold for further sifting the scalar damage indices. Additionally, the proposed PZT-enable SHM method for prompt damage level is first established, providing the relationship between the voltage frequency response of the 32 PZT sensors and the crack propagation of the FRC prisms due to the step-by-step increased imposed load. In conclusion, damage diagnosis through continuous monitoring of PZTs responses of FRC due to flexural loading is a quantitative, reliable, and promising application.

A probabilistic machine learning framework for stiffness tensor estimation of carbon composite laminate

The potential uses of carbon composite material are vast, particularly in the civil, mechanical and aero-structures. Nonetheless, the practical utilization of carbon composite faces challenges due to complex experimental methods and imprecise algorithms for inverting material properties. Characterization of full set of stiffness tensor for an anisotropic material in particular orthotropic material can be considered as a complex non-linear inversion problem. The inversion process requires a robust optimization algorithm and forward models. The complexity of this inversion process impedes the practical utility in large-scale automation and in real-time structural health monitoring (SHM) application. To cater this, in this work an advanced probabilistic machine learning framework based on multi-output Gaussian process regression (moGPR) is proposed as an inversion algorithm. The inversion algorithm proposed is based on probabilistic framework that utilises the dispersion of Lamb waves as an input for optimal estimation of stiffness tensor. Experimental measurements of full wavefield data of propagating waves are conducted by scanning laser Doppler vibrometer. Further, an optimal training dataset is generated by employing a forward model based on stiffness matrix method (SMM). In the presence of measurement uncertainties, the effectiveness of the optimal prediction algorithm is showcased through a comparison between the estimated parameter and its ground truth. This comparison reveals that the proposed inversion algorithm is both efficient and robust, delivering satisfactory performance even in the presence of substantial measurement uncertainties.

Leveraging Zero-Effort Technology for Quantification of Sleep Quality Metrics.

Sleep quality is a critical factor in human health and well-being, with implications for various physiological and psychological processes. Traditional methods of sleep data collection are often limited by the quality and reliability of the data due to issues such as recall bias and subjective interpretation. This research aims to propose a novel framework that objectively measures and evaluates sleep quality using smart thermostats equipped with motion sensors, providing noninvasive and effortless sleep monitoring. The study conducts a comprehensive analysis of sleep patterns, exploring the relationship between activity sensors and sleep quality. By analyzing behavioral characteristics, the study identifies periods or clusters of days that require attention in terms of health and stress levels. The approach ensures privacy, ease of access, and integrates environmental factors, enabling a comprehensive understanding of an individual's sleep health. The findings suggest that this zero-effort technology can significantly enhance sleep monitoring at both individual and population levels, with implications for health monitoring, stress management, and personalized healthcare interventions. Future work will focus on expanding the data set, incorporating more variables, and integrating contextual data to further improve sleep quality analysis and support real-time health interventions.

Co-occurring autism, ADHD, and gender dysphoria in children, adolescents, and young adults with eating disorders: an examination of pre- vs. post-COVID pandemic outbreak trends with real-time electronic health record data.

Incidence rates of autism, attention-deficit/hyperactivity disorder (ADHD), and gender dysphoria (GD) are rising not only in the general population, but particularly among children, adolescents, and young adults with eating disorders (EDs). While ED rates have risen during the COVID pandemic, trends in co-occurring autism, ADHD, and GD have yet to be investigated in detail or at scale by way of large electronic medical record data. To investigate trends in rates of co-occurring autism, ADHD, and GD among children, adolescents, and young adults with EDs in years prior to and during the COVID-19 pandemic. We utilized a de-identified multinational electronic health records database (TriNetX) with 48,558 individuals aged 5-26 diagnosed with eating disorders (EDs) at least twice between 2017 and 2022. The primary predictor variable differentiated between the years of each person's index (first) ED diagnosis (2017-2019 vs. 2020-2022). The primary outcome variable was the rate of new co-occurring psychiatric diagnoses of autism, ADHD, and GD in the year following each patient's first ED diagnosis. We applied propensity score-matched multivariable logistic regressions to compare primary outcomes between 2017-2019 and 2020-2022. Our analysis included 17,445 individuals diagnosed with EDs in 2017-2019 (8% autism, 13.5% ADHD, 1.9% GD) and 31,113 diagnosed with EDs in 2020-2022 (8% autism, 14.6% ADHD, 3.2% GD). After 1:1 propensity score matching, 17,202 individuals from the 2017-2019 cohort were matched to peers mirroring the 2020-2022 cohort. Those diagnosed in 2020-2022 showed a 19% (aOR[95%CI]=1.19[1.07-1.33]), 25% (aOR=1.25[1.04-1.49]), and 36% (aOR=1.36[1.07-1.74]) increase in odds for autism, ADHD, and GD diagnoses, respectively, within the 365 days after the index EDs diagnosis, compared to the 2017-2019 cohort. Rates of autism, ADHD, and GD are significantly higher in individuals with ED in the post-pandemic 2020-2022 cohort in comparison to the pre-pandemic 2017-2019 cohort, even after controlling for baseline levels of co-occurring psychiatric diagnoses. Such findings reveal a critical gap in our current understanding of the totality of ways in which COVID-19 may have impacted the onset and clinical course of EDs, autism, ADHD, and GD among children, adolescents, and young adults.

Simultaneous Detection of Biomarkers in Urine Using a Multicalibration Potentiometric Sensing Array Combined with a Portable Analyzer.

Domestic monitoring devices make real-time and long-term health monitoring possible, allowing people to track their health status regularly. Uric acid (UA), creatinine, and urea in urine are three important biomarkers for various diseases, especially kidney diseases. This work proposed a 10-channel potentiometric sensing array containing a UA electrode group, a creatinine electrode group, a urea electrode group, a pH electrode group, and one pair of reference channels, which could be connected with a portable potentiometric analyzer, realizing the simultaneous detection of UA, creatinine, urea, and pH in urine. The prepared Pt/carbon nanotubes (CNTs)-uricase, creatinine deiminase, Au@urease, and polyaniline were employed as the sensing materials, showing responses to four targets with high sensitivity and selectivity. To improve the accuracy of domestic monitoring, a calibration channel was integrated into each electrode group to calibrate the basic potential of the sensing channels, and the influences of pH and temperature on the responses were investigated through the pH electrode group and an external temperature probe to calibrate the slope and intercept. With the preset of the deduced calibration parameters and computational formula for the four targets in the analyzer in Lab Mode, the concentrations of UA, creatinine, and urea and the pH of the human urine samples were directly displayed on the screen of the analyzer in Practical Mode. The agreement of these results with those obtained from commercial kits and pH meters reveals the high potential of these methods for developing domestic devices to facilitate health monitoring.

Integrating electro-mechanical impedance data with machine learning for damage detection and classification of blended concrete systems

In recent years, the assessment of structural integrity and durability of concrete systems has significantly advanced with the integration of novel sensing technologies and data-driven techniques. This study introduces an innovative approach that utilizes electro-mechanical impedance (EMI) data acquired through embedded piezo sensors (EPS) to monitor and analyze the condition of blended concrete systems. Moreover, the study involves blended concrete specimens are subjected to controlled damage scenarios under mechanical loadings to establish EPS as an early warning sensor for detecting damage in these systems. Furthermore, the extent of damage under these loadings is comprehensively evaluated using statistical indices and an equivalent stiffness parameter. Additionally, the study focuses on the application of machine learning (ML) algorithms to interpret EMI data for efficient damage classification. ML models, such as decision trees (DT), random forests (RF), k-nearest neighbors (KNN), support vector machines (SVM), and naive Bayes (NB) are trained to identify patterns correlating with different types and extents of structural damage. Among them, the RF classifier exhibits the highest accuracy of 0.91, followed by DT, KNN, SVM, and NB with accuracies of 0.88, 0.86, 0.68, and 0.17 respectively. The integration of EMI data with ML approaches demonstrates significant potential to revolutionize predictive maintenance strategies, enabling precise classification of structural damage and providing critical insights for engineers and stakeholders to proactively address issues, thereby significantly enhancing the safety, durability, and lifespan of concrete infrastructure. This study not only extends the current understanding of EMI data applications but also opens new paths for automated and real-time structural health monitoring.

Otolaryngic Allergy Patient Journey Mapping: A Framework for Allergy Immunotherapy Adherence.

Allergen-specific immunotherapy (AIT) is an effective treatment for allergic disease but requires long treatment duration and premature cessation is of significant concern. Drivers of premature cessation remain poorly understood and no predictive models currently exist. We hypothesized that a novel patient journey map and de novo real-time patient electronic health status instruments (eHSIs) could effectively capture patient perceived cost, commitment, and treatment benefit to identify individual patients at risk for premature AIT cessation. Cross-Sectional Observational Study. A single Otolaryngology allergy immunotherapy (AIT) program was studied over 5 years. Instances of premature cessation were classified. An Otolaryngic Allergy Patient Journey Map was developed to identify and target automated, real-time, patient-reported, electronic health status instrument responses. Data capture was robust, with 61,406 data points collected and an eHSI survey completion rate of 81.3%. However, based on correlation analysis and logistic regression alone, real-time eHSI responses were not predictive of individual patient premature AIT cessation. A total of 597 AIT patients discontinued treatment prematurely: 64.4% stopping within the first year. Specifically, 74.0%-76.3% of subcutaneous AIT patients and 88.5%-100% of sublingual AIT patients did not complete the minimum recommended treatment duration of 3 years. Patient journey mapping can aid in the design of longitudinal care models and patient engagement strategies. Yet, eHSI patient responses of perception of AIT cost, benefit, and convenience did not correlate with the likelihood of premature treatment cessation. Our imperfect clinical intuition may not account for the dynamic drivers of premature AIT discontinuation. Future development of predictive tools feed by large patient-centric data sets may be incorporated into routine practice resulting in delivery of a more streamlined and personalized approach with reduced premature AIT cessation, improved outcomes, and reduced health care expenditures. NA Laryngoscope, 2024.

Artificial intelligence enabled biodegradable all-textile sensor for smart monitoring and recognition

Artificial intelligence (AI) enabled electronic textiles inherit the advantages of traditional textiles, such as softness, flexibility, and wearable convenience, and demonstrate significant potential for wearable applications. However, the existence of metallic electrodes and polymer thin films sensitive/encapsulating layers in current textile- or fiber-based pressure sensors significantly reduces the unique advantages of textiles, particularly in terms of renewability and biodegradability. Here, an all-textile biodegradable AI enabled pressure sensor is demonstrated using tunable conductivity cotton as the electrode/sensitive layer, incorporating real-time deep learning-based data analysis. The metal electrode and polymer thin film widely adopted in smart textile-based pressure sensors are avoided, and the all-textile components allow the sensors to be freely cut and reassembled like Lego. Based on these Lego-like smart textiles, intelligence applications such as real-time health monitoring, game control, gait analysis, and authentication systems are demonstrated. After completing the functions, the whole device can be rapidly degraded in the cellulase solution and broken down into reducing sugars, thus effectively reducing the environmental pollution. This work provides a new strategy for creating renewable and sustainable textile pressure sensors with significant application potential in next-generation smart green electronics.

Designing a Hybrid Energy-Efficient Harvesting System for Head- or Wrist-Worn Healthcare Wearable Devices.

Battery power is crucial for wearable devices as it ensures continuous operation, which is critical for real-time health monitoring and emergency alerts. One solution for long-lasting monitoring is energy harvesting systems. Ensuring a consistent energy supply from variable sources for reliable device performance is a major challenge. Additionally, integrating energy harvesting components without compromising the wearability, comfort, and esthetic design of healthcare devices presents a significant bottleneck. Here, we show that with a meticulous design using small and highly efficient photovoltaic (PV) panels, compact thermoelectric (TEG) modules, and two ultra-low-power BQ25504 DC-DC boost converters, the battery life can increase from 9.31 h to over 18 h. The parallel connection of boost converters at two points of the output allows both energy sources to individually achieve maximum power point tracking (MPPT) during battery charging. We found that under specific conditions such as facing the sun for more than two hours, the device became self-powered. Our results demonstrate the long-term and stable performance of the sensor node with an efficiency of 96%. Given the high-power density of solar cells outdoors, a combination of PV and TEG energy can harvest energy quickly and sufficiently from sunlight and body heat. The small form factor of the harvesting system and the environmental conditions of particular occupations such as the oil and gas industry make it suitable for health monitoring wearables worn on the head, face, or wrist region, targeting outdoor workers.

Deduplication-Aware Healthcare Data Distribution in IoMT

As medical sensors undergo expeditious advancements, there is rising interest in the realm of healthcare applications within the Internet of Medical Things (IoMT) because of its broad applicability in monitoring the health of patients. IoMT proves beneficial in monitoring, disease diagnosis, and better treatment recommendations. This emerging technology aggregates real-time patient health data from sensors deployed on their bodies. This data collection mechanism consumes excessive power due to the transmission of data of similar types. It necessitates a deduplication mechanism, but this is complicated by the variable sizes of the data chunks, which may be either very small or larger in size. This reduces the likelihood of efficient chunking and, hence, deduplication. In this study, a deduplication-based data aggregation scheme was presented. It includes a Delimiter-Based Incremental Chunking Algorithm (DICA), which recognizes the breakpoint among two frames. The scheme includes static as well as variable-length windows. The proposed algorithm identifies a variable-length chunk using a terminator that optimizes the windows that are variable in size, with a threshold limit for the window size. To validate the scheme, a simulation was performed by utilizing NS-2.35 with the C language in the Ubuntu operating system. The TCL language was employed to set up networks, as well as for messaging purposes. The results demonstrate that the rise in the number of windows of variable size amounts to 62%, 66.7%, 68%, and 72.1% for DSW, RAM, CWCA, and DICA, respectively. The proposed scheme exhibits superior performance in terms of the probability of the false recognition of breakpoints, the static and dynamic sizes of chunks, the average sizes of chunks, the total attained chunks, and energy utilization.

Continuous Monitoring of Heart Rate Variability in Free-Living Conditions Using Wearable Sensors: Exploratory Observational Study.

Wearable physiological monitoring devices are promising tools for remote monitoring and early detection of potential health changes of interest. The widespread adoption of such an approach across communities and over long periods of time will require an automated data platform for collecting, processing, and analyzing relevant health information. In this study, we explore prospective monitoring of individual health through an automated data collection, metrics extraction, and health anomaly analysis pipeline in free-living conditions over a continuous monitoring period of several months with a focus on viral respiratory infections, such as influenza or COVID-19. A total of 59 participants provided smartwatch data and health symptom and illness reports daily over an 8-month window. Physiological and activity data from photoplethysmography sensors, including high-resolution interbeat interval (IBI) and step counts, were uploaded directly from Garmin Fenix 6 smartwatches and processed automatically in the cloud using a stand-alone, open-source analytical engine. Health risk scores were computed based on a deviation in heart rate and heart rate variability metrics from each individual's activity-matched baseline values, and scores exceeding a predefined threshold were checked for corresponding symptoms or illness reports. Conversely, reports of viral respiratory illnesses in health survey responses were also checked for corresponding changes in health risk scores to qualitatively assess the risk score as an indicator of acute respiratory health anomalies. The median average percentage of sensor data provided per day indicating smartwatch wear compliance was 70%, and survey responses indicating health reporting compliance was 46%. A total of 29 elevated health risk scores were detected, of which 12 (41%) had concurrent survey data and indicated a health symptom or illness. A total of 21 influenza or COVID-19 illnesses were reported by study participants; 9 (43%) of these reports had concurrent smartwatch data, of which 6 (67%) had an increase in health risk score. We demonstrate a protocol for data collection, extraction of heart rate and heart rate variability metrics, and prospective analysis that is compatible with near real-time health assessment using wearable sensors for continuous monitoring. The modular platform for data collection and analysis allows for a choice of different wearable sensors and algorithms. Here, we demonstrate its implementation in the collection of high-fidelity IBI data from Garmin Fenix 6 smartwatches worn by individuals in free-living conditions, and the prospective, near real-time analysis of the data, culminating in the calculation of health risk scores. To our knowledge, this study demonstrates for the first time the feasibility of measuring high-resolution heart IBI and step count using smartwatches in near real time for respiratory illness detection over a long-term monitoring period in free-living conditions.

An MLP-CNN Model for Real-time Health Monitoring and Intervention

The use of Artificial Intelligence (AI) in healthcare, particularly in real-time health monitoring and predictive interventions for chronic diseases, has many benefits but also many drawbacks. Existing health risk prediction algorithms face accuracy issues and, due to the wide variety of health profiles, general algorithm applicability is problematic. The proposed model solves this issue by using an advanced AI framework to improve the accuracy of the prediction of Chronic Kidney Disease (CKD) and eliminate false positives. Our hybrid Deep Learning (DL) method blends a Multi-Layer Perceptron (MLP) and a Convolutional Neural Network (CNN), thus including real-time feedback to help the system learn from its predictions and results. The proposed system solves a major problem in this area and sets a new benchmark for AI applications in healthcare by directly addressing prediction accuracy. It offers more tailored, accurate, and responsive chronic disease management, improving patient outcomes and healthcare resource efficiency.

A Highly Stretchable, Conductive, and Transparent Bioadhesive Hydrogel as a Flexible Sensor for Enhanced Real-Time Human Health Monitoring.

Real-time continuous monitoring of non-cognitive markers is crucial for the early detection and management of chronic conditions. Current diagnostic methods are often invasive and not suitable for at-home monitoring. An elastic, adhesive, and biodegradable hydrogel-based wearable sensor with superior accuracy and durability for monitoring real-time human health is developed. Employing a supramolecular engineering strategy, a pseudo-slide-ring hydrogel is synthesized by combining polyacrylamide (pAAm), β-cyclodextrin (β-CD), and poly 2-(acryloyloxy)ethyltrimethylammonium chloride (AETAc) bio ionic liquid (Bio-IL). This novel approach decouples conflicting mechano-chemical effects arising from different molecular building blocks and provides a balance of mechanical toughness (1.1 × 106 Jm-3), flexibility, conductivity (≈0.29 S m-1), and tissue adhesion (≈27kPa), along with rapid self-healing and remarkable stretchability (≈3000%). Unlike traditional hydrogels, the one-pot synthesis avoids chemical crosslinkers and metallic nanofillers, reducing cytotoxicity. While the pAAm provides mechanical strength, the formation of the pseudo-slide-ring structure ensureshigh stretchability and flexibility. Combining pAAm with β-CD and pAETAc enhances biocompatibility and biodegradability, as confirmed by in vitro and in vivo studies. The hydrogel also offers transparency, passive-cooling, ultraviolet (UV)-shielding, and 3D printability, enhancing its practicality for everyday use. The engineered sensor demonstratesimproved efficiency, stability, and sensitivity in motion/haptic sensing, advancing real-time human healthcare monitoring.

Advancing micro-electrometric techniques for the detection of organophosphate and carbamate residues using cricket cholinesterase.

The widespread use of organophosphate (OP) and carbamate (CM) pesticides requires efficient and cost-effective detection methods. This study introduces a micro-electrometric method using cricket cholinesterase (ChE) to detect OP and CM residues, providing a rapid and economical alternative to conventional chromatographic techniques. The parameters of the method, including the substrate concentration, incubation temperature, and incubation time, were optimized. By leveraging the sensitivity of cricket ChE to OP and CM inhibition, this approach translates enzyme inhibition into an electrical signal to quantify pesticide levels, achieving an impressive limit of detection (LOD) from 0.036 to 0.086 parts per million (ppm). This method demonstrated reproducibility and stability, making it suitable for field applications and on-site testing across various environmental matrices. This research represents a significant advancement in pesticide residue analysis with potential applications in the development of portable biosensor devices for real-time environmental monitoring and public health protection.

Noise Study on the OH1 Wearable Device: Analysis of 11 Hand Movement Artifacts

Wearable devices like the OH1 are increasingly used for real-time health monitoring, particularly for measuring heart rate (BPM). However, their accuracy is often compromised by motion artifacts, introducing significant noise into the measurements. This study specifically addresses the issue of noise generated by the OH1 wearable device during eleven different hand movements. To tackle this problem, we implemented a precise experimental setup involving device calibration, stable testing conditions, and participant training to ensure high consistency in hand movements. Additionally, machine learning algorithms were employed to separate noise from desired hand movement data. Our results indicate that certain hand movements, such as lifting arms and shoulder rotations, produce higher noise levels, while others, like placing hands on the table, generate minimal noise. These findings provide valuable insights for developing effective noise reduction algorithms, ultimately enhancing the accuracy and reliability of BPM measurements from wearable devices.

Variation of segment joint opening of underwater shield tunnel during long operational period

Segment joint is the main water leakage channel in underwater shield tunnels, and joint deformation influences tunnel waterproof capacity greatly. Real-time structure health monitoring is a great way to investigate the joint opening and to offer early warnings for abnormality, such as data outlier and joint properties degradation. This study investigated the variation of joint opening, provided early warning indexes and evaluated the joint mechanical properties degradation using the field monitoring data of an underwater shield tunnel over 10 years, which is rarely seen in the literature. Study results show that: (1) Field monitoring data reveals that the distribution of joint opening increments obeys a heavy-tailed distribution rather than the normal distribution. The exponential distribution model and the log normal distribution model can fit the longitudinal joint increments and circumferential joint increments much better. (2) The early warning index is determined based on the statistic theory and 0.999 quantile is set as the warning value. The obtained warning values of the studied underwater tunnel are 0.005 mm and 0.08 mm for the longitudinal and circumferential joint respectively. The small warning value means that small abnormality can be identified and the early warning would be more efficient. (3) A mechanical model is proposed to evaluate the joint mechanical properties degradation based on monitoring data. Results show that normal stiffness of circumferential joint decreases from 18.3 GPa/m to 14.7 GPa/m. Results of this study provides valuable reference for early-warning and joint degradation evaluation of under water shield tunnel.