Sensor System For Monitoring Research Articles

Optoelectronic dynamic memristor systems based on two-dimensional crystals

Abstract Optical modulation of resistive switching in an optoelectronic memristor allows it to be optically controlled at ultra-high speed and ultra-low power consumption. Optical memristive systems with memory elements switched by electromagnetic radiation can be used in optically reconfigurable and tunable neural networks for neuromorphic computing and brain-inspired artificial intelligence systems. Two-dimensional (2D) crystals with unique electrical and optical properties demonstrate tremendous potential in the creation of highly efficient information and sensor systems for real-time data monitoring and processing. In this paper, we consider optoelectronic structures based on graphene, graphene oxide, and molybdenum disulfide for dynamic memristive signal processing. 2D optoelectronic memristor structures exhibit multiple states, which can be monitored in a wide range of optical excitations and used as sensors for pattern recognition and image processing. An optoelectronic dynamic memristive structure with quantum dots (QDs), controlled by ultrafast photoinduced structural phase transitions, is promising for creating information systems with stochastic data processing. The phase transition, controlled by charge and temperature, results in tunable periodic oscillations due to two state variables that exhibit chaotic behavior similar to that which occurs in a biological neuron network.

Assessment of the Piezoelectric Response of an Epoxy Resin/SbSINanowires Composite Filling FDM Printed Grid.

This paper shows a piezoelectric response from an innovative sensor obtained by casting epoxy-SbSI (antimony sulfoiodide) nanowires nanocomposite to a grid structure printed using a fuse deposition modeling (FDM) method. The grid is shown to be a support structure for the nanocomposite. The applied design approach prospectively enables the formation of sensors with a wide spectrum of shapes and a wide applicability. The voltage signal obtained as a result of the piezoelectric effect reached 1.5V and 0.5V under a maximum static stress of 8.5 MPa and under a maximum dynamic stress of 22.3 kPa, respectively. These values are sufficient for potential application in sensor systems. The effect of a systematic increase in the voltage signal with subsequent cycles was also observed, which similarly allows the use of these sensors in monitoring systems for structures exposed to unfavorable cyclical loads. The obtained results also show that the piezoelectric signal improves with increase in strain rate.

A Study of Accelerometer and Gyroscope Measurements in Physical Life-Log Activities Detection Systems.

Nowadays, wearable technology can enhance physical human life-log routines by shifting goals from merely counting steps to tackling significant healthcare challenges. Such wearable technology modules have presented opportunities to acquire important information about human activities in real-life environments. The purpose of this paper is to report on recent developments and to project future advances regarding wearable sensor systems for the sustainable monitoring and recording of human life-logs. On the basis of this survey, we propose a model that is designed to retrieve better information during physical activities in indoor and outdoor environments in order to improve the quality of life and to reduce risks. This model uses a fusion of both statistical and non-statistical features for the recognition of different activity patterns using wearable inertial sensors, i.e., triaxial accelerometers, gyroscopes and magnetometers. These features include signal magnitude, positive/negative peaks and position direction to explore signal orientation changes, position differentiation, temporal variation and optimal changes among coordinates. These features are processed by a genetic algorithm for the selection and classification of inertial signals to learn and recognize abnormal human movement. Our model was experimentally evaluated on four benchmark datasets: Intelligent Media Wearable Smart Home Activities (IM-WSHA), a self-annotated physical activities dataset, Wireless Sensor Data Mining (WISDM) with different sporting patterns from an IM-SB dataset and an SMotion dataset with different physical activities. Experimental results show that the proposed feature extraction strategy outperformed others, achieving an improved recognition accuracy of 81.92%, 95.37%, 90.17%, 94.58%, respectively, when IM-WSHA, WISDM, IM-SB and SMotion datasets were applied.

Wearable Sensors for Monitoring and Preventing Noncommunicable Diseases: A Systematic Review

Ensuring healthy lives and promoting a healthy well-being for all at all ages are listed as some of the goals in Agenda 2030 for Sustainable Development. Considering that noncommunicable diseases (NCDs) are the leading cause of death worldwide, reducing the mortality of NCDs is an important target. To reach this goal, means for detecting and reacting to warning signals are necessary. Here, remote health monitoring in real time has great potential. This article provides a systematic review of the use of wearable sensors for the monitoring and prevention of NCDs. In addition, this article not only provides in-depth information about the retrieved articles, but also discusses examples of studies assessing warning signals that may result in serious health conditions, such as stroke and cardiac arrest, if left untreated. One finding is that even though many good examples of wearable sensor systems for monitoring and controlling NCDs are presented, many issues also remain to be solved. One major issue is the lack of testing on representative people from a sociodemographic perspective. Even though substantial work remains, the use of wearable sensor systems has a great potential to be used in the battle against NCDs by providing the means to diagnose, monitor and prevent NCDs.

Toward Trust in Internet of Things Ecosystems: Design Principles for Blockchain-Based IoT Applications

The Internet of Things (IoT) describes the concept of physical objects equipped with identifying, sensing, networking, and processing capabilities being connected to the Internet. Architectures for the IoT typically rely on transmitting data to centralized cloud servers for processing. Although cloud services are supposed to enhance the IoT in storage, computation, and communication capabilities, this approach often generates isolated data silos and requires trust in third parties operating the cloud servers, which become single point of failure. In addition, centralized cloud-based applications lack transparency and allow for undetected manipulation and concealment of IoT data. To overcome these downsides, we develop and evaluate a blockchain-based IoT sensor data logging and monitoring system, employing a design science research approach. In this article, we show that such systems should provide modularity, data parsimony, and availability in addition to domain-specific principles. The prototype improves data integrity and availability but uncovers challenges, such as high operating costs through smart contract computation fees. Furthermore, semistructured interviews with practitioners allowed us to derive insights for developing blockchain-based IoT ecosystems and reveal that cooperation with organizations is key for transferring solutions into production. We contribute to the IoT knowledge base by providing design principles as well as managerial and technological recommendations.

Sensitivity Analysis and Epidemiologic Triangulation

Epidemiologic triangulation integrates evidence from a variety of studies that have unrelated sources of bias. Sensitivity analysis assesses how a conclusion would change if assumptions were relaxed. Causal interpretations of associations between measures of health and air quality require a non-confounding assumption. Exposures are not randomly assigned nor selected, but with integrated sensor monitoring systems and annotation a key confounding parameter can be estimated. This talk introduces a novel statistical methodology for causal inference based on data transport and collaboration between exposure scientists and environmental epidemiologists.

Tool wear estimation in turning of Inconel 718 based on wavelet sensor signal analysis and machine learning paradigms

In the last years, hard-to-machine nickel-based alloys have been widely employed in the aerospace industry for their properties of high strength, excellent resistance to corrosion and oxidation, and long creep life at elevated temperatures. As the machinability of these materials is quite low due to high cutting forces, high temperature development and strong work hardening, during machining the cutting tool conditions tend to rapidly deteriorate. Thus, tool health monitoring systems are highly desired to improve tool life and increase productivity. This research work focuses on tool wear estimation during turning of Inconel 718 using wavelet packet transform (WPT) signal analysis and machine learning paradigms. A multiple sensor monitoring system, based on the detection of cutting force, acoustic emission and vibration acceleration signals, was employed during experimental turning trials. The detected sensor signals were subjected to WPT decomposition to extract diverse signal features. The most relevant features were then selected, using correlation measurements, in order to be utilized in artificial neural network based machine learning paradigms for tool wear estimation.

Study of surface topography detection and analysis methods of direct laser deposition 24CrNiMo alloy steel

The precise laser displacement sensor monitoring system during direct laser deposition process was established. The two-dimensional and three-dimensional topography images were acquired through data analysis and point cloud reconstruction method. The results of monitoring system show that with the increasing of scanning speed from 2 mm/s to 6 mm/s, the width of single track deposition kept stable due to the laser energy distribution, and the height of deposition decreased from 1.59 mm to 0.33 mm. The relationship between scanning speed and deposited 24CrNiMo alloy topography was established through data fitting. Deposition surface convex and concave defects can be distinguished through the feature of three dimensional images, uneven surface mainly existed in the deposition sides, which was represented by blank zone of acquired images. With the scanning speed increasing, the surface quality of deposited samples improved while the depositing efficiency decreased. The formation of surface defects is related to the solidification speed of molten pool, the phase structure of single track deposition was martensite owing to the high solidification speed, and the martensite has strain along the block boundary. The solidification rate increased with scanning speed increased, which led to the less time in forming surface defects.

Preocular sensor system for concurrent monitoring of glucose levels and dry eye syndrome using tear fluids.

The present study demonstrated a noninvasive preocular sensor system for the concurrent monitoring of diabetes and one of its prevalent complications, dry eye syndrome (DES), using tear fluids. Two distinct sensors, i.e., the glucose and DES sensors, were prepared and encased together in a single housing unit to produce the sensor system, and the tip was designed to be in contact with the eye surface noninvasively to collect and deliver tear fluid to the sensors. The glucose sensor was modified from a commercially available electrochemical sensor to allow for the measurement of glucose concentrations, even in a small amount of collected tear fluid. The DES sensor was equipped with a microchannel spaced with two parallel electrodes to determine the amount of collected tear fluid. In vivo experimental results revealed that with the collected tear fluid of about 0.6–1.0 μl, the sensor system estimated the blood glucose concentrations with acceptable accuracy compared with that of the glucometer in clinical use. The DES condition in animals was diagnosed with high sensitivity (91.7%) and specificity (83.3%).

CO2 capture and storage monitoring based on remote sensing techniques: A review

Carbon Dioxide (CO2) has been causing harmful effects to the environment and even to the whole earth. Nowadays, it is the leading cause of global warming and the greenhouse effect that poses a serious threat to the welfare of the planet earth and its inhabitant plants, animals, and humans alike. After years of theoretical demonstration and field tests, carbon capture and storage (CCS) has been proven to be an effective approach to reduce atmospheric CO2 and has made an important contribution to reducing global greenhouse gas emissions. The monitoring of storage site leakage is an important link to ensure the effective implementation of the project such as injection of CO2 into deep Earth to drive the oil and gas to shallow depth of Earth for increasing the oil/gas productions and meanwhile to protect the surrounding environment and personnel from the harmful effects of CO2 leakage. As a new leakage monitoring method, remote sensing has been successfully applied in many storage sites with its advantages of extensive scope of observations, non-contact and long-term cost-effective monitoring. In this study, various remote sensing monitoring technologies are summarized and discussed in terms of their advantages and disadvantages in applications, their development trend in the future, and promising remote sensing monitoring system for the safety of different CO2 injection stages.

Continuous Forest Monitoring Using Cumulative Sums of Sentinel-1 Timeseries

Forest degradation is recognized as a major environmental threat on a global scale. The recent rise in natural and anthropogenic destruction of forested ecosystems highlights the need for developing new, rapid, and accurate remote sensing monitoring systems, which capture forested land transformations. In spite of the great technological advances made in airborne and spaceborne sensors over the past decades, current Earth observation (EO) change detection methods still need to overcome numerous limitations. Optical sensors have been commonly used for detecting land use and land cover changes (LULCC), however, the requirement of certain technical and environmental conditions (e.g., sunlight, not cloud-coverage) restrict their use. More recently, synthetic aperture radar (SAR)-based change detection approaches have been used to overcome these technical limitations, but they commonly rely on static detection approaches (e.g., pre and post disturbance scenario comparison) that are slow to monitor change. In this context, this paper presents a novel approach for mapping forest structural changes in a continuous and near-real-time manner using dense Sentinel-1 image time-series. Our cumulative sum–spatial mean corrected (CUSU-SMC) algorithm approach is based on cumulative sum statistical analysis, which allows the continuous monitoring of radar signal variations, derived from forest structural change. Taking advantage of the high data availability offered by the Sentinel-1 (S-1) C-band constellation, we used an S-1 ground range detected (GRD) dual (VV, VH) polarization timeseries, formed by a total of 84 images, to monitor clear-cutting operations carried out in a Scottish forest during 2019. The analysis showed a user’s accuracy of 82% for the (conservative) detection approach. The use of a post-processing neighbor filter increased the detection performance to a user’s accuracy of 86% with an overall accuracy of 77% for areas of a minimum extent of 0.4 ha. To further validate the detection performance of the method, the CUSU-SMC change detector was tested against commonly-used pairwise change detection approaches for the same period. These results emphasize the capabilities of dense SAR time-series for environmental monitoring and provide a useful tool for optimizing national forest inventories.

Quasi-Distributed Fiber Optic Temperature and Humidity Sensor System for Monitoring of Grain Storage in Granaries

Mildew is an important issue in grain storage, due to the very expensive losses that can arise due to grain spoilage by mildew. Monitoring the conditions which allow mildew to develop is important and current monitoring technology cannot readily, or safely, be used for internal humidity monitoring of the very large quantities of grain kept in storage. In this work, a quasi-distributed, tailored fiber optic temperature and humidity sensor system suitable for use in the large granaries has been developed. Dust from the grain is a major issue and the sensor system copes well with this. Various sensor designs have been evaluated and building on prior work, a design with a humidity sensitivity of 5.8pm/%RH, a measurement error of 2%RH, and response time of 4.3 minutes, which is more than adequate for the applications discussed has been developed. Field-testing of the sensor system was carried out at a major storage facility in China, monitoring over an extended period of 4 months, giving results consistent with the outputs from other point sensors currently used in the granary. The fiber optic sensor system developed shows the additional advantages that as a quasi-distributed system, it can be used to record the temperature and humidity distribution across a longitudinal section of the large size of the grain pile, in real time, reflecting the changes in the internal moisture content. The results obtained showed the sensor system has broad market application prospects in this and other important areas of agricultural and food storage.

On Providing an Assessment Monitoring System for Especially Essential Structures

The article deals with the assessment problems of especially essential structures. Increased demands on prevention of emergency situations and minimizing the consequences in the event of their occurrence require constant determination of especially essential structures condition. Achieving the goal of reliability and continuity of information is possible by coating the structure surface by a layer of electroconductive concrete, working as a monitoring system sensor. The study of the electrical properties of concrete was performed using the voltmeter – ammeter scheme. After the measurements had been made, the conditional electrical resistance of the electrode pair was calculated. The analysis of the above dependencies found that the change in the electrical resistance of the material from its stress approaches the linear law at lower values of W/C over a larger section of the studied interval. Processing of the obtained data showed that the measurement results were significantly affected by the shape and size of the electrodes used during the experiments.

Multifunctional Smart Ball Sensor for Wireless Structural Health Monitoring in a Fire Situation.

A variety of sensor systems have been developed to monitor the structural health status of buildings and infrastructures. However, most sensor systems for structural health monitoring (SHM) are difficult to use in extreme conditions, such as a fire situation, because of their vulnerability to high temperature and physical shocks, as well as time-consuming installation process. Here, we present a smart ball sensor (SBS) that can be immediately installed on surfaces of structures, stably measure vital SHM data in real time and wirelessly transmit the data in a high-temperature fire situation. The smart ball sensor mainly consists of sensor and data transmission module, heat insulator and adhesive module. With the integrated device configuration, the SBS can be strongly attached to the target surface with maximum adhesion force of 233.7-N and stably detect acceleration and temperature of the structure without damaging the key modules of the systems even at high temperatures of up to 500 °C while ensuring wireless transmission of the data. Field tests for a model pre-engineered building (PEB) structure demonstrate the validity of the smart ball sensor as an instantly deployable, high-temperature SHM system. This SBS can be used for SHM of a wider variety of structures and buildings beyond PEB structures.

Potential of Ambient Sensor Systems for Early Detection of Health Problems in Older Adults.

Background: Home monitoring sensor systems are increasingly used to monitor seniors in their apartments for detection of emergency situations. The aim of this study was to deliver a proof-of-concept for the use of multimodal sensor systems with pervasive computing technology for the detection of clinically relevant health problems over longer time periods.Methods: Data were collected with a longitudinal home monitoring study in Switzerland (StrongAge Cohort Study) in a cohort of 24 old and oldest-old, community-dwelling adults over a period of 1 to 2 years. Physical activity in the apartment, toilet visits, refrigerator use, and entrance door openings were quantified using a commercially available passive infrared motion sensing system (Domosafety S.A., Switzerland). Heart rate, respiration rate, and sleep quality were recorded with the commercially available EMFIT QS bed sensor device (Emfit Ltd., Finland). Vital signs and contextual data were collected using a wearable sensor on the upper arm (Everion, Biovotion, Switzerland). Sensor data were correlated with health-related data collected from the weekly visits of the seniors by health professionals, including information about physical, psychological, cognitive, and behavior status, health problems, diseases, medication, and medical diagnoses.Results: Twenty of the 24 recruited participants (age 88.9 ± 7.5 years, 79% females) completed the study; two participants had to stop their study participation because of severe health deterioration, whereas two participants died during the course of the study. A history of chronic disease was present in 12/24 seniors, including heart failure, heart rhythm disturbances, pulmonary embolism, severe insulin-dependent diabetes, and Parkinson's disease. In total, 242,232 person-hours were recorded. During the monitoring period, 963 health status records were reported and repeated clinical assessments of aging-relevant indicators and outcomes were performed. Several episodes of health deterioration, including heart failure worsening and heart rhythm disturbances, could be captured by sensor signals from different sources.Conclusions: Our results indicate that monitoring of seniors with a multimodal sensor and pervasive computing system over longer time periods is feasible and well-accepted, with a great potential for detection of health deterioration. Further studies are necessary to evaluate the full range of the clinical potential of these findings.

Physiological Signal Monitoring for Identification of Emotional Dysregulation in Children.

Children, particularly those with atypical or delayed development, have a reduced ability to self-regulate their emotions and behaviour. After a number of anxiety or stress provoking events, this reduced regulatory ability can result in a meltdown. Extrinsic signals of an impending meltdown are often recognised and acted on by clinicians or parents. These external indications are also accompanied by internal physiological changes, such as increase in heart rate, skin electrodermal activity, and skin temperature. These physiological signals may be used to predict impending meltdown events and facilitate earlier and effective carer intervention, especially in complex management cases. We present a preliminary study using a wearable sensor system for continuous monitoring of physiological signals to measure and predict emotional changes in school-aged children. Our models are able to correctly classify the behavioural state of a child with 68% mean global model accuracy and up to 85% for person-dependent models. Prediction of emotion and identification of impending meltdowns will potentially assist parents, carers, teachers and clinicians to manage stress and problem behaviours before they escalate, and support self-management strategies throughout the variety of normal daily life.

IEpilepsy monitoring and alerting system using machine learning algorithm and WHMS

Advances in wireless electronics devices have led to easy design and develop of wearable sensor systems for health monitoring. These wireless sensors have been considered as one of the most important technologies that can change the future and has garnered lots of attention in the scientific community and the industry during the last years. These devices consist of small battery with limited computation and radio communication capabilities. These wireless sensor systems has become essential in such domains as industrial operations, health care, environmental infrastructure and research and development. Accelerometer sensor added with these wireless devices value to automatically detect seizures in temporal lobe epilepsy patients. The accelerometer sensor that is used in the wireless device is used to calculate the vibration threshold developed in the body. After calculating the threshold with various thresholds factors that has occurred in the body using seizure detection algorithm the device sends alerts to emergency contacts. In the future, we’ll see the mixing of a huge array of wireless networks into existing specialised medical technology. The aim of this paper is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements.

A Biomechanical Energy Based Wearable Sensor System for Real Time Human Gait Phase Detection and Postoperative Trauma Monitoring

Real time sensitive monitoring of gait phase has important applications for medical treatment and diagnostics. Time periods composing walking phase of gait can be clinically meaningful parameters to reveal differences between normal and pathological gait. In this study, biomechanical energy harvesting have been reported as a feasible solution for directly driving wearable sensors for gait phase monitoring. This not only can save the original space of battery in the sensors but also can avoid environmental pollution caused by waste batteries. Here, we have developed a flexible and wearable sensor with a broader range for pressure detection using triboelectric nanogenerators (TENG) to acquire human walking gait phase signals. According to previous studies, if people have trauma on their foot, the walking gait phase will become unusual compared to normal people. In addition, fewer cases of trauma like Anterior Cruciate Ligament Tear and Ankle muscle injury have also been analyzed with the developed sensor systems. The obtained time point of gait phase signal and the amplitude of peak can help patient understand their recovery situation precisely. We believed this wearable gait phase system not only expands the applications of biomechanical energy harvesters for wearable sensors, but also provides possible approaches for wearable human motion monitoring which is of great importance in many fields such as sports science and medical diagnostics.

Unobtrusive Health Monitoring in Private Spaces: The Smart Vehicle.

Unobtrusive in-vehicle health monitoring has the potential to use the driving time to perform regular medical check-ups. This work intends to provide a guide to currently proposed sensor systems for in-vehicle monitoring and to answer, in particular, the questions: (1) Which sensors are suitable for in-vehicle data collection? (2) Where should the sensors be placed? (3) Which biosignals or vital signs can be monitored in the vehicle? (4) Which purposes can be supported with the health data? We reviewed retrospective literature systematically and summarized the up-to-date research on leveraging sensor technology for unobtrusive in-vehicle health monitoring. PubMed, IEEE Xplore, and Scopus delivered 959 articles. We firstly screened titles and abstracts for relevance. Thereafter, we assessed the entire articles. Finally, 46 papers were included and analyzed. A guide is provided to the currently proposed sensor systems. Through this guide, potential sensor information can be derived from the biomedical data needed for respective purposes. The suggested locations for the corresponding sensors are also linked. Fifteen types of sensors were found. Driver-centered locations, such as steering wheel, car seat, and windscreen, are frequently used for mounting unobtrusive sensors, through which some typical biosignals like heart rate and respiration rate are measured. To date, most research focuses on sensor technology development, and most application-driven research aims at driving safety. Health-oriented research on the medical use of sensor-derived physiological parameters is still of interest.

A Biomechanical Energy Based Wearable Sensor System for Real Time Human Gait Phase Detection and Postoperative Trauma Monitoring

Real time sensitive monitoring of gait phase has important applications for medical treatment and diagnostics. Time periods composing walking phase of gait can be clinically meaningful parameters to reveal differences between normal and pathological gait. In this study, biomechanical energy harvesting have been reported as a feasible solution for directly driving wearable sensors for gait phase monitoring. This not only can save the original space of battery in the sensors but also can avoid environmental pollution caused by waste batteries. Here, we have developed a flexible and wearable sensor with a broader range for pressure detection using triboelectric nanogenerators (TENG) to acquire human walking gait phase signals. According to previous studies, if people have trauma on their foot, the walking gait phase will become unusual compared to normal people. In addition, fewer cases of trauma like Anterior Cruciate Ligament Tear and Ankle muscle injury have also been analyzed with the developed sensor systems. The obtained time point of gait phase signal and the amplitude of peak can help patient understand their recovery situation precisely. We believed this wearable gait phase system not only expands the applications of biomechanical energy harvesters for wearable sensors, but also provides possible approaches for wearable human motion monitoring which is of great importance in many fields such as sports science and medical diagnostics.