Reliable Monitoring Research Articles

Wireless Body Sensor Networks for Real-Time Healthcare Monitoring: A Cost-Effective and Energy-Efficient Approach

Background: The continuous and accurate monitoring of physiological signals is critical for timely medical interventions, particularly in the context of chronic disease management and elderly care. The advent of Wireless Body Sensor Networks (WBSN) and Patient Health Monitoring Systems (PHMS) promises to revolutionize healthcare by integrating real-time data collection with advanced processing and communication technologies, enabling proactive medical responses through the Internet of Things (IoT). Methods: In this study, a WBSN-PHMS framework is proposed, leveraging a network of biomedical sensors to continuously monitor vital signs such as temperature and heart rate. The system includes a gateway node that wirelessly transmits sensor data to a cloud-based storage and processing system. Physiological data are processed in real-time using advanced algorithms to detect anomalies and trigger alerts. The health status is modeled using a function g(v,w(v),∅−1(v(w))−∂i(v,∂))g(v, w(v), \varnothing^{-1}(v(w)) - \partial_i(v, \partial))g(v,w(v),∅−1(v(w))−∂i?(v,∂)), which integrates patient-specific factors and real-time physiological states. The processed data are communicated efficiently through IoT platforms, ensuring that healthcare professionals receive timely alerts via mobile applications and specialized dashboards. Results: The WBSN-PHMS demonstrated high accuracy in detecting abnormal temperature and heart rate patterns, achieving detection rates of 98.61% and 99.43%, respectively. Additionally, the system improved overall patient health monitoring by 98.42%, offering a significant advancement in patient safety and healthcare efficiency. The cost-effectiveness of the system was established with a 96.21% improvement, while energy consumption was reduced by 23%, underscoring the sustainability of the technology. Conclusion: The WBSN-PHMS proves to be a reliable and efficient healthcare monitoring system that enhances patient outcomes through real-time data analysis and rapid medical responses. The system's ability to continuously monitor and analyze physiological signals in a non-intrusive manner positions it as a vital tool in modern medical practice, with the potential to revolutionize remote patient care and monitoring.

Machine Learning-Enabled Acoustic Emission Signal Processing for real-time crack growth monitoring in heterogeneous structures

Heterogeneous structures pose unique challenges for structural health monitoring, demanding advanced techniques for early detection of crack propagation. Acoustic Emission (AE) is a non-destructive testing technique that relies on the detection and analysis of stress-induced high-frequency acoustic waves. It is commonly used for identifying and monitoring the development of cracks, defects, or other structural anomalies. However, raw AE data is often noisy and contains background noise and interference, which make them not suitable for reliable real-time crack monitoring applications. This study integrates AE data with machine learning algorithms to provide a proactive and accurate system for continuous crack propagation monitoring. The performance of the proposed approach for features extraction to derive relevant information from the AE signals, data labeling for training machine learning models, and the deployment of these models for real-time monitoring, are discussed.

Towards reliability assessment of ultra-wideband microwave leakage monitoring in detecting stringer debondings in composite airframes

Ultra-wideband guided electromagnetic waves have been proposed recently for non-destructive testing and structural health monitoring applications. Among the multitude of possible microwave waveguides, recent works explored the concept of microwave leakage monitoring to detect stringer debondings in hollow carbon composite structures. The principle is to monitor the wave propagation characteristics within the cavity formed by the stringer and the airframe skin using transmission measurements. Changes in the transmission path, e.g. occurring from microwave leakage in the debonded region, can be assessed through a damage indicator approach. To proof the reliability of this concept, an experimental campaign was carried out according to the building block approach typically used in aeronautics. First, a small-scale specimen with a debonded stringer was investigated in the laboratory to proof the methodology. Then, a large-scale fuselage segment was used for technology demonstration enabling stringer debonding with increasing severity through static and fatigue loading. The paper shows that the microwave characteristics in the waveguide (stringer tunnel) is affected by the debonding conditions according to its severity and depth. In addition, extending the same approach on a number of test cases, the probability of detection was carried out showing the minimum defect size which can be identify reliably. This allows designing a reliable structural health monitoring system based on guided electromagnetic waves trapped in the tunnel.

Uncovering the Secret Clues: Using Stool Calprotectin to Improve Low Anterior Resection Syndrome after Surgery

Abstract Background: A majority of patients who undergo low rectal resection experience a frequent complication of low anterior resection syndrome (LARS). It is characterized by distressing bowel dysfunction such as urgency, frequency, and incontinence. Despite the high prevalence of LARS, its management remains challenging due to the lack of reliable diagnostic and monitoring tools. Objectives: This study investigates the relationship between stool calprotectin levels and LARS severity and evaluates the effectiveness of mesalamine treatment. Design: This prospective study involved 48 patients with low rectal cancer who had undergone resection with temporary ileostomy. The patients subsequently presented with LARS for over four weeks after ileostomy closure. Patient and Methods: LARS diagnosis was based on the LARS score, and stool calprotectin levels were measured at baseline and one month and six months post-ileostomy closure. Patients with elevated calprotectin levels received mesalamine treatment and dietary modifications. Statistical analyses were performed to determine the correlation between calprotectin levels and LARS scores using Pearson correlation analysis, linear regression analysis, paired t-test, and ANOVA. Main Outcome Measure: The predictive capacity for stool calprotectin for LARS was the main outcome variable. Sample Size: Forty-eight patients who underwent low rectal cancer resection were enrolled. Results: Among the study group, 28 were males and 20 were females. The mean age was 50 (9.998) years, 95% confidence interval CI = [17–78]) in both groups. The predictive value of calprotectin levels for LARS severity was high with β = 0.72, P-value < .001, and R² 0.55. The mean (SD) stool calprotectin level was 360 (6.282) mcg/g, which correlated with severe LARS symptoms (mean [SD] LARS score = 35 [2.449]). After six months, 60% of patients showed significant improvement (LARS scores <20), 30% had minor improvement (LARS scores 29–30), and 10% showed no improvement. Correlation analysis revealed a strong positive correlation between calprotectin levels and LARS scores (r = 0.75, P < .001). Conclusion: Stool calprotectin is demonstrated to be a promising biomarker for diagnosing and monitoring LARS. The findings highlight the association between LARS and high levels of stool calprotectin. However, the study had limitations of being a single-center study, comprising a small sample size, and not using a randomized clinical trial to evaluate the effectiveness of mesalamine. Conflicts of Interest: None.

MARS: The first line of defense for IoT incident response

The proliferation of Internet of Things (IoT) devices across homes, businesses, and industrial landscapes has significantly increased our capability to gather data and automate tasks. Despite their ubiquity, these devices are notably resource-constrained and frequently lack robust security defenses, presenting a substantial risk of intrusion and cyber threats. To address these concerns, we propose a novel anomaly-based host intrusion detection system specifically designed for IoT devices, titled MARS (Memory Anomaly Recognition System). MARS is designed to function as a crucial component in the incident response framework, acting as an early detection system for potential security breaches within an organization’s network or systems. The fundamental architecture of MARS leverages the device’s memory as a key indicator for monitoring system-level events. To enhance its security and integrity, MARS is embedded within a Trusted Execution Environment—a secure, hardware-isolated region of a microcontroller protected from untrusted software. This design choice not only makes MARS tamper-proof but also ensures reliable monitoring of the device’s memory. Deviations from established memory baselines, indicative of a security compromise, are detected through an anomaly detection algorithm hosted on a remote server. Our evaluation of the MARS prototype on STM32L562QEI6QU showed our proposed architecture can achieve decent scalability while maintaining trust, accuracy, and robustness of memory changes.

Prediction of directivity characteristics of piezoelectric macro-fiber composite interdigital transducers: a semi-analytical approach

The employment of guided waves for structural health monitoring applications has attracted substantial attention in recent years. Piezoelectric transducers are frequently employed in these systems for elastic wave generation and acquisition. Their dynamic properties, i.e., direction-dependent frequency characteristics, are an important feature for designing a reliable monitoring strategy. Although analysis of the transducer directivity pattern can be performed using finite element models, these usually tend to be computationally very expensive, especially, for parametric and optimization studies. In this paper, a semi-analytical methodology is proposed that can predict the far-field responses of complex transducers of arbitrary shape and structure mounted on elastic plates. The approach reported here couples analytical far-field predictions with a local FE model evaluating the traction field generated by the transducer. Thus, the FE model captures the near-field responses, while the analytical part predicts the far-field responses. The implementation of this semi-analytical method requires development of analytical and numerical frameworks. The former includes computation of dispersion curves of the inspected plate, the stress and displacement profiles of wave modes and the excitability curves, while the latter includes estimation of the traction field between the transducer and the plate using the FE model. Owing to the popularity of piezoelectric macro-fiber composite (MFC) interdigital transducers, in this paper, we apply the proposed method to simulate the directivity patterns of the MFC IDTs of different configurations. To validate the reliability of the proposed method the simulated patterns are compared with experimental results obtained using Laser Doppler Vibrometer (LDV).

Calibration of CAMS PM2.5 data over Hungary: a machine learning approach

Air pollution is a major environmental problem, and reliable monitoring of particulate matter (PM) concentrations is critical for assessing its impact on human health and the environment. The Copernicus Atmosphere Monitoring Service (CAMS) offers vital data on PM2.5 concentrations by applying a worldwide modelling system. This study compares in situ PM2.5 measurements and raw CAMS data at 0.1° × 0.1° resolutions for 2019 and 2020 in Hungary. It proposes a calibration method to improve the accuracy of CAMS PM2.5 data at the scale of air monitoring stations. In the study, the accuracy of the raw CAMS PM2.5 data is assessed based on the chosen air quality stations. Then, to improve the precision, we employed machine learning algorithms (LightGBM, Random Forest (RF), and Multiple Linear Regression (MLR)) for calibration. Initial assessment of the raw CAMS PM2.5 data showed positive hourly Spearman correlation coefficient values (SR between 0.64 and 0.87 for the 14 air quality stations used), indicating a positive relationship between the datasets but a systemic underestimation. Our findings highlight LightGBM as the most effective method, consistently demonstrating elevated correlation SR and coefficient of determination R2 values reaching up to 0.95 and 0.93, respectively, and very good RSR (Root mean square error ratio) and NSE (Nash-Sutcliffe Efficiency) values (lower than 0.5 and higher than 0.75 for RSR and NSE, respectively). In contrast, RF yields mixed results, and MLR exhibits variable performance. By correcting underestimation and lowering modelling biases, the calibrated PM2.5 data better matches ground-based observations, which can be promising for using the obtained model for accurate estimation at individual air monitoring stations.

IoT Based Irrigation and Fertigation System for Smart Smallholder Farming Application

Integrating Internet of Things (IoT) technology with irrigation and fertigation systems has transformed traditional farming methods by enabling reliable monitoring and control of critical environmental parameters. This project aims to design an automated irrigation and fertigation system as a prototype to reduce water wastage and monitor the pH level in the soil, temperature, soil dryness and detection of intruders. Sensors strategically placed throughout the system monitor and control these elements. This system has several important features, including the ability to automatically control water pumps to maintain proper hydration levels, detect intruders within a range of one to six meters of the plant by using the Blynk app, and monitor pH levels through the same app to ensure appropriate neutral levels. Automation and control features are also assessed to confirm that sensors, pumps, and other components precisely respond in line with predetermined requirements. This evaluation helps to determine the overall health of the farmed plants as well as the effectiveness of the system. Finally, the irrigation and fertilizing system for the targeted smallholder farming area has been developed. The resulting smart irrigation and fertigation system is not only a practical solution but also an effective alternative to modern agricultural methods.

Interlayer cross-linked MXene enables ultra-stable printed paper-based flexible sensor for real-time humidity monitoring

With an abundance of hydrophilic active sites present on the surfaces, MXene exhibits significant potential for application in the field of humidity monitoring. Nevertheless, achieving the high response intensity, excellent response sensitivity, structural stability, and oxidation resistance in the MXene-based humidity sensors remains a formidable challenge. Herein, we propose a humidity sensor made from the paper-based flexible sensor with copper electrode and polyethyleneimine/silver nitrate cross-linking modified MXene composite materials. The humidity sensor prepared in this work presents a satisfactory capacitance response rate (819.9 % in 97 % RH) and favorable sensitivity (6.9/11.9 s for response/recovery time). Meanwhile, the humidity sensor possesses outstanding structural stability (less than 3 % attenuation of the capacitance response after 100 bending cycles) and superior oxidation resistance. Moreover, the real-time humidity monitor assembled based on the humidity sensor has realized the effective monitoring of human skin humidity and dynamic respiration. Consequently, this study provides a viable approach for achieving reliable real-time humidity monitoring, demonstrating significant potential in the realms of intelligent actuators, sensing devices, and health care.

A step toward Molnupiravir, COVID-19 Antiviral, detection using Cauliflower-Like cobalt Sulfide-Decorated carbon nanotube modified glassy carbon electrodes

Inhibiting the replication of COVID-19 viruses, Molnupiravir (MOL) has drawn extensive attention as an efficient antiviral medication during the global coronavirus pandemic. Adjusting the antiviral drug dosage is at the leading edge of achieving the optimum therapeutic effects while avoiding unwanted side effects. Developing inexpensive, highly-sensitive analytical methods, benefiting from high reliability and fast response of prime significance in monitoring and quality control of MOL in various matrices. Herein, an innovative strategy is developed to fabricate high-performance MOL sensing platforms through a facile and scalable approach. Accordingly, multi-walled carbon nanotubes (MWCNTs) are first drop-casted on the surface of glassy carbon electrode. Afterward, cauliflower-like cobalt sulfide nanosheets are electrodeposited onto the MWCNT-casted electrode. The MOL-sensing ability of the prepared sensing platforms is evaluated using various electrochemical methods. Under the optimal experimental conditions, two linear responses within broad detection ranges of 0.5 – 16 µM and 16 – 90 µM are obtained using the linear sweep voltammetry technique. A detection limit of 0.16 µM with a maximum sensitivity of 0.63 µA µM−1 in parallel to a relative standard deviation of 6.3 % is also achieved. Having the merit of high MOL sensitivity compared to other coexist interferences in human body fluids, together with fast response time, long-term stability, excellent repeatability, and high reproducibility, makes the fabricated electrode promising for reliable, fast, and accurate MOL monitoring.

A Review of Subsidence Monitoring Techniques in Offshore Environments.

In view of the ever-increasing global energy demands and the imperative for sustainability in extraction methods, this article surveys subsidence monitoring systems applied to oil and gas fields located in offshore areas. Subsidence is an issue that can harm infrastructure, whether onshore or especially offshore, so it must be carefully monitored to ensure safety and prevent potential environmental damage. A comprehensive review of major monitoring technologies used offshore is still lacking; here, we address this gap by evaluating several techniques, including InSAR, GNSSs, hydrostatic leveling, and fiber optic cables, among others. Their accuracy, applicability, and limitations within offshore operations have also been assessed. Based on an extensive literature review of more than 60 published papers and technical reports, we have found that no single method works best for all settings; instead, a combination of different monitoring approaches is more likely to provide a reliable subsidence assessment. We also present selected case histories to document the results achieved using integrated monitoring studies. With the emerging offshore energy industry, combining GNSSs, InSAR, and other subsidence monitoring technologies offers a pathway to achieving precision in the assessment of offshore infrastructural stability, thus underpinning the sustainability and safety of offshore oil and gas operations. Reliable and comprehensive subsidence monitoring systems are essential for safety, to protect the environment, and ensure the sustainable exploitation of hydrocarbon resources.

Intraoperative Neurophysiological Monitoring in Tethered Cord Syndrome Surgery: Predictive Values and Clinical Outcome.

"Tethered cord syndrome" (TCS) refers to a congenital abnormality associated with neurological signs and symptoms. The aim of surgery is to prevent or arrest their progression. This study reports a retrospective case series of tethered cord syndrome surgeries, supported by intraoperative neurophysiological monitoring. The case series comprises 50 surgeries for tethered cord syndrome in which multimodal intraoperative neurophysiological monitoring was performed using motor evoked potentials (transcranial motor evoked potentials [TcMEPs]), tibial nerve somatosensory evoked potentials (TNSEPs), and pudendal-anal reflex (PAR). The intraoperative neurophysiological monitoring results are reported and correlated with clinical outcomes. Sensitivity, specificity, and negative predictive value were high for TcMEPs and TNSEPs, while PAR exhibited low sensitivity and positive predictive value but high specificity and negative predictive value. Fisher's exact test revealed a significant correlation between changes in TcMEPs, TNSEPs, and clinical outcome ( P < 0.000 and P = 0.049 respectively), but no correlation was detected between PAR and urinary/anal function ( P = 0.497). While TcMEPs and TNSEPs were found to be reliable intraoperative neurophysiological monitoring parameters during tethered cord syndrome surgery, PAR had low sensitivity and positive predictive value probably because the reflex is not directly related to bladder function and because its multisynaptic pathway may be sensitive to anesthetics. New onset muscle weakness and sensory deficits were related to postoperative changes in TcMEPs and TNSEPs, whereas changes in PAR did not predict bladder/urinary impairment. Urinary deficits may be predicted and prevented with other neurophysiological techniques, such as the bladder-anal reflex.

Ambient nano RF-Energy driven self-powered wearable multimodal real-time health monitoring

As the trend of population aging intensifies, the demand for continuous and efficient health monitoring for solitary elderly individuals and those with limited self-care capabilities is growing. Traditional wearable health monitoring devices primarily rely on battery power, which not only incurs high maintenance costs but also risks interruption of monitoring due to battery depletion. To address these issues, this paper introduces a self-powered flexible wearable monitoring device utilizing far-field Radio Frequency Energy Harvesting (RFEH) technology. This device powers integrated sensors and Bluetooth by harvesting RF energy from ambient Wi-Fi and other wireless signals, enabling real-time monitoring of the wearer's physical behaviour and health status with immediate feedback via mobile terminals. Under conditions of 100 cm distance and a power intensity of 0.8 dBm, the system can charge a 220 μF capacitor to 4.12 V within just 23.24 seconds, ensuring stable operation of the device. Moreover, the monitoring device is equipped with a low-power wireless sensor system capable of sampling up to 100 Hz, which accurately and promptly tracks and analyzes key health indicators such as walking, physiological activities, and respiratory status. This technology provides a reliable health monitoring solution for elderly individuals living alone and those with difficulties in self-care, significantly enhancing the effectiveness of remote medical services, improving their quality of life, and reducing the occurrence of emergency medical events. This research not only advances wearable device technology but also paves new paths for health management in an aging society.

Characterisation of a microelectrochemical biosensor for real-time detection of brain extracellular d-serine

A modified development protocol and concomitant characterisation of a first generation biosensor for the detection of brain extracellular d-serine is reported. Functional parameters important for neurochemical monitoring, including sensor sensitivity, O2 interference, selectivity, shelf-life and biocompatibility were examined. Construction and development involved the enzyme d-amino acid oxidase (DAAO), utilising a dip-coating immobilisation method employing a new extended drying approach. The resultant Pt-based polymer enzyme composite sensor achieved high sensitivity to d-serine (0.76 ± 0.04 nA mm−2. μM−1) and a low μM limit of detection (0.33 ± 0.02 μM). The in-vitro response time was within the solution stirring time, suggesting potential sub-second in-vivo response characteristics. Oxygen interference studies demonstrated a 1 % reduction in current at 50 μM O2 when compared to atmospheric O2 levels (200 μM), indicating that the sensor can be used for reliable neurochemical monitoring of d-serine, free from changes in current associated with physiological O2 fluctuations. Potential interference signals generated by the principal electroactive analytes present in the brain were minimised by using a permselective layer of poly(o-phenylenediamine), and although several d-amino acids are possible substrates for DAAO, their physiologically relevant signals were small relative to that for d-serine. Additionally, changing both temperature and pH over possible in vivo ranges (34–40 °C and 7.2–7.6 respectively) resulted in no significant effect on performance. Finally, the biosensor was implanted in the striatum of freely moving rats and used to monitor physiological changes in d-serine over a two-week period.

Improved bridge modal identification from vibration measurements using a hybrid empirical Fourier decomposition

Bridge health monitoring has been a prominent focus within the global engineering community. Bridge owners, stakeholders, and engineers face the formidable tasks of ensuring efficient monitoring, conducting reliable data analysis, interpreting data logically, and making timely decisions. With the increasing global infrastructure deficit, there is an ever-increasing need to develop reliable and economical bridge monitoring solutions. In this paper, a bridge condition assessment technique is proposed that can utilize the vibration data collected from the instrumented sensors and provide reliable system identification results. The proposed method develops a hybrid approach by integrating the Natural Excitation Technique (NExT) and Empirical Fourier Decomposition (EFD) to analyze ambient bridge vibration data and determine the modal parameters of the bridge. First, NExT is formulated to determine the cross-correlation functions of the bridge measurements, and then EFD is explored to decompose the signals into their monocomponents to identify the bridge modal parameters. The proposed methodology can overcome mode mixing and perform modal identification of a system with closely spaced frequencies and low energy modes. The estimated modal parameters such as bridge frequencies, mode shapes, and damping ratio are used for condition assessment of numerical, experimental and full-scale structures, including a short-span steel bridge located in Ontario, Canada. The results demonstrate that the proposed methodology can provide accurate and robust estimates of bridge modal parameters. Future research is reserved for real-time implementation of the proposed methodology for a wide range of civil structures.

Exploring the impact of tenure arrangements and incentives on sustainable forest use: Evidence from a framed-field experiment in Ethiopia

The types of tenurial arrangements and incentives appropriate for the sustainable management of common pool resources (CPRs), such as forests, remain a topic of debate. In this study, we aim to (i) investigate the extraction level of forest resources under short and long-term property rights, and (ii) evaluate the effectiveness of introducing mechanisms that leverage reputation and feelings of guilt in promoting cooperation among CPR users with short-term property rights to reduce over-harvesting. We develop a simple theoretical model to predict the optimal extraction level of a shared forest resource and validate the predictions using data from a framed field experiment conducted in rural Ethiopia. Our findings demonstrate that extraction levels under short-term property rights are higher compared to long-term property rights, aligning with the model predictions. Leveraging reputation and feelings of guilt is effective in bridging the gap in extraction intensity between short- and long-term property rights. However, as implementing reputation requires reliable monitoring that can be costly and challenging in the study context, we propose extending the duration of property rights over shared forest resources as a preferred strategy for curtailing over-extraction.

Surface temperature field real-time reconstruction of hot forging die based on 1DCNN

Accurate reconstruction of the die surface temperature field during the hot forging process is crucial for ensuring optimal control of die temperature and high-quality metal forgings. In this study, we propose a novel method that combines hot forging process parameters with a deep neural network to reconstruct the temperature field on the die surface in real-time. Firstly, a high-fidelity finite element model of the hot forging process is created and validated with a forging experiment. Secondly, temperature observation points are initially selected based on the simulated temperature distribution of the forging die, and the correlation coefficient method is used to optimize the layout of observation points to improve reconstruction accuracy and efficiency. Subsequently, multiple high-quality simulations with varying process parameters are conducted, generating a comprehensive dataset. The 1DCNN algorithm is then presented to produce a mapping from the input to the surface temperature field. Finally, the effectiveness of the surface temperature field reconstruction method is verified. The results indicate that the overall performance of the proposed 1DCNN algorithm is better than the traditional regression algorithms. In terms of reconstruction accuracy, it achieves a prediction accuracy within an absolute error of 3 °C for the outer surface points and within a relative error of 5 % for the cavity region. Regarding reconstruction efficiency, it requires an approximate execution time of 0.064 s for predicting a new sample. This paper presents a method for real-time reconstruction of the die surface temperature field, leveraging hot forging process parameters and temperature data from observation points. This approach enables reliable online monitoring of transient temperature variations on the hot forging die surface, offering valuable support for operational control and maintenance.

A bioadhesive pacing lead for atraumatic cardiac monitoring and stimulation in rodent and porcine models.

Current clinically used electronic implants, including cardiac pacing leads for epicardial monitoring and stimulation of the heart, rely on surgical suturing or direct insertion of electrodes to the heart tissue. These approaches can cause tissue trauma during the implantation and retrieval of the pacing leads, with the potential for bleeding, tissue damage, and device failure. Here, we report a bioadhesive pacing lead that can directly interface with cardiac tissue through physical and covalent interactions to support minimally invasive adhesive implantation and gentle on-demand removal of the device with a detachment solution. We developed 3D-printable bioadhesive materials for customized fabrication of the device by graft-polymerizing polyacrylic acid on hydrophilic polyurethane and mixing with poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) to obtain electrical conductivity. The bioadhesive construct exhibited mechanical properties similar to cardiac tissue and strong tissue adhesion, supporting stable electrical interfacing. Infusion of a detachment solution to cleave physical and covalent cross-links between the adhesive interface and the tissue allowed retrieval of the bioadhesive pacing leads in rat and porcine models without apparent tissue damage. Continuous and reliable cardiac monitoring and pacing of rodent and porcine hearts were demonstrated for 2 weeks with consistent capture threshold and sensing amplitude, in contrast to a commercially available alternative. Pacing and continuous telemetric monitoring were achieved in a porcine model. These findings may offer a promising platform for adhesive bioelectronic devices for cardiac monitoring and treatment.

Validation Scores to Evaluate the Detection Capability of Sensor Systems Used for Autonomous Machines in Outdoor Environments

The characterization of the detection capability assumes significance when the reliable monitoring of the region of interest by a non-contact sensor is a safety-relevant function. This paper introduces new validation scores that evaluate the detection capability of non-contact sensors intended to be applied to outdoor machines. The scores quantify, in terms of safety, the suitability of the sensor for the intended implementation in an environmental perception system of (highly) automated machines. This was achieved by developing an extension to the new Real Environment Detection Area (REDA) method and linking the methodology with the sensor standard IEC/TS 62998-1. The extension includes point-by-point and statistic-based error evaluation which leads to the Usability-Score, Availability-Score, and Reliability-Score. By applying the principle in the agricultural sector using ISO 18497 and linking this with data from a real outdoor test stand, it was possible to show that the validation scores offer a generic approach to quantify the detection capability and express this in a machine manufacturer-oriented manner. The findings of this study have significant implications for the advancement of safety-related sensor systems integrated into machines operating in complex environments. In order to achieve full implementation, it is necessary to define in the standards which score is required for each Performance Level (PL).

Broiler health monitoring technology based on sound features and random forest

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