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Swing yarders in running skyline configuration using either grapples or chokers represent the most common configuration for cable yarding in coastal British Columbia. In this context, whole-tree logging and short work cycles lead to heavy and repeated loads applied to the trees and stumps when used as anchors for the rigging cables. Moreover, increased harvesting of second-growth forest stands leads to the unavailability of large and safe trees, thus introducing new challenges to identify suitable anchors and potentially increases safety risks of cable yarding operations. The present study aimed to collect evidence of the mechanical response provided by anchors for typical cable yarders used in second-growth harvesting in coastal British Columbia, and test the suitability of innovative techniques for the stability assessment based on the relationship between the anchor root-plate rotations and the related applied tensile forces. A conspicuous dataset could be derived from all the surveys, storing anchor rotations from a total of 1522 work cycles of which 1224 work cycles included also tensions measurements. The methodological approach was proven effective for monitoring different rigging configurations giving proof that repeated loading affects the stability of a tree/stump which can rapidly change over few hours of active yarding operations. Acquired data proved also that comparing theoretical failure limits with anchor rotations could be a valid approach however a considerable amount of species-specific data from tree pulling tests is required. [ABSTRACT FROM AUTHOR]

One of the most hazardous pollutants of natural water resources is lead -Pb (II)- which poses a significant threat to human health and environmental safety. The accumulation of this heavy metal in an organism affects a number of systems and is particularly dangerous for children. At low levels of intake over short periods, it induces diarrhea, abdominal pain, and renal damage, with the potential for fatal outcomes in extreme cases. The principal sources of lead pollution are industries, coal-fired power plants and motor vehicles. In response to the critical demand for effective lead detection, researchers have developed advanced Micro-Electromechanical Systems (MEMS) piezoresistive cantilever sensors that make use of the chelating properties of Ethylenediaminetetraacetic Acid (EDTA) and the superior electrical properties of reduced Graphene Oxide (rGO). It has been proven that this composite can be effectively immobilized on a MEMS cantilever surface, enabling the selective removal of Pb (II) ions from wastewater. This adsorption process exerts stress on the surface of the cantilever, resulting in variations in resistance that can subsequently be measured. A sensitive and selective MEMS piezoresistive cantilever sensor for Pb (II) has been developed, offering significant potential as a lead monitoring tool in water samples. The sensor demonstrated high sensitivity and selectivity, with a detection limit of 1 ppb and a linear response range of 10-100 ppb. This novel approach has the potential to significantly enhance pollution monitoring and provide substantial benefits for public health by enabling real-time, on-site mapping of lead contamination across aqueous environments. This technological advancement in the environmental surveillance domain offers a new perspective on the safety of water and the reduction of potential health hazards associated with lead consumption. [ABSTRACT FROM AUTHOR]

MEMS devices are more and more commonly used as sensors, actuators, and microfluidic devices in different fields like electronics, opto-electronics, and biomedical engineering. Traditional fabrication technologies cannot meet the growing demand for device miniaturisation and fabrication time reduction, especially when customised devices are required. That is why additive manufacturing technologies are increasingly applied to MEMS. In this review, attention is focused on the Italian scenario in regard to 3D-printed MEMS, studying the techniques and materials used for their fabrication. To this aim, research has been conducted as follows: first, the commonly applied 3D-printing technologies for MEMS manufacturing have been illustrated, then some examples of 3D-printed MEMS have been reported. After that, the typical materials for these technologies have been presented, and finally, some examples of their application in MEMS fabrication have been described. In conclusion, the application of 3D-printing techniques, instead of traditional processes, is a growing trend in Italy, where some exciting and promising results have already been obtained, due to these new selected technologies and the new materials involved. [ABSTRACT FROM AUTHOR]

Sensor-based monitoring of bridges has the potential to be an important tool to supplement visual inspection. Monitoring can provide quantitative data to evaluate the condition of bridge components (e.g. bearings and expansion joints) and to inform operation and maintenance decisions. However, the use of sensor systems to monitor bridges is often limited by cost. This paper presents the design, development, and field implementation of a low-cost micro-electromechanical systems (MEMS) and Internet of things (IoT)-based system to measure bridge bearing movement. The developed system uses accelerometers and converts changes in gravitational acceleration to longitudinal bearing displacement. The monitoring system uses a hybrid wired/wireless approach, in which the sensing nodes are wired to a gateway node from which data is transmitted to the cloud. Power is provided by means of a single battery that is charged using a solar panel. To evaluate the system performance in the field, it was installed on the Waaban Crossing in Kingston, Canada. Results of the study showed that the proposed system was capable of measuring movement of the bridge at a cost that was significantly less than a commercial monitoring system. Limitations of the system, cost of installation, and calibration of the sensors are also discussed. [ABSTRACT FROM AUTHOR]

The article describes problems related to the construction of an automatic system for teaching human motor activities. Teaching these activities in rehabilitation, sports, and professional work is of great importance in both social and individual dimensions. The prospect of using automated systems is therefore highly significant. The system can use signals from any motion sensors, e.g., cameras or MEMS (Micro-Electro-Mechanical Systems) inertial sensors. A significant problem is real-time signal analysis. In the system presented, this analysis involves a classification process. It enables the selection of an optimal motor learning algorithm for a given situation. The learner is provided with information about required movement corrections through haptic devices. The primary aim of the research described in the article is to identify key features of classification methods that ensure the construction of an effective teaching system. To achieve this goal, three classification methods were statistically tested, namely: a method using CNN (Convolutional Neural Network), a minimum distance method, and a method based on hidden Markov models. The main result of the study is the statement that the key feature of the methods is their interpretability. This property enables the efficient transfer of knowledge from experts to the system and facilitates its improvement. Based on the research conducted, directions for further development of the system can be determined. The most important of them are developing software tools to help explain the work of minimum distance algorithms and creating a collection of CNN configurations for typical motor learning tasks.

In this paper integration of Micro Electro Mechanical Systems (MEMS) based Inertial Measurement Unit (IMU) and Global positioning System are used for data logging. This paper presents the design and implementation of a data logging system for an Unmanned Aerial Vehicle. The main focus is designing a prototype system for tracking purposes with image-capturing capabilities. The purpose is to develop a recorder that can provide a complete record of the parameters in case of surveillance. The data logger can also be used to determine the cause of a flight crash. The results are gathered to improve the quality of measurements. And also the quality of the sensor's measurements can be improved with sophisticated filtering techniques. This filter was designed using the Mahony filter which provides efficient and effective solutions for IMU. This filter is computationally efficient and requires fewer mathematical computations to produce desired results at lower sampling rates. Additionally, the pitch, roll, and yaw stabilization of the device must be done when the system enters abnormal conditions. However, altitude, camera angle, and motion blur make it a more challenging task.

Incipient motion has been a topic of investigation by researchers, engineers and scientists for more than a century. The main approach for studying sediment entrainment has been the static approach that uses temporal and spatial averaged flow parameters like bed shear stress and stream power to link them indirectly to sediment entrainment. Recent research outputs have shed light on the important role of turbulent fluctuations in the sediment transport process. It is suggested that the approach of using temporal and spatial averaged parameters fails to account for the dynamic and probabilistic nature of the entrainment process, as inherited by flow turbulence. This has led to the introduction of the only dynamic criteria in the literature for studying sediment entrainment, namely the impulse and energy criteria. These criteria take into account both the magnitude and duration of the turbulent flow event used for assessing the conditions that can result in sediment entrainment. In light of this, this work aims to assess whether there is a trend in terms of the type of flow structures that occur in sequence before and after the occurrences of the flow impulses that have resulted in the coarse particle's entrainment. To achieve this, we conducted a well-controlled laboratory experiment to investigate the incipient motion of a 7 cm diameter instrumented particle. Five runs of the experiment were performed at flowrates close to the threshold of motion. The instrumented particle was equipped with micro-electro-mechanical sensors (MEMS) to accurately measure its inertial dynamics and detect motion. The sensors recorded entrainment events, and these events were stochastically linked to the impulses occurring for the tested flow conditions. Quadrant analysis was used to investigate the type of flow structures that occurred before, during and after the occurrence of quadrant events with an impulse above the critical impulse. The findings herein associate coarse particle entrainments with energetic impulses linked primarily to sweep events (Q4) and secondarily, sequence of sweeps (Q4) and ejections (Q1). [ABSTRACT FROM AUTHOR]

Hazards, ageing, damaging, and traffic loads increase are some of the growing risks that bridge infrastructures in Europe face throughout their design life. Key issues of the asset management plan are the safety assessment of existing structures, the prioritization of the interventions and the optimization of the maintenance schedules. As such, to enable appropriate diagnostics and cost‐effective decisions on assessment/repairs, timely and accurate information from monitoring, inspection and testing is essential. This paper outlines the strategic role of SHM systems to identify the actual condition and future performance of the infrastructures, supporting an effective maintenance plan to meet the target reliability requirements. This contribution describes the use of dense sensing on an Italian bridges network of over 100 bridges, to account for monitoring in safety assessment, facilitating the application of proactive maintenance strategies through a deeper knowledge of the structural behaviour over time. Multiple case studies are detailed, where MEMS sensors have been installed with the purpose of monitoring the evolution of deterioration process, supporting a data‐informed structural diagnostics as well as providing evidence of the effectiveness of strengthening interventions. [ABSTRACT FROM AUTHOR]

Virtual Reality (VR) is an immersive simulation of the environment. The quality of tracking in VR applications is measured in terms of accuracy and robustness. Tracking based on sensing the environment using only one type of sensor may not be sufficient to meet the requirements of VR applications. Hybrid tracking based on inputs from two or more sensors can cope with the challenging needs of such applications. This paper discusses the implementation of two mobile-based VR applications designed for Military shooting training. The first application is developed using a single type of tracking technique named Virtual Shooting Tutor (VST) without hybrid tracking. In the second application, the inputs from external Micro Electro-Mechanical Systems (MEMS) gyroscope and those from mobile integrated gyroscope are hybridized. The application is named VST with hybrid tracking. Two versions of VST are compared with each other to evaluate the impact of hybrid tracking on the accuracy and robustness of the VR application. [ABSTRACT FROM AUTHOR]

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Once a promising polymeric (detection) material is identified, it needs to be incorporated into a MEMS sensor. In this sensor functionalization step, the deposition of the polymeric material on the sense‐plate should be accurate and repeatable. After a quick overview of different deposition methods and related solubility characteristics of polyaniline (and derivatives), drop coating is implemented to deposit polyaniline on sense‐plates, moving from a manual deposition technique to a semi‐automated deposition procedure with more practical benefits, and identifying an optimal 'carrier' mixture of 10–15/90–85 polyaniline/ethylene glycol. A correlation can be established between area coverage and polymer mass, which leaves room for future fully automated deposition using image processing. [ABSTRACT FROM AUTHOR]

The quantitative evaluation of bacterial populations is required in many studies, particularly in the field of microbiology. The current techniques can be time-consuming and require a large volume of samples and trained laboratory personnel. In this regard, on-site, easy-to-use, and direct detection techniques are desirable. In this study, a quartz tuning fork (QTF) was investigated for the real-time detection of E. coli in different media, as well as the ability to determine the bacterial state and correlate the QTF parameters to the bacterial concentration. QTFs that are commercially available can also be used as sensitive sensors of viscosity and density by determining the QTFs' damping and resonance frequency. As a result, the influence of viscous biofilm adhered to its surface should be detectable. First, the response of a QTF to different media without E. coli was investigated, and Luria–Bertani broth (LB) growth medium caused the largest change in frequency. Then, the QTF was tested against different concentrations of E. coli (i.e., 102–105 colony-forming units per milliliter (CFU/mL)). As the E. coli concentration increased, the frequency decreased from 32.836 to 32.242 kHz. Similarly, the quality factor decreased with the increasing E. coli concentration. With a coefficient (R) of 0.955, a linear correlation between the QTF parameters and bacterial concentration was established with a 26 CFU/mL detection limit. Furthermore, a considerable change in frequency was observed against live and dead cells in different media. These observations demonstrate the ability of QTFs to distinguish between different bacterial states. QTFs allow real-time, rapid, low-cost, and non-destructive microbial enumeration testing that requires only a small volume of liquid sample. [ABSTRACT FROM AUTHOR]

Due to the complexity of urban environments, localizing pedestrians indoors using mobile terminals is an urgent task in many emerging areas. Multi-source fusion-based localization is considered to be an effective way to provide location-based services in large-scale indoor areas. This paper presents an intelligent 3D indoor localization framework that uses the integration of Wi-Fi, Bluetooth Low Energy (BLE), quick response (QR) code, and micro-electro-mechanical system sensors (the 3D-WBQM framework). An enhanced inertial odometry was developed for accurate pedestrian localization and trajectory optimization in indoor spaces containing magnetic interference and external acceleration, and Wi-Fi fine time Measurement stations, BLE nodes, and QR codes were applied for landmark detection to provide an absolute reference for trajectory optimization and crowdsourced navigation database construction. In addition, the robust unscented Kalman filter (RUKF) was applied as a generic integrated model to combine the estimated location results from inertial odometry, BLE, QR, Wi-Fi FTM, and the crowdsourced Wi-Fi fingerprinting for large-scale indoor positioning. The experimental results indicated that the proposed 3D-WBQM framework was verified to realize autonomous and accurate positioning performance in large-scale indoor areas using different location sources, and meter-level positioning accuracy can be acquired in Wi-Fi FTM supported areas. [ABSTRACT FROM AUTHOR]

Glass has emerged as a highly versatile substrate for various sensor and MEMS packaging applications, including electromechanical, thermal, optical, biomedical, and RF devices, due to its exceptional properties such as high geometrical tolerances, outstanding heat and chemical resistance, excellent high-frequency electrical properties, and the ability to be hermetically sealed. In these applications, Through Glass Via (TGV) technology plays a vital role in manufacturing and packaging by creating electrical interconnections through glass substrates. This paper provides a comprehensive summary of the research progress in TGV fabrication along with its integrations, including through via formation and metallization. This paper also reviews the significant qualification and reliability achievements obtained by the scientific community for TGV technology. Additionally, this paper summarizes the application of TGV technology in various sensors such as MEMS sensors and discusses the potential applications and future development directions of TGV technology.

The integration of Micro Electronic Mechanical Systems (MEMS) sensor technology in smartphones has greatly improved the capability for Human Activity Recognition (HAR). By utilizing Machine Learning (ML) techniques and data from these sensors, various human motion activities can be classified. This study performed experiments and compiled a large dataset of nine daily activities, including Laying Down, Stationary, Walking, Brisk Walking, Running, Stairs-Up, Stairs-Down, Squatting, and Cycling. Several ML models, such as Decision Tree Classifier, Random Forest Classifier, K Neighbors Classifier, Multinomial Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine, were trained on sensor data collected from accelerometer, gyroscope, and magnetometer embedded in smartphones and wearable devices. The highest test accuracy of 95% was achieved using the random forest algorithm. Additionally, a custom-built Bidirectional Long-Short-Term Memory (Bi-LSTM) model, a type of Recurrent Neural Network (RNN), was proposed and yielded an improved test accuracy of 98.1%. This approach differs from traditional algorithmic-based human activity detection used in current wearable technologies, resulting in improved accuracy. [ABSTRACT FROM AUTHOR]

• Achieves high-precision gait analysis by using only low-cost inertial sensors. • Proposes a new multi-level information fusion architecture. • Involves data fusion from the sensor-level, single-foot level, and dual-foot level. • Extracts new human-walking constraints. • Achieved state-of-the-art for stride-length and foot-clearance estimation. Gait can reflect locomotion and physical condition and thus is used to assess people's health. Traditional high-precision gait analysis devices are expensive and are limited to laboratory conditions, so the trend is to use wearable devices to analyze gait. The existing wearable inertial-based gait-analysis methods have stride-length and foot-clearance estimation accuracy of 2.0 cm to 5.0 cm (in root mean square) and have limitations. They have not considered the regularity of the movement between the toe and the heel, the varying dual-feet distance due to foot dynamics, and the diversity of sensors used. Involving these factors, this paper achieves an improved gait-analysis system that provides stride-length and foot-clearance accuracy of 1.5 cm and 1.0 cm, respectively. Such accuracy is state-of-the-art for low-cost inertial systems and is even competitive with those from visual-sensor-based gait-analysis systems. A key to the proposed method is a new multi-level information fusion architecture and the extraction of human-walking constraints. The information-fusion architecture involves data fusion from the sensor, single-foot, and dual-foot levels. Two gait-characteristic-based motion constraints are presented to achieve such fusion, including the toe-heel constant distance constraint and the dual-foot flexible distance constraint. To implement these constraints, a constrained Kalman filter is constructed. The corresponding hardware system has been designed using multiple dollar-level inertial measurement units. [ABSTRACT FROM AUTHOR]

Incipient motion has been a topic of investigation by researchers, engineers and scientists for more than a century. The main approach for studying sediment entrainment has been the static approach that uses temporal and spatial averaged flow parameters like bed shear stress and stream power to link them indirectly to sediment entrainment. Recent research outputs have shed light on the important role of turbulent fluctuations in the sediment transport process. It is suggested that the approach of using temporal and spatial averaged parameters fails to account for the dynamic and probabilistic nature of the entrainment process, as inherited by flow turbulence. This has led to the introduction of the only dynamic criteria in the literature for studying sediment entrainment, namely the impulse and energy criteria. These criteria take into account both the magnitude and duration of the turbulent flow event used for assessing the conditions that can result in sediment entrainment. In light of this, this work aims to assess whether there is a trend in terms of the type of flow structures that occur in sequence before and after the occurrences of the flow impulses that have resulted in the coarse particle’s entrainment. To achieve this, we conducted a well-controlled laboratory experiment to investigate the incipient motion of a 7 cm diameter instrumented particle. Five runs of the experiment were performed at flowrates close to the threshold of motion. The instrumented particle was equipped with micro-electro-mechanical sensors (MEMS) to accurately measure its inertial dynamics and detect motion. The sensors recorded entrainment events, and these events were stochastically linked to the impulses occurring for the tested flow conditions. Quadrant analysis was used to investigate the type of flow structures that occurred before, during and after the occurrence of quadrant events with an impulse above the critical impulse. The findings herein associate coarse particle entrainments with energetic impulses linked primarily to sweep events (Q4) and secondarily, sequence of sweeps (Q4) and ejections (Q1).

In the past decades, biologging, i.e., the development and deployment of animal-borne loggers, has revolutionized ecology. Despite recent advances, power consumption and battery size however remain central issues and limiting factors, constraining the quantity of data that can be collected and the size of the animals that can be studied. Here, we present strategies to achieve ultra-low power in biologging, using the design of a lightweight audio-inertial logger as an example. It is designed with low-power MEMS sensors, and a firmware based on a small embedded RTOS. Both methodologies for power reduction and experimental results are detailed. With an average power consumption reduced to 5.3 mW, combined with a battery of 1800 mAh, the logger provides 900 h of continuous 8 kHz audio, 50 Hz accelerometer and 10 Hz magnetometer data. [ABSTRACT FROM AUTHOR]

The scenario is very important to smartphone-based pedestrian positioning services. The smartphone is equipped with MEMS(Micro Electro Mechanical System) sensors, which have low accuracy. Now, the methods for scenario recognition are mainly machine-learning methods. The recognition rate of a single method is not high. Multi-model fusion can improve recognition accuracy, but it needs to collect many samples, the computational cost is high, and it is heavily dependent on feature selection. Therefore, we designed the DT-BP(decision tree-Bayesian probability) scenario recognition algorithm by introducing the Bayesian state transition model based on experience design in the decision tree. The decision-tree rules and state transition probability assignment methods were respectively designed for smartphone mode and motion mode. We carried out experiments for each scenario and compared them with the methods in the references. The results showed that the method proposed in this paper has a high recognition accuracy, which is equivalent to the accuracy of multi-model machine learning, but it is simpler, easier to implement, requires less computation, and requires fewer samples. [ABSTRACT FROM AUTHOR]

In this work, we evaluate the suitability of a new MEMS sensor prototype, called ASX1000 (ADEL s.r.l., Modena, Italy), for the monitoring of distant and moderate seismic events. This device is an inexpensive capacitive accelerometer with a relatively low level of instrumental noise; it can record both local and far seismic events. An experimental network built with ASX1000 MEMS, located in northern Italy, was able to record the Mw 6.3 Petrinja earthquake that occurred in December 2020; it had an epicentral distance of more than 350 km. We retrieved the strong motion parameters (PGA, pseudo-absolute velocity, and pseudo-absolute spectral acceleration) from the acceleration time histories recorded by the MEMS sensors. The obtained parameters were compared with the ones obtained by the closer high-quality seismometers, belonging to the INGV National Seismic Network. The comparison to the highest-quality sensors confirms a reasonable agreement of the inferred parameters. This work suggests that—in the near future—MEMS sensors could be adopted to integrate the existing seismic network. A denser coverage of sensors can sample more accurately the seismic wavefield, taking into account the large spatial variability of local geology and the relative differences in seismic response. [ABSTRACT FROM AUTHOR]

The quantitative evaluation of bacterial populations is required in many studies, particularly in the field of microbiology. The current techniques can be time-consuming and require a large volume of samples and trained laboratory personnel. In this regard, on-site, easy-to-use, and direct detection techniques are desirable. In this study, a quartz tuning fork (QTF) was investigated for the real-time detection of E. coli in different media, as well as the ability to determine the bacterial state and correlate the QTF parameters to the bacterial concentration. QTFs that are commercially available can also be used as sensitive sensors of viscosity and density by determining the QTFs’ damping and resonance frequency. As a result, the influence of viscous biofilm adhered to its surface should be detectable. First, the response of a QTF to different media without E. coli was investigated, and Luria–Bertani broth (LB) growth medium caused the largest change in frequency. Then, the QTF was tested against different concentrations of E. coli (i.e., 102–105 colony-forming units per milliliter (CFU/mL)). As the E. coli concentration increased, the frequency decreased from 32.836 to 32.242 kHz. Similarly, the quality factor decreased with the increasing E. coli concentration. With a coefficient (R) of 0.955, a linear correlation between the QTF parameters and bacterial concentration was established with a 26 CFU/mL detection limit. Furthermore, a considerable change in frequency was observed against live and dead cells in different media. These observations demonstrate the ability of QTFs to distinguish between different bacterial states. QTFs allow real-time, rapid, low-cost, and non-destructive microbial enumeration testing that requires only a small volume of liquid sample.

• Newbie study using standard ML techniques with HAR Application and Discussions. • Activities found in Literature with the corresponding reference. • Co-occurrences between activities and sensors with the corresponding reference. • Summary and comparison among the different datasets found in Literature. • Summary of the experimentation settings with performance scores found in Literature. Human Activity Recognition (HAR) is an essential area of research related to the ability of smartphones to retrieve information through embedded sensors and recognize the activity that humans are performing. Researchers have recognized people's activities by processing the data received from the sensors with Machine Learning Models. This work is intended to be a hands-on survey with practical's tables capable of guiding the reader through the sensors used in modern smartphones and highly cited developed machine learning models that perform human activity recognition. Several papers in the literature have been studied, paying attention to the preprocessing, feature extraction, feature selection, and classification techniques of the HAR system. In addition, several summary tables illustrating HAR approaches have been provided: most popular human activities in the literature with paper references, the most popular datasets available for download (Analyzing their characteristics, such as the number of subjects involved, the activities recorded, and the sensors with online-availability), co-occurrences between activities and sensors, and a summary table showing the performance obtained by researchers. =The paper's goal is to recommend, through the discussion phase and thanks to the tables, the current state of the art on this topic. [ABSTRACT FROM AUTHOR]

In recent years, drones have been widely used in various applications, from entertainment, agriculture, their use in photo and video services, military applications and so on. The risk of accidents while using a drone is quite high. To meet this risk, the most important solution is to use a device that helps and simplifies the control of a drone; in addition, the training of drone pilots is very important. To train the drone pilots, both physical and virtual environments can be used, but the probability of an accident is higher for beginners, so the safest method is to train in a virtual environment. The aim of this study is to develop a new device for controlling a drone in a virtual environment. This device is attached to the upper limb of the person involved in the control of that drone. For precise control, the newly created device uses MEMS sensor technology and artificial intelligence-specific methods. [ABSTRACT FROM AUTHOR]

As the world is going through an existential global health crisis, i.e., the outbreak of novel coronavirus-caused respiratory disease (Covid-19), the healthcare systems of all the countries require readily available, low cost and highly precise equipment for the rapid diagnostics, monitoring, and treatment of the disease. The performance and precision of this equipment are solely dependent on the sensors being used. The advancement in research and development of micro-electro-mechanical systems (MEMS) based sensors during recent years, has resulted in the improvement of the conventional equipment being used in biomedical and health care applications. Microfluidics (Lab-on-a-chip) and MEMS sensors are now being used extensively for quick and accurate detection, progression monitoring, and treatment of various diseases including Covid-19. The ongoing miniaturization and design improvements have resulted in more precise sensors and actuators for healthcare applications, even for micro and nanoscale measurements in drug delivery and other invasive applications. This article aims at reviewing the MEMS sensors being used or which can be used in the important equipment for the detection and treatment of Covid-19 or other pandemics. An insight into various designs and working principles of the research-based and commercially available MEMS sensors is presented. The study highlights the role and importance of MEMS sensors in the improvement of equipment with conventional sensors. MEMS sensors outperform the conventional sensors due to their small size ( 1 μm-1mm), negligible weight, prompt response, precise measurements, portability, and ease of integration with electronic circuitry.

Predictive maintenance techniques allow monitoring machines without the need to interrupt its operation, reducing the frequency of corrective maintenance interventions. This work proposes a noninvasive vibration detection system to analyze the state of a three-phase electric powered centrifugal pumps using a micro-electro-mechanical system sensor composed of three-axial accelerometer and gyroscope. The structural analysis is used to extracting time domain signal characteristics. Classification experiments were performed in order to analyze the signal discrimination power for different machine conditions. The results show that using the signals from accelerometers and gyroscopes improves the performance of the classifiers, achieving more than 98% correct condition classification and 100% pump classification. In addition, using the SCM structural analysis method as feature extraction is fast and produces a relative small feature dimension enabling it to be used in embedded systems. [ABSTRACT FROM AUTHOR]

Global positioning systems (GPS) position is determined from the distance measurements to four or more satellites with good geometry. Autonomous vehicles can utilize GPS navigation under open sky areas. However, GPS signal is degraded/denied in urban areas due to satellite signal blockage, multipath, and interference. A suite of multiple types of sensors with distinct strengths is required to achieve localization and positioning for autonomous operation. This paper is a review of a multi-sensor system with different configurations. Two types of the reduced inertial sensor system (RISS) were utilized: Odometer-based RISS/GPS and Frequency Modulated Continuous Wave Radar-based RISS/GPS. The Radar-based RISS/GPS navigation system performance is then evaluated by adding Magnetometer measurements. Fast Orthogonal Search (FOS) nonlinear error modeling is employed for residual non-linear position errors modeling associated with the Magnetometer / Radarbased RISS/GPS positioning solution to improve the results further. During the availability of the GPS, FOS established the non-linear error model. In degraded/denied GPS environments such as urban canyons, the FOS-based non-linear error model operates in prediction mode to estimate the position errors associated with the Magnetometer / Radar-based RISS/GPS, thus providing improved positioning performance. These system configurations were evaluated for an actual road test trajectory for a GPS outage of 300 seconds to examine the performance. The results show that the system configurations play a significant role in improving positioning accuracy. [ABSTRACT FROM AUTHOR]

Nowadays MEMS sensors, like accelerometers, gyroscopes, and magnetometers, are spreading in a wide range of applications, because of their small size, cheapness and increasing performance. For instance, smartphones are currently equipped with this kind of sensors, which could be used to improve the user experience of the phone itself or the navigation functionalities. In this work, accelerometers, gyros, and orientation measurements are exploited to provide advanced information about the walker bringing the phone. In particular, smartphone sensors outputs are used to recognize the identity of the walker and the pose of the device during the walk. The aforesaid information, if known, could be used to improve specific smartphone functionalities. For instance, the recognition of walker identity can be used for theft protection or the device pose can be used to improve the performance of the pedestrian navigation. Machine learning algorithms have been effectively adopted in several fields to solve problems involving classification, time series prediction, pattern recognition, and object detection. Herein, a novel hierarchical approach for classification is applied to data produced by smartphone sensors in order to recognize the previously described contexts, obtaining effective results.

There is great social and economic significance in the teaching and learning of motion activities. This is notably true for teaching the activities involved in rehabilitation, sports, and professional work. The possibility of engaging an automatic teaching system is highly significant. Nevertheless, building an effective system is an ongoing challenge. This article describes a general outline of the teaching system, which includes MEMS (micro-electro-mechanical systems) sensors, haptic actuators, and algorithms for signal classification applied to the online selection of an appropriate teaching method. The main goal of this paper was to prove that the system is able to teach fast and synchronized movements effectively. To this end, system performance was presented and discussed. The statistical tests revealed an efficiency of the proposed approach, especially for tasks of teaching fast and periodic movements. This result was the primary outcome of the presented paper. The described scheme can be utilized for building two types of motor learning systems. The first relates to the “personal” learning systems for rehabilitation and sports. The second type can perform the classification of complex movements of human body parts and may be used in teaching the remote control of machines and vehicles (excavators, cranes, search and rescue drones, etc.).

Due to the complexity of urban environments, localizing pedestrians indoors using mobile terminals is an urgent task in many emerging areas. Multi-source fusion-based localization is considered to be an effective way to provide location-based services in large-scale indoor areas. This paper presents an intelligent 3D indoor localization framework that uses the integration of Wi-Fi, Bluetooth Low Energy (BLE), quick response (QR) code, and micro-electro-mechanical system sensors (the 3D-WBQM framework). An enhanced inertial odometry was developed for accurate pedestrian localization and trajectory optimization in indoor spaces containing magnetic interference and external acceleration, and Wi-Fi fine time Measurement stations, BLE nodes, and QR codes were applied for landmark detection to provide an absolute reference for trajectory optimization and crowdsourced navigation database construction. In addition, the robust unscented Kalman filter (RUKF) was applied as a generic integrated model to combine the estimated location results from inertial odometry, BLE, QR, Wi-Fi FTM, and the crowdsourced Wi-Fi fingerprinting for large-scale indoor positioning. The experimental results indicated that the proposed 3D-WBQM framework was verified to realize autonomous and accurate positioning performance in large-scale indoor areas using different location sources, and meter-level positioning accuracy can be acquired in Wi-Fi FTM supported areas.

Abstract The cause of the rapid growth and enhancement in the dynamics of unmanned aerial vehicles (UAVs) is due to its vast utilization in every-day application. The major advantage of UAVs is no risk of human life with secure and suitable surveillance. The UAV facilitates in live video streaming and wide aerial coverage for monitoring. In this project, a type of UAV named quadcopter has been developed. The work mainly consists of design of the quadcopter frame, interfacing of the brushless DC motors and bluetooth module with the microcontroller, and adjustment of the roll, pitch, and yaw for keeping the smooth flight dynamics by flight controller board containing MEMS sensors, i.e., gyroscope, accelerometer, magnetometer, and pressure sensors. The repeated simulation and testing has been carried out in MATLAB for the mathematical modeling of the dynamics of the system, i.e., Euler method for solving differential equation for finding system states, computation of the rotation matrix R and functions to convert from an angular velocity vector w to the derivatives of roll, pitch, and yaw. The improvement in the parameters of the flight dynamics and removing the errors to gain stability in the frame are the main core issues which have been achieved by the several iterations. A closed-loop control system, i.e., PID controller, has been simulated and carefully designed for stabilizing the actual angle from the sensors and desired angle from the pilot. The reduced error rate of 0.05 degrees after every 10 s was achieved.