Continuous Health Monitoring Research Articles

A multisource data‐driven monitoring model for assessing concrete dam behavior

AbstractThe pivotal role of dam infrastructure necessitates continuous health monitoring, which results in extensive sets of data. Most monitoring data‐based models in dam engineering concentrate on predicting dam behavior. However, little attention has been systematically paid to the processing of extensive monitoring data, modeling of comprehensive dam behavior, and assessment of overall dam operation status. Here, we propose a novel monitoring model comprising three main aspects: a multidimensional data mining method, a multipoint response prediction method, and a multilayer data fusion‐based assessment method. Utilizing monitoring data from a mega concrete arch dam, we evaluate and discuss the effects of data mining, modeling accuracy for dam behavior, robustness against data pollution, and sensitivity to anomalies. Comparisons with classical benchmarks demonstrate the performance of the proposed model for the dam.

Smart Hydrogel Sensors for Health Monitoring and Early Warning

AbstractSmart hydrogel sensors, functioning as implantable devices, play a vital role in health monitoring and early warning, overcoming the limitations of conventional clinical methods to achieve direct, continuous, and precise monitoring. Widely employed across various biomedical fields, these sensors offer unique advantages for early health monitoring, ensuring direct, continuous, and highly accurate monitoring. In addition to detecting biomolecules, smart hydrogel sensors, with their flexibility and biocompatibility, monitor disease‐specific markers, offer insights into disease progression, and contribute to the early identification of diseases. This article provides a comprehensive review of the types of hydrogel sensors employed in human health monitoring. The study discusses recent advancements in smart hydrogel sensor research, aiming to offer promising methods for human health monitoring. Finally, the paper outlines prospective research directions for hydrogel sensors in the field of human health monitoring. While further research and clinical validation are essential, hydrogel sensors are poised to play a pivotal role in clinical applications, furnishing people with accurate and continuous health monitoring.

PPG-Hear: A Practical Eavesdropping Attack with Photoplethysmography Sensors

Photoplethysmography (PPG) sensors have become integral components of wearable and portable health devices in the current technological landscape. These sensors offer easy access to heart rate and blood oxygenation, facilitating continuous long-term health monitoring in clinic and non-clinic environments. While people understand that health-related information provided by PPG is private, no existing research has demonstrated that PPG sensors are dangerous devices capable of obtaining sensitive information other than health-related data. This work introduces PPG-Hear, a novel side-channel attack that exploits PPG sensors to intercept nearby audio information covertly. Specifically, PPG-Hear exploits low-frequency PPG measurements to discern and reconstruct human speech emitted from proximate speakers. This technology allows attackers to eavesdrop on sensitive conversations (e.g., audio passwords, business decisions, or intellectual properties) without being noticed. To achieve this non-trivial attack on commodity PPG-enabled devices, we employ differentiation and filtering techniques to mitigate the impact of temperature drift and user-specific gestures. We develop the first PPG-based speech reconstructor, which can identify speech patterns in the PPG spectrogram and establish the correlation between PPG and speech spectrograms. By integrating a MiniRocket-based classifier with a PixelGAN model, PPG-Hear can reconstruct human speech using low-sampling-rate PPG measurements. Through an array of real-world experiments, encompassing common eavesdropping scenarios such as surrounding speakers and the device's own speakers, we show that PPG-Hear can achieve remarkable accuracy of 90% for recognizing human speech, surpassing the current state-of-the-art side-channel eavesdropping attacks using motion sensors operating at equivalent sampling rates (i.e., 50Hz to 500Hz).

Hydrogels in biosensing and medical diagnostics

This article presents an in-depth examination of recent advancements in medical and biotechnological sensing technologies, focusing on the forefront of innovation in hydrogel-based sensors within the domains of biomedical engineering and regenerative medicine. It delves into cutting-edge sensing technologies that facilitate non-invasive glucose monitoring, highlights progress in the development of intelligent solutions for wound care, and discusses the application of optical and fluorescence-based sensors for real-time diagnostics within the body. Further, it reviews the latest glucose monitoring devices, alongside wearable and implantable sensors designed for the continuous monitoring of health, including the measurement of physiological strain and stress. The exploration extends to the latest in non-invasive and minimally invasive technologies for ongoing health assessment, and to imaging and visualization techniques critical for medical diagnostics and therapeutic procedures. These advancements mark a pivotal move toward more efficient, precise, and patient-focused healthcare technologies, signaling new avenues for diagnosis, monitoring, and treatment in the healthcare sector.

Confluence Effect of Debris-Filled Damage and Temperature Variations on Guided-Wave Ultrasonic Testing (GWUT)

Continuous monitoring of structural health is essential for the timely detection of damage and avoidance of structural failure. Guided-wave ultrasonic testing (GWUT) assesses structural damages by correlating its sensitive features with the damage parameter of interest. However, few or no studies have been performed on the detection and influence of debris-filled damage on GWUT under environmental conditions. This paper used the pitch–catch technique of GWUT, signal cross-correlation, statistical root mean square (RMS) and root mean square deviation (RMSD) to study the combined influence of varying debris-filled damage percentages and temperatures on damage detection. Through experimental result analysis, a predictive model with an R2 of about 78% and RMSE values of about 7.5×10−5 was established. When validated, the model proved effective, with a comparable relative error of less than 10%.

Bio-inspired flexible optical microfiber sensor with nanohemisphere array microstructure for ultra-sensitive detection of skin signals

We propose the utilization of a bio-inspired nanohemispherical array of microstructure flexible optical microfiber sensor (NHAOMS) designed for highly sensitive detection of epidermal signals and cardiovascular health assessment. NHAOMS builds a flexible wearable self-mixing optical sensor by assembling microfiber in a polydimethylsiloxane (PDMS) film with a surface modified nanohemispheres (400 nm diameter) array, where the microfiber sensor is used to sense physiological signals. PDMS with a surface modified nano-hemispherical array is used to eliminate misalignment and increase the sensitivity of sensing pressure. Due to the high flexibility of PDMS and its surface modified nano-hemispherical array microstructure, the sensing element can reliably transmit pressure stimuli into interpretable optical signals with high fidelity, allowing instantaneous pressure perception. The sensor, measuring 20×20mm in size, boasts a sensitivity of 0.0138mV/kPa, with a rise time of 3.7 ms and a fall time of 6.26 ms. By tracking fluctuations in the reflected light power of microfiber within PDMS, real-time monitoring of the fingertip pulse wave is achieved. Extracting cardiovascular vital signs from this pulse signal enables the evaluation of cardiovascular health status, yielding results consistent with human physiological characteristics. This non-invasive, continuous, and precise cardiovascular health monitoring, facilitated by wearable devices, holds significant promise for applications in human health monitoring, as well as in the prevention and diagnosis of cardiovascular diseases.

Data management for continuous learning in EHR systems

To gain a comprehensive understanding of a patient’s health, advanced analytics must be applied to the data collected by electronic health record (EHR) systems. However, managing and curating this data requires carefully designed workflows. While digitalization and standardization enable continuous health monitoring, missing data values and technical issues can compromise the consistency and timeliness of the data. In this paper, we propose a workflow for developing prognostic models that leverages the SMART BEAR infrastructure and the capabilities of the Big Data Analytics (BDA) engine to homogenize and harmonize data points. Our workflow improves the quality of the data by evaluating different imputation algorithms and selecting one that maintains the distribution and correlation of features similar to the raw data. We applied this workflow to a subset of the data stored in the SMART BEAR repository and examined its impact on the prediction of emerging health states such as cardiovascular disease and mild depression. We also discussed the possibility of model validation by clinicians in the SMART BEAR project, the transmission of subsequent actions in the decision support system, and the estimation of the required number of data points.

Advancements in Polymer-Assisted Layer-by-Layer Fabrication of Wearable Sensors for Health Monitoring.

Recent advancements in polymer-assisted layer-by-layer (LbL) fabrication have revolutionized the development of wearable sensors for health monitoring. LbL self-assembly has emerged as a powerful and versatile technique for creating conformal, flexible, and multi-functional films on various substrates, making it particularly suitable for fabricating wearable sensors. The incorporation of polymers, both natural and synthetic, has played a crucial role in enhancing the performance, stability, and biocompatibility of these sensors. This review provides a comprehensive overview of the principles of LbL self-assembly, the role of polymers in sensor fabrication, and the various types of LbL-fabricated wearable sensors for physical, chemical, and biological sensing. The applications of these sensors in continuous health monitoring, disease diagnosis, and management are discussed in detail, highlighting their potential to revolutionize personalized healthcare. Despite significant progress, challenges related to long-term stability, biocompatibility, data acquisition, and large-scale manufacturing are still to be addressed, providing insights into future research directions. With continued advancements in polymer-assisted LbL fabrication and related fields, wearable sensors are poised to improve the quality of life for individuals worldwide.

Bio-Electronics Interface between Electronics and Biological Systems for Healthcare Applications.

The intersection of electronics and biology has led to the emergence of bio-electronics interfaces, which hold tremendous potential for revolutionizing healthcare applications. These interfaces facilitate seamless communication between electronic devices and biological systems, enabling a range of diagnostic, therapeutic, and monitoring capabilities with unprecedented precision and efficiency.This paper provides a comprehensive overview of the recent advancements in bio-electronics interfaces and their diverse applications in healthcare. The fundamental principles underlying the integration of electronics with biological systems, including the design and fabrication of bio-compatible materials, signal transduction mechanisms, and biointegration strategies are discussed. [1] The paper discusses specific healthcare applications enabled by bio-electronics interfaces, such as bio-sensing for disease diagnosis, neural interfaces for brain-machine communication and prosthetics, bio-electronic implants for targeted drug delivery and neuromodulation, and wearable devices for continuous health monitoring. We also examine the challenges and opportunities associated with the development and implementation of bio-electronics interfaces in healthcare settings. These challenges include biocompatibility issues, signal interference, power management, data security, and regulatory considerations. However, rapid advancements in materials science, microfabrication techniques, wireless communication, and machine learning are driving innovation and overcoming these hurdles. We highlight the potential of bio-electronics interfaces to transform personalized medicine by enabling real-time monitoring of physiological parameters, early detection of diseases, and personalized therapeutic interventions tailored to individual patients' needs. By integrating electronic devices with biological systems at the molecular, cellular, and organismal levels, bio-electronics interfaces have the potential to revolutionize healthcare delivery, improve patient outcomes, and enhance quality of life.

Continuous Health Monitoring System For Patients Using IoT

Internet of Things (IoT) can be used to monitor health conditions of patients after major surgeries. Information from various sensors is processed by PIC16F877A microcontroller which sends data to GSM Network with the help of IoT module. A relay is used to switch between IoT module and GSM module based on the output voltage of the sensors. The data transmitted from the microcontroller is stored on the cloud for long term storage and access. The address of the webpage used should be configured in the IoT module. Alert signal is sent to caretaker in case of emergency. This system is easy to setup with low power consumption and high performance. This system uses GSM network so can be used even in rural areas where broadband facility is not available.

Artificial Intelligence and Occupational Health and Safety, Benefits and Drawbacks.

This paper discusses the impact of artificial intelligence (AI) on occupational health and safety. Although the integration of AI into the field of occupational health and safety is still in its early stages, it has numerous applications in the workplace. Some of these applications offer numerous benefits for the health and safety of workers, such as continuous monitoring of workers' health and safety and the workplace environment through wearable devices and sensors. However, AI might have negative impacts in the workplace, such as ethical worries and data privacy concerns. To maximize the benefits and minimize the drawbacks of AI in the workplace, certain measures should be applied, such as training for both employers and employees and setting policies and guidelines regulating the integration of AI in the workplace.

Assessment of salt-affected soil in selected rice irrigation schemes in Tanzania: understanding salt types for optimizing management approaches

Salt-affected soils have serious implications for agricultural land quality and productivity, leading to a reduction in the net cultivable area available for food production. This issue has emerged as one of the foremost global challenges in recent years, impacting both food security and environmental sustainability. This research focuses on soil characterizations conducted in three irrigation schemes to understand the types, distribution, and nature of salt-affected soils. Auger observations served as the basic methodology for soil unit delineation in this study. Twelve representative soil pits with a depth of more than 160 cm were described, and undisturbed and disturbed samples were collected and analyzed for physical and chemical properties. The schemes were characterized by low levels of available phosphorus, organic carbon, and total nitrogen, as well as variations in exchangeable bases. Furthermore, 37.5% of the analyzed horizons highlighted the presence of salt-affected soils. Sodium was the predominant cation, followed by magnesium, potassium, and calcium. Likewise, HCO3− was the dominant anion, followed by Cl− and SO42−. The results of this study reveal the existence of two types of salt-affected soil. Uturo irrigation scheme had sodic soils, while the Ndungu irrigation scheme had saline-sodic soils. Additionally, both sodic and saline-sodic soils have been discovered in the Mawala irrigation scheme. The root cause of this challenge is inherent soil conditions, exacerbated by inadequate drainage infrastructure. Therefore, measures such as the application of gypsum followed by salt leaching, improving soil drainage by incorporating organic matter, improving drainage infrastructure, and using rice-tolerant varieties are recommended to mitigate salt stress and improve soil fertility. It is imperative to establish a robust framework for continuous monitoring and regular assessment of soil health to enable timely interventions and informed decisions for sustainable agricultural management.

A proposed tacho-less order tracking technique suitable for railway vehicle condition monitoring applications

To perform a condition monitoring for rotating machines with variable running speed, order tracking is considered as an effective technique suitable for their non-stationary vibration signals. A tachometer signal is necessary to perform a computed order tracking (COT) technique; however, in some applications it's not possible to add tachometer hardware due to its complexity or shortage of power. To improve the applicability of order tracking techniques, some tacho-less order tracking techniques have been introduced in the last few years. These techniques are working off-line by inspecting the vibration signals in time-frequency domain to extract the machine rotating speed. In this paper, a new tacho-less technique suitable for continuous structural health monitoring applications of railway vehicles, is proposed. In the presented approach, a coarse angular speed of the railway axle is estimated firstly using estimation train speed. Then this coarse angular speed is used to improve the applicability of the traditional tacho-less techniques to be able to work online. The train speed is estimated from two acceleration signals obtained from vertical vibration of axle-boxes from same side of train. The sequential properties of the two signals are used to estimate the delay time of one signal with respect to the other one by means of cross-correlation function. By knowing the horizontal distance between the two sensors collected the vibration signals, the train speed can be estimated. This modification of tacho-less technique improves the effectiveness and robustness of the traditional maxima tracking strategy to be performed automatically. Thereafter, signal processing technique based on the computed order tracking as well as averaging technique can be applied to extract the useful information from noisy and non-stationary signals. This technique has been proposed in a framework of a project investigating crack detection using vibration measurements, therefore, vibration signals of cracked axle have been processed using tacho-based method and using the proposed tacho-less technique and a good agreement has been found between the two techniques.

A Novel Induction-Type Pressure Sensor based on Magneto-Stress Impedance and Magnetoelastic Coupling Effect for Monitoring Hand Rehabilitation.

Visualization of training effectiveness is critical to patients' confidence and eventual rehabilitation. Here, an innovative magnetoinductive pressure sensor is proposed for monitoring hand rehabilitation in stroke hemiplegic patients. It couples the giant magneto and stress-impedance effects of a square spiral amorphous wire with the giant magnetoelastic effect of a polymer magnet (NdFeB@PDMS). The addition of the magnetoelastic layer results in a sensitivity improvement of 178%, a wide sensing range (up to 1 MPa), fast response/recovery times (40 ms), and excellent mechanical robustness (over 15 000 cycles). Further integration with an LC oscillation circuit enables frequency adjustment into the MHz range resulting in a sensitivity of 6.6% kPa-1 and outstanding linearity (R2 = 0.99717) over a stress range of up to 100 kPa. When attached to a commercial split-fingerboard, the sensor is capable of dynamically monitoring the force in each finger, providing a reading of the rehabilitation process. Unlike conventional inductive sensors, the sensor is based on an inductive force-responsive material (amorphous wire), which significantly boosts the sensitivity. The approach also demonstrates the potential of magnetoelasticity in static pressure sensing, which is highly sensitive to dynamic pressure only through electromagnetic induction. This makes it more suitable for long-term and continuous human health monitoring.

Ubiquitous Gait Analysis through Footstep-Induced Floor Vibrations.

Quantitative analysis of human gait is critical for the early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson's disease, stroke, and cerebral palsy. Gait analysis typically involves estimating gait characteristics, such as spatiotemporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates but are limited in operational requirements when applied in daily life, such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing floor vibrations generated by human footsteps using vibration sensors mounted on the floor surface. Our approach is low-cost, non-intrusive, and perceived as privacy-friendly, making it suitable for continuous gait health monitoring in daily life. Our algorithm estimates various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step time, stride time, stance time, swing time, double-support time, and single-support time), spatial parameters (step length, width, angle, and stride length), and extracts gait health indicators (cadence/walking speed, left-right symmetry, gait balance, and initial contact types). The main challenge we addressed in this paper is the effect of different floor types on the resultant vibrations. We develop floor-adaptive algorithms to extract features that are generalizable to various practical settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.

INTEGRATING ADVANCED TECHNOLOGIES FOR ENHANCED HSE MANAGEMENT IN THE FMCG SECTOR

The Fast-Moving Consumer Goods (FMCG) sector operates in a dynamic environment, facing numerous challenges in maintaining Health, Safety, and Environment (HSE) standards while meeting the demands of a rapidly evolving market. To address these challenges, integrating advanced technologies has emerged as a strategic approach for enhancing HSE management practices within the FMCG sector. This review explores the integration of various advanced technologies and their impact on improving HSE management in the FMCG industry. The utilization of technologies such as Internet of Things (IoT), Artificial Intelligence (AI), and Big Data Analytics has revolutionized HSE management practices in the FMCG sector. IoT sensors embedded in production machinery and equipment enable real-time monitoring of environmental conditions, equipment performance, and worker safety. AI-driven predictive analytics algorithms analyze vast amounts of data to identify potential safety hazards, predict equipment failures, and optimize HSE protocols. Furthermore, the adoption of wearable devices equipped with biometric sensors provides continuous health monitoring for employees, ensuring early detection of fatigue, stress, or other health-related issues. Virtual Reality (VR) and Augmented Reality (AR) technologies are utilized for immersive HSE training simulations, enabling employees to practice safety procedures in realistic virtual environments, thus enhancing their preparedness for real-life scenarios. Moreover, the integration of drone technology facilitates remote monitoring of vast operational areas, enabling quick identification of potential hazards and swift response to emergencies. Additionally, blockchain technology ensures the transparency and traceability of HSE data across the supply chain, enhancing accountability and compliance with regulatory standards. The integration of advanced technologies holds significant promise for enhancing HSE management practices in the FMCG sector, fostering a safer and more sustainable operational environment while addressing the evolving challenges of the industry.
 Keywords: HSE, Management, FMCG, Technologies, Technology, Review, Innovation.

Designing an Internet of Things solution for monitoring vital signs

The object of study is the process of monitoring vital signs using an automated system based on an Internet of Things (IoT) solution. The study investigates and analyses the best existing solutions for continuous monitoring of human health. The research is important in the context of a possible pandemic and general health monitoring. An IoT model of a solution for monitoring and analyzing vital signs in patients is proposed. The project involves the creation of hardware and software for tracking vital signs. The interaction of the two parts will ensure that the main task is to obtain the result and analyze the indicators of vital functions of the human body. The hardware is implemented using devices for scanning data on heart rate, temperature, saturation, and the ability to track electrocardiograms. It is possible to transmit data on the state of the body. The position of the sensors attached to the body is taken into account in case they come off. The device itself should be placed on the human body in the area of the front chest wall, wrists, and ankles. The device is also programmed to respond to sudden changes in these values. The software implementation is based on a web-based interface. The design of the final solutions for the interaction between the local and intermediate server was implemented using Django and Python. The ability to administer the intermediate server of the client's time zone was written using HTML, CSS, and JavaScript. The use of the IoT solution allows monitoring the indicators of vital functions of the body and their analysis. A scheme of information exchange in the system for monitoring health indicators has been built.

Magnetic Micro and Nano Sensors for Continuous Health Monitoring

Magnetic micro and nano sensors can be used in a broad variety of applications, e.g., for navigation, automotives, smartphones and also for health monitoring. Based on physical effects such as the well-known magnetic induction, the Hall effect, tunnel magnetoresistance and giant magnetoresistance, they can be used to measure positions, flow, pressure and other physical properties. In biomedicine and healthcare, these miniaturized sensors can be either integrated into garments and other wearables, be directed through the body by passive capsules or active micro-robots or be implanted, which usually necessitates bio-functionalization and avoiding cell-toxic materials. This review describes the physical effects that can be applied in these sensors and discusses the most recent micro and nano sensors developed for healthcare applications.

Livestock Management System

Abstract: Currently, advancements in farm automation and technological techniques contribute significantly to boosting farm productivity, with a notable focus on cattle health monitoring systems in research. This system encompasses infrastructure, hardware, and software components aimed at enhancing cattle health, crucial for overall farm productivity. Given the challenges posed by managing numerous cattle in dairy farms, continuous monitoring of individual health becomes imperative. Leveraging sensor technology to monitor vital signs such as temperature and heart rate allows for early detection of health issues, reducing the need for frequent health inspections and long-term healthcare costs for the animals.

HealthGuard: Real-Time Health Monitoring and Emergency Alert System

Abstract: In today's fast-paced world, the need for continuous health monitoring and rapid response to emergent medical situations is paramount. "HealthGuard" is a cutting-edge real-time health monitoring and emergency alert system designed to address this critical need. Leveraging the power of IoT sensors and advanced data analytics, HealthGuard offers a comprehensive solution for tracking and responding to individual's health conditions, ensuring timely intervention when necessary.