Health Monitoring Research Articles

Farmers Awareness and Perception of Drone Technology

The agricultural sector is increasingly leveraging technological advancements to enhance productivity and sustainability. Among these innovations, drone technology, a crucial component of precision agriculture, is gaining global recognition. This study explores the current awareness and perception of drone technology among farmers in Coimbatore district, Tamil Nadu, where agriculture continues to be a critical livelihood source. Using an ex-post facto research design, the study sampled 60 farmers from various regions within Coimbatore district, selected through a simple random sampling technique to assess their awareness and perception of drone technology. Data were collected through structured interviews and questionnaires, analyzed using statistical tools like frequency, percentage analysis and mean score is to understand response patterns. The findings reveal that awareness of drone technology is highest for its use in spraying agrochemicals (75 per cent), followed by pest and disease control (68 per cent) and irrigation management (66 per cent). Farmers expressed positive perceptions of the efficiency and precision that drones bring to farming operations, particularly in reducing labor and time spent on manual tasks. Drones were recognized for their potential to improve crop health monitoring, optimize resource use, and promote sustainability by minimizing chemical overuse. Nonetheless, logistical challenges such as the durability of drones in extreme weather conditions, connectivity issues in rural areas, and maintenance costs pose significant obstacles to widespread adoption. While government initiatives, including subsidies have begun to alleviate some financial burdens, there is a clear need for further support in terms of training, infrastructure, and policy enhancements. The study underscores the importance of integrating drones into the agricultural sector to ensure more sustainable and productive farming practices that can meet the growing global food demand.

Stretchable Ag/AgCl Nanowire Dry Electrodes for High-Quality Multimodal Bioelectronic Sensing.

Bioelectrical signal measurements play a crucial role in clinical diagnosis and continuous health monitoring. Conventional wet electrodes, however, present limitations as they are conductive gel for skin irritation and/or have inflexibility. Here, we developed a cost-effective and user-friendly stretchable dry electrode constructed with a flexible network of Ag/AgCl nanowires embedded in polydimethylsiloxane (PDMS). We compared the performance of the stretched Ag/AgCl nanowire electrode with commonly used commercial wet electrodes to measure electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG) signals. All the signal-to-noise ratios (SNRs) of the as-fabricated or stretched (50% tensile strain) Ag/AgCl nanowire electrodes are higher than that measured by commercial wet electrodes as well as other dry electrodes. The evaluation of ECG signal quality through waveform segmentation, the signal quality index (SQI), and heart rate variability (HRV) reveal that both the as-fabricated and stretched Ag/AgCl nanowire electrode produce high-quality signals similar to those obtained from commercial wet electrodes. The stretchable electrode exhibits high sensitivity and dependability in measuring EMG and EEG data, successfully capturing EMG signals associated with muscle activity and clearly recording α-waves in EEG signals during eye closure. Our stretchable dry electrode shows enhanced comfort, high sensitivity, and convenience for curved surface biosignal monitoring in clinical contexts.

Convolution neural network based multi-class classification of rehabilitation exercises for diastasis recti abdominis using wearable EMG-IMU sensors

PurposeGlobally, postnatal women endure a prominent issue caused by midline separation of abdominal recti muscles, characterized by a sagging and pouch-like appearance of the belly termed as Diastasis Recti Abdominis (DRA). The necessity of ensuring the efficacy of rehabilitative workouts for individuals with DRA cannot be overstated, as inaccurate exercises can exacerbate the condition and deteriorate the health of affected women. The purpose of these exercises is to specifically focus on the rectus abdominis muscles to facilitate the reapproximation of the linea alba. The primary aim of this research work is to assess the effectiveness of rehabilitation exercises for DRA women obtained from Inertial Measurement Unit (IMU) and Electromyography (EMG) sensors.Design/methodology/approachConvolutional neural networks (CNN) employs convolutional activation functions and pooling layers. Recently, 1D CNNs have emerged as a promising approach used in various applications, including personalized biomedical data classification and early diagnosis, structural health monitoring and anomaly detection. Yet another significant benefit is the feasibility of a real-time and cost-effective implementation of 1D CNN. The EMG and IMU signals serve as inputs for the 1D CNN. Features are then extracted from the fully connected layer of the CNN and fed into a boosting machine learning algorithm for classification.FindingsThe findings demonstrate that a combination of sensors provides more details about the exercises, thereby contributing to the classification accuracy.Practical implicationsIn real time, collecting data from postnatal women was incredibly challenging. The process of examining these women was time-consuming, and they were often preoccupied with their newborns, leading to a reluctance to focus on their own health. Additionally, postnatal women might not be fully aware of the implications of DRA and the importance of rehabilitation exercises. Many might not realize that neglecting DRA can lead to long-term issues such as back pain, pelvic floor dysfunction, and compromised core strength.Social implicationsDuring our data collection camps, there were educational sessions to raise awareness about the DRA problem and the benefits of rehabilitation exercises. This dual approach helped in building trust and encouraging participation. Moreover, the use of wearable sensors in this study provided a non-invasive and convenient way for new mothers to engage in rehabilitation exercises without needing frequent visits to a clinic, which is often impractical for them.Originality/valueThe utilization of discriminating features retrieved from the output layer of 1D CNN is a significant contribution to this work. The responses of this study indicate that 1D convolutional neural network (1D CNN) and Boosting algorithms used in a transfer learning strategy produce successful discrimination between accurate and inaccurate performance of exercises by achieving an accuracy of 96%.

Computational Analysis of Stiffness Reduction Effects on the Dynamic Behaviour of Floating Offshore Wind Turbine Blades

This paper describes the study of a floating offshore wind turbine (FOWT) blade in terms of its dynamic response due to structural damage and its repercussions on structural health monitoring (SHM) systems. Using a finite element model, natural frequencies and mode shapes were derived for both an undamaged and a damaged blade configuration. A 35% reduction in stiffness at node 1 was applied in order to simulate significant damage. Concretely, the results are that the intact blade has a fundamental frequency of 0.16 Hz, and this does not change when damaged, while higher modes exhibit frequency changes: mode 2 drops from 2.05 Hz to 2.00 Hz and mode 3 from 6.15 Hz to 6.01 Hz. The shifts show a critical loss in the capability of handling vibrational energy due to the damage; higher modes (4, 5, and 6) show larger frequency deviations going down to as low as 18.06 Hz in mode 6. The mode shape change is considerable for the edge-wise and flap-wise deflection of the 2D contour plots, indicating possible coupling effects between modes. These results indicate that lower modes are sensitive to stiffness reductions, and the continuous monitoring of the lower harmonic modes early is required to detect damages. These studies have helped to improve blade design, maintenance, and operational safety for FOWT systems.

Maximum Fourier spectrum cyclostationarity blind deconvolution and its application in structural health monitoring of power transformers

Abstract Power transformer is the most important equipment that affects whether the electric power system can be operated safely and normally, whose condition assessment problem has attracted considerable attention. Background noise frequently affects the effectiveness of nonintrusive techniques based on vibro-acoustic signals for structural health monitoring in power transformers. It is challenging to effectively eliminate background noise interference from time-frequency domain transformer defect detection techniques such as wavelet transformations, modal decomposition, etc. In this scenario, the Fourier spectrum cyclostationarity index (FSC) is designed based on the cyclostationarity index used for rotating machinery fault diagnosis to construct a maximum Fourier spectrum cyclostationarity blind deconvolution method (MFSCBD) for transformer fault detection in this paper. Firstly, the limitations of the traditional blind deconvolution (BD) in transformer fault detection are discussed in the mathematical principle. Then, a new BD framework based on Kepler optimization algorithm is proposed according to the principle of convex optimization to address the problems of difficulty in solving the complex blind objective function in the traditional differential BD framework and the ill-condition problem in the Rayleigh quotient BD framework. Subsequently, a synthetic nonstationary and nonlinear simulation signal is constructed for numerical verification, and a six-microphone array is designed to obtain the practical signals from the operating transformer to verify the performance of MFSCBD. Finally, the applications on the simulated and experimental signals of power transformers demonstrate that MFSCBD outperforms complete ensemble empirical mode decomposition with adaptive noise and successive variational mode decomposition to some extent for structural health monitoring.

Virtual medical astronaut avatars for future deep space missions

This paper explores the telemedicine possibilities available to astronauts during spaceflight, focusing on the possible utilization of medical avatars for continuous health monitoring. Various types and sources of telemedicine are examined, along with their applications in deep space travel to address the challenges faced by astronauts and facilitate timely intervention in case of injuries and illnesses. The use of medical avatars presents a promising solution to mitigate risks and ensure effective healthcare delivery in the absence of immediate physical medical support. Furthermore, potential terrestrial applications of telemedicine and virtual medical avatars are discussed, highlighting the wider implications and benefits beyond space exploration. By leveraging advanced technologies and innovative approaches, telemedicine in conjunction with medical avatars offers a compelling avenue for enhancing healthcare practices both in space and on Earth.

Long-term spatiotemporal changes in nitrate contamination of municipal groundwater resources after sewerage network construction in the Hungarian Great Plain.

Over the last decades, as a consequence of wastewater discharges and other anthropogenic sources, severe nitrate (NO3-) pollution has developed in municipal environment causing global concern. Thus, eliminating the potential sources of pollution is one of the major challenges of the twenty-first century, whereby sanitation services are essential for ensuring public health and environmental protection. In the present study, long-term monitoring (2011-2022) of shallow groundwater NO3- contamination in municipal environment was carried following the construction of the sewerage network (2014) in the light of the pre-sewerage situation. Our primary aim was to assess the long-term effects of sewerage on nitrate NO3- levels in the shallow groundwater and evaluate the efficiency of these sanitation measures over time. Based on the results, significant pollution of the shallow groundwater in the municipality was identified. During the pre-sewer period, NO3- concentrations exceeded the 50mg/L limit in the majority of monitoring wells significantly, upper quartile values ranged between 341 and 623mg/L respectively. Using Nitrate Pollution Index (NPI) and interpolated NO3- pollution maps, marked spatial north-south differences were detected. In order to verify the presence of wastewater discharges in the monitoring wells, the isotopic ratio shifts (δ) for 18O and D(2H) were determined, confirming municipal wastewater effluent. Variations in NO3-/Cl- molar ratios suggest also contamination from anthropogenic sources, including septic tank effluent from households and the extensive use of manure. Data series of 7years (2015-2022) after the investment indicate marked positive changes by the appearance of decreasing trends in NO3- values confirmed by Wilcoxon signed rank test and ANOVA. By comparing the pre- and post-sewerage conditions, the mean NO3- value decreased from 289.7 to 175.6mg/L, with an increasing number of monitoring wells with concentrations below the limit. Our results emphasise the critical role of sanitation investments, while also indicating that the decontamination processes occur at a notably slow pace. Detailed, long-term monitoring is therefore essential to ensure accurate follow-up of the ongoing changes. The results can provide information for local citizens and authorities to improve groundwater management tools in the region.

Groundwater monitoring in challenging environments: an argument for the construction of observation wells based on data from Niamey, Niger

AbstractGroundwater sampling in challenging environments often leads to compromises in following best practices to obtain representative samples from aquifers. This includes collecting samples from existing production or domestic wells instead of using properly constructed monitoring wells or using a bailer instead of a submersible pump for sampling. To address unusual patterns and trends in groundwater chemistry data collected in Niamey, Niger from 2012–2021, a state-of-the-art monthly sampling routine was established for eight wells tapping the basement aquifer. This was based on the hypothesis that the observed changes in groundwater composition were mainly due to differences in sampling technique, and the aim of the study was to gain insights into possible seasonal variations in water composition, to examine if the previously observed trends could be validated and to provide baseline data for future studies. The results indicate that in most cases the long well response zones in the stratified aquifer system led to the collection of water from different strata/aquifers or of strongly mixed samples. Therefore, any sample from those wells is only of limited value for the interpretation of hydrogeological processes. To obtain sound data for the development of groundwater management strategies, the monitoring has to be shifted from existing production wells to properly constructed monitoring wells. In the complex hydrogeological setting of Niamey, with hydraulically interacting aquifers and occurrences of density layering, it is fundamental to ensure that a monitoring well taps one specific depth of one target aquifer and that well-volume purging is applied properly.

Multiple covalent modification enables nylon fiber biosensor with robust scrub-resistant and signal-capture ability for multiscenario health monitoring and security warning

Nylon fibers have great potentials in smart textiles due to excellent wear resistance, resilience, and chemical stability, whereas poor combination between fibers and conductive materials causes discontinuous signal capture. In this work, nylon fibers/di-aldehyde cellulose nanocrystals/polypyrrole (NFACP) biosensors with robust scrub-resistant and signal-capture ability were fabricated by interfacial multiple covalent reactions. The best NFACP0.2 biosensor exhibited high conductivity (354 S/m), robust mechanical strength and stretching-releasing dynamic durability. Especially, its textile sensors still possessed high sensitivity and excellent sensing performance after repeated washing and friction. Moreover, NFACP0.2 biosensor was designed into various multifunctional health monitoring and security warning systems for “stress reducing exercise” enthusiasts, high-altitude activities, and deep-sea exploration, demonstating great potentials of conductive nylon fiber biosensor in flexible electronics.

Biosensor applications in the monitoring of elderly patients

Nurse-based patient monitoring is prone to errors due to manual measurements and documentation, leading to potential inaccuracies in care. The use of biosensors offers a promising solution by enabling real-time and continuous monitoring of patient health. Categorizing patient care reports as critical or non-critical using mobile recording systems based on biosensor data can help prevent errors and improve care. The use of biosensors can significantly reduce morbidity and mortality, especially after emergencies and accidents. These devices improve the quality of care and increase the satisfaction of older people, their families and healthcare professionals. Wearable biosensors make it easier for older people to monitor their health, which can help reduce hospital admissions. Chronic diseases such as cardiovascular disease, cancer, diabetes, dementia and stroke pose challenges to healthcare delivery and interpretation of results. Integrating biosensors into health monitoring and measurement is an innovative approach to managing these chronic conditions more effectively. To improve self-management of chronic diseases in older people, it is essential to educate healthcare professionals and promote research in this area. As a result, the use of biosensors to monitor the daily activities and health parameters of elderly patients is expanding, highlighting the importance of multidisciplinary research in biotechnology, chemistry, engineering and nursing.

Hybrid WARIMA-WANN algorithm for data prediction in bridge health monitoring system

Application of data-driven algorithm to model and predict the future trend of the structural health monitoring (SHM) data serves as important references for the future safety evaluation and decision-making of the bridges. An innovative hybrid WARIMA-WANN algorithm combining the Wavelet model, the autoregressive moving average (ARIMA) model and the artificial neural network (ANN) model is proposed to predict massive monitoring data in the bridge SHM system. The WPT serves as the decomposition function for the linear and nonlinear components of the hybrid model. The separated linear/nonlinear components are individually predicted with the ARIMA/ANN models and then combined to obtain the prediction values of the future data. The hybrid method is applied to predict the deflection values of a single-tower cable-stayed bridge. Multiple parameters in individual models are discussed and selected to increase the credibility of the prediction results for the example bridge. The results show that the proposed hybrid algorithm successfully captures the linear and nonlinear coupling relationships inside the SHM data from the frequency perspective of view and shows the best prediction performance when compared with the other four existing models. Different prediction steps obviously influence prediction performance due to the periodic nature of the bridge SHM data.

Gender-responsive monitoring and evaluation for health systems.

Gender-responsive monitoring and evaluation (M&E) for health and health systems interventions and programs is vital to improve health, health systems, and gender equality outcomes. It can be used to identify and address gender disparities in program participation, outcomes and benefits, as well as ensure that programs are designed and implemented in a way that is inclusive and accessible for all. While gender-responsive M&E is most effective when interventions and programs intentionally integrate a gender lens, it is relevant for all health systems programs and interventions. Within the literature, gender-responsive M&E is defined in different and diverse ways, making it difficult to operationalize. This is compounded by the complexity and multi-faceted nature of gender. Within this methodological musing, we present our evolving approach to gender-responsive M&E which we are operationalizing within the Monitoring for Gender and Equity project. We define gender-responsive M&E as intentionally integrating the needs, rights, preferences of, and power relations among, women and girls, men and boys, and gender minority individuals, as well as across social, political, economic, and health systems in M&E processes. This is done through the integration of different types of gender data and indicators, including: sex- or gender-specific, sex- or gender-disaggregated, sex- or gender-specific/disaggregated which incorporate needs, rights and preferences, and gender power relations and systems indicators. Examples of each of these are included within the paper. Active approaches can also enhance the gender-responsiveness of any M&E activities, including incorporating an intersectional lens and tailoring the types of data and indicators included and processes used to the specific context. Incorporating gender into the programmatic cycle, including M&E, can lead to more fit-for-purpose, effective and equitable programs and interventions. The framework presented in this paper provides an outline of how to do this, enabling the uptake of gender-responsive M&E.

Bound state continuum resonance transition in a shallow quantum well

Bound state continuum resonance transition in a shallow quantum well

PVDF/NiFe2O4 multiferroic composite membranes for dual mechanical and magnetic energy harvesting devices

A multiferroic composite membrane, combining PVDF piezoelectric polymer and nickel ferrite (NFO) nanofibers, was successfully fabricated and studied as an active material layer in multifunctional devices designed to harvest both mechanical and magnetic energy. Optimization of the manufacturing process ensured an even distribution of NFO fibers within the PVDF matrix, enhancing the crystallization of PVDF in the electroactive β phase. The resulting PVDF/NFO multiferroic films exhibited both piezoelectric and magnetic properties, along with a pronounced magnetoelectric (ME) effect. In a structure comprising Al/PVDF-NFO/PDMS/Al, the device operated as a piezoelectric generator (PEG) under a pressing force of 0.5 MPa, achieving a maximum output power density of 14.7 μW cm−12 with a peak-to-peak voltage of 12.2 V. When subjected to an AC magnetic field of 20 Oe at 50 Hz, the device functioned as a magneto-mechano-electrical (MME) generator, producing a sinusoidal waveform voltage of 486 mV. The cost-effective and easily integrable PVDF/NFO composite membrane presents promising opportunities for developing flexible, self-powered smart sensors for human health monitoring systems and implantable biomedical devices.

A hierarchical multistage holistic model for acoustic emission source monitoring in composites

Abstract This paper introduces a multistage smart structural health monitoring (SHM) model for carbon-fiber composites, with a focus on multiple types of acoustic emission (AE) source localization and classification. The SHM model uses time-frequency data from various AE events (such as tool drops, impact, and artificial debonding) across different zones of a composite structure. The SHM strategy demonstrates a robust smart monitoring of composites with high accuracy. Further, a hypothesis testing has been carried out that supports the superiority of a 2-stage identification process, revealing statistically significant higher accuracy and confidence intervals across all zones and AE source types. This research establishes a novel framework for solving a hierarchical multistage holistic damage source identification problem, offering robustness in identifying various damage scenarios and quantifying associated prediction uncertainties.

HOT Watch: IoT-Based Wearable Health Monitoring System

HOT Watch: IoT-Based Wearable Health Monitoring System

Advanced Predictive Structural Health Monitoring in High-Rise Buildings Using Recurrent Neural Networks

This study proposes a machine learning (ML) model to predict the displacement response of high-rise structures under various vertical and lateral loading conditions. The study combined finite element analysis (FEA), parametric modeling, and a multi-objective genetic algorithm to create a robust and diverse dataset of loading scenarios for developing a predictive ML model. The ML model was trained using a recurrent neural network (RNN) with Long Short-Term Memory (LSTM) layers. The developed model demonstrated high accuracy in predicting time series of vertical, lateral (X), and lateral (Y) displacements. The training and testing results showed Mean Squared Errors (MSE) of 0.1796 and 0.0033, respectively, with R2 values of 0.8416 and 0.9939. The model’s predictions differed by only 0.93% from the actual vertical displacement values and by 4.55% and 7.35% for lateral displacements in the Y and X directions, respectively. The results demonstrate the model’s high accuracy and generalization ability, making it a valuable tool for structural health monitoring (SHM) in high-rise buildings. This research highlights the potential of ML to provide real-time displacement predictions under various load conditions, offering practical applications for ensuring the structural integrity and safety of high-rise buildings, particularly in high-risk seismic areas.

Preparation and characterization of functional dual network hydrogel for motion monitoring in smart bra

The smart bra is a kind of sports underwear with providing health monitoring and comfort protection for women breast in sports. Developing smart sensing materials that combine comfort and functionality for sensing breast condition remains a challenge. Here, natural polysaccharide sodium alginate (SA), acrylamide (AM) monomer and chitosan (CS) and polypyrrole (PPy) in-situ polymerized from pyrole (Py) monomer were used as raw materials to successfully synthesize double network conductive hydrogel by two-step method. The results show that in the hydrogels, the entanglement and interpenetration of macromolecules produce toughness (tensile/compressive strength is ∼0.248 MPa/∼2.836 MPa), elasticity (tensile/compression elastic recovery rate is∼94.06 %/∼98.41 %) and durability (after 30 tensile/compression cycles conducting at 25 %, 50 % and 100 % constant strain keep at high recovery rate). The rich moisture content provides its adhesion (adhesion strength ∼4.7557 kPa) and flexibility (bending stiffness 1.0843 cN cm∼), presenting sufficient adhesion reliability and robustness to different substrates. The addition of PPy enables the hydrogel to have conductivity (6.77 × 10−2 S cm−1) and sensing performance (tensile/compression sensitivity is ∼1.94/∼1.43). The conductive hydrogel, as a flexible strain sensor, shows good adhesion, flexibility, elasticity and sensitivity in application of sports smart bra, can detect both the tensile behavior of skin and the compression state of tissue through its mechano-electricity, and accurately sense the changes of physiological parameters during movement. The conductive hydrogel with high elasticity and strength has broad application prospects in smart bra.

Quantitative assessment of cracks in concrete structures using active-learning-integrated transformer and unmanned robotic platform

Quantitative assessment of cracks in concrete bridges is crucial for structural health monitoring and digital twin. However, the training of crack segmentation models relies heavily on annotation resources, and their segmentation capabilities are often unsatisfactory in terms of the accuracy of boundary location of thin cracks encountered in practice. In this paper, an active-learning-integrated crack segmentation transformer (ACS-Former) framework is proposed to maximize segmentation performance with limited annotation resources. The two-branch ACS-Former includes (1) a feature pyramid transformer (FPT) for multi-scale crack segmentation and (2) boundary difficulty-aware active learning (BDAL) to select informative images for labeling and incorporation into FPT training. Additionally, an adhesive climbing robot is proposed for image collection of hard-to-access components of large bridges. The on-site operational feasibility and practicability of the proposed ACS-Former and climbing robot are demonstrated by field experiments performed on in-service bridges, including the quantification of cracks narrower than 0.2 mm, as required by engineering codes.

Impact of operator characteristics on landing gear ground manoeuvre occurrences

Abstract The variability in ground manoeuvre occurrences for aircraft landing gear is intrinsically linked to the airport geometries served by aircraft in-service and consequently, the cyclic loads that landing gear carry are driven by the route network and characteristics of aircraft operators. Currently, assumptions must be made when deriving fatigue load spectra for aircraft landing gear, which may fail to capture the operator characteristics, potentially leading to design conservatism. This paper presents the enhanced characterisation of ground turning manoeuvres within the Automatic Dependent Surveillance-Broadcast (ADS-B) trajectories for six narrow-body aircraft across a full-service carrier (FSC) and a low-cost carrier (LCC) fleet. The methodology presented within this paper employs ADS-B latitude and longitude information to overcome limitations of previous approaches, increasing the rate of correct manoeuvre identification within ADS-B trajectories to 77% of flights from the 50% rate achieved previously. When characterising the ground manoeuvres across 3,000 flights, significant differences in manoeuvre occurrences were observed between individual aircraft within the LCC fleet and between the FSC and LCC fleets. The occurrence of tight and pivot turns were shown to vary across the six aircraft with six and eight fatigue-critical turns being performed by the FSC and LCC fleet for every 10 flights performed. In addition, it was observed that the direction of fatigue critical turns is biased in specific directions, suggesting that individual main landing gear assemblies will accumulate fatigue damage at an increased rate, leading to greater justification for operator-specific spectra and structural health monitoring of aircraft landing gear.