Pt-functionalized Amorphous RuOx as Excellent Stability and High-activity Catalysts for Low Temperature MEMS Sensors.

The unsaturated coordination and abundant active sites endow amorphous metals with tremendous potential in improving metal oxide semiconductors' gas-sensing properties. However, the amorphous materials maintain the metastable status and easily transfer into the lower-active crystals during the gas-sensing process at high working temperatures, significantly limiting their further applications. Here, a bimetal amorphous PtRu catalyst is developed by accurately regulating the introduction of Pt species into amorphous RuOx supports to realize the highly active and stable H2 S gas-sensing detection. It is found that incorporation of low-concentration Pt species can effectively maintain the amorphous state of initial RuOx and delay the crystallization temperature as high as 100 °C. Further, ex situ XPS and in situ Raman spectroscopy analysis confirm that active Pt species can facilitate H2 S adsorption by strong Pt-S coordination and dissociate the sulfur species to the surrounding support, which contribute to the chemisorption and sensitization of H2 S. Meanwhile, electron transport at the interface between Pt, RuOx and ZnO further activates the reaction process at the surface of the gas-sensitive material. The final PtRu-modified ZnO (PtRu/ZnO) sensor enables the detection of H2 S in the ultra-low concentration range of 15-2000 ppb with remarkable stability.

Application of Through Glass Via (TGV) Technology for Sensors Manufacturing and Packaging.

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.

Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data.

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.

Characterization of Distant and Moderate Earthquakes with Inexpensive MEMS Sensors: Application to the Mw 6.3, 29th December 2020, Petrinja Event.

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.

Addressing Power Issues in Biologging: An Audio/Inertial Recorder Case Study.

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.

Design, Simulation, and Fabrication of Multilayer Al2O3 Ceramic Micro-Hotplates for High Temperature Gas Sensors.

In gas sensors composed of semiconductor metal oxides and two-dimensional materials, the gas-sensitive material is deposited or coated on a metallic signal electrode and must be selective and responsive at a specific temperature. The microelectromechanical devices hosting this material must keep it at the correct operating temperature using a micro-hotplate robust to high temperatures. In this study, three hotplate designs were investigated: electrodes arranged on both sides of an AlN substrate, a micro-hotplate buried in an alumina ceramic substrate, and a beam structure formed using laser punching. The last two designs use magnetron-sputtered ultra-thin AlN films to separate the upper Au interdigital electrodes and lower Pt heating resistor in a sandwich-like structure. The temperature distribution is simulated by the Joule heat model, and the third design has better energy consumption performance. This design was fabricated, and the effect of the rough surface of the alumina ceramic on the preparation was addressed. The experimental results show that the micro-hotplate can operate at nearly 700 °C. The micro-hotplate heats to nearly 240 °C in 2.4 s using a power of ~340 mW. This design makes ceramic-based micro-hotplates a more practical alternative to silicon-based micro-hotplates in gas sensors.

Low-Cost and High-Performance Solution for Positioning and Monitoring of Large Structures.

Systems for accurate attitude and position monitoring of large structures, such as bridges, tunnels, and offshore platforms are changing in recent years thanks to the exploitation of sensors based on Micro-ElectroMechanical Systems (MEMS) as an Inertial Measurement Unit (IMU). Currently adopted solutions are, in fact, mainly based on fiber optic sensors (characterized by high performance in attitude estimation to the detriment of relevant costs large volumes and heavy weights) and integrated with a Global Position System (GPS) capable of providing low-frequency or single-update information about the position. To provide a cost-effective alternative and overcome the limitations in terms of dimensions and position update frequency, a suitable solution and a corresponding prototype, exhibiting performance very close to those of the traditional solutions, are presented and described hereinafter. The solution leverages a real-time Kalman filter that, along with the proper features of the MEMS inertial sensor and Real-Time Kinematic (RTK) GPS, allows achieving performance in terms of attitude and position estimates suitable for this kind of application. The results obtained in a number of tests underline the promising reliability and effectiveness of the solution in estimating the attitude and position of large structures. In particular, several tests carried out in the laboratory highlighted high system stability; standard deviations of attitude estimates as low as 0.04° were, in fact, experienced in tests conducted in static conditions. Moreover, the prototype performance was also compared with a fiber optic sensor in tests emulating actual operating conditions; differences in the order of a few hundredths of a degree were found in the attitude measurements.

Towards Wearable-Inertial-Sensor-Based Gait Posture Evaluation for Subjects with Unbalanced Gaits.

Human gait reflects health condition and is widely adopted as a diagnostic basisin clinical practice. This research adopts compact inertial sensor nodes to monitor the functionof human lower limbs, which implies the most fundamental locomotion ability. The proposedwearable gait analysis system captures limb motion and reconstructs 3D models with high accuracy.It can output the kinematic parameters of joint flexion and extension, as well as the displacementdata of human limbs. The experimental results provide strong support for quick access to accuratehuman gait data. This paper aims to provide a clue for how to learn more about gait postureand how wearable gait analysis can enhance clinical outcomes. With an ever-expanding gait database,it is possible to help physiotherapists to quickly discover the causes of abnormal gaits, sports injuryrisks, and chronic pain, and provides guidance for arranging personalized rehabilitation programsfor patients. The proposed framework may eventually become a useful tool for continually monitoringspatio-temporal gait parameters and decision-making in an ambulatory environment.

Machine Learning Methodology in a System Applying the Adaptive Strategy for Teaching Human Motions.

The teaching of motion activities in rehabilitation, sports, and professional work has great social significance. However, the automatic teaching of these activities, particularly those involving fast motions, requires the use of an adaptive system that can adequately react to the changing stages and conditions of the teaching process. This paper describes a prototype of an automatic system that utilizes the online classification of motion signals to select the proper teaching algorithm. The knowledge necessary to perform the classification process is acquired from experts by the use of the machine learning methodology. The system utilizes multidimensional motion signals that are captured using MEMS (Micro-Electro-Mechanical Systems) sensors. Moreover, an array of vibrotactile actuators is used to provide feedback to the learner. The main goal of the presented article is to prove that the effectiveness of the described teaching system is higher than the system that controls the learning process without the use of signal classification. Statistical tests carried out by the use of a prototype system confirmed that thesis. This is the main outcome of the presented study. An important contribution is also a proposal to standardize the system structure. The standardization facilitates the system configuration and implementation of individual, specialized teaching algorithms.

Design of Readout Circuit with Quadrature Error and Auxiliary PLL for MEMS Vibratory Gyroscope.

Traditional MEMS gyroscope readout eliminates quadrature error and relies on the phase relationship between the drive displacement and the Coriolis position to accomplish a coherent demodulation. This scheme shows some risk, especially for a mode-matching gyro. If only a slight resonant frequency deviation between the drive and sense mode occurs, a dramatic change in the phase relationship follows, which leads to a wrong demodulation. To solve this, this paper proposes a new readout based on the quadrature error and an auxiliary phase-locked loop (PLL). By tuning the phase shifter in the sense-mode circuit, letting the quadrature error and the carrier of the mixer be in 90° phase alignment, the Coriolis was simultaneously in phase with the carrier. Hence, the demodulation was accomplished. The carrier comes from the PLL output of the drive-mode circuit due to its low jitter and independence of the work mode of the gyro. Moreover, an auxiliary PLL is used to filter the quadrature error to enhance the phase alignment accuracy. Through an elaborate design, a printed circuit board was used to verify the proposed idea. The experimental results show the readout circuit functioned well. The scale factor of the gyro was 6.8 mV/°/s, and the bias instability was 204°/h.

An Approach for Managing Manufacturing Assets through Radio Frequency Energy Harvesting.

The manufacturing industry requests novel solutions that will permit enterprises to stay competitive in the market. This leads to decisions being made based on different technologies that are focused on real-time accurate measurement and monitoring of manufacturing assets. In the context of traceability, radio frequency identification (RFID) tags have been traditionally used for tracking, monitoring, and collecting data of various manufacturing resources operating along the value chain. RFID tags and microelectromechanical systems (MEMS) sensors enable the monitoring of manufacturing assets by providing real-time data. Such devices are usually powered by batteries that need regular maintenance, which in turn leads to delays that affect the overall manufacturing process time. This article presents a low-cost approach to detect and measure radio frequency (RF) signals in assembly lines for optimizing the manufacturing operations in the manufacturing industry. Through the detection and measurement of RF signals, the RF energy can be harvested at certain locations on the assembly line. Then, the harvested energy can be supplied to the MEMS sensors, minimizing the regular maintenance for checking and replacing batteries. This leads to an increase in the operational efficiency and an overall reduction in operational and maintenance costs.

NordicWalking Performance Analysis with an Integrated Monitoring System.

There is a growing interest in Nordic walking both from the fitness and medical point of views due to its possible therapeutic applications. The proper execution of the technique is an essential requirement to maximize the benefits of this practice. This is the reason why a monitoring system for outdoor Nordic walking activity was developed. Using data obtained from synchronized sensors, it is possible to have a complete overview of the users' movements. The system described in this paper is able to measure: the pole angle during the pushing phase, the arms cycle frequency and synchronization and the pushing force applied to the ground. Furthermore, data from a GPS module give an image of the environment where the activity session takes place, in terms of the distance, slope, as well as the ground typology. A heart rate sensor is used to monitor the effort of the user through his/her Beats Per Minute (BPM). In this work, the developed monitoring system is presented, explaining how to use the gathered data to obtain the main feedback parameters for Nordic walking performance analysis. The comparison between left and right arm measurements allowed validating the system as a tool for technique evaluation. Finally, a procedure to estimate the peak pushing force from acceleration measurements is proposed.

Heading Estimation for Pedestrian Dead Reckoning Based on Robust Adaptive Kalman Filtering.

Pedestrian dead reckoning (PDR) using smart phone-embedded micro-electro-mechanical system (MEMS) sensors plays a key role in ubiquitous localization indoors and outdoors. However, as a relative localization method, it suffers from the problem of error accumulation which prevents it from long term independent running. Heading estimation error is one of the main location error sources, and therefore, in order to improve the location tracking performance of the PDR method in complex environments, an approach based on robust adaptive Kalman filtering (RAKF) for estimating accurate headings is proposed. In our approach, outputs from gyroscope, accelerometer, and magnetometer sensors are fused using the solution of Kalman filtering (KF) that the heading measurements derived from accelerations and magnetic field data are used to correct the states integrated from angular rates. In order to identify and control measurement outliers, a maximum likelihood-type estimator (M-estimator)-based model is used. Moreover, an adaptive factor is applied to resist the negative effects of state model disturbances. Extensive experiments under static and dynamic conditions were conducted in indoor environments. The experimental results demonstrate the proposed approach provides more accurate heading estimates and supports more robust and dynamic adaptive location tracking, compared with methods based on conventional KF.

Suitability of Smartphone Inertial Sensors for Real-Time Biofeedback Applications.

This article studies the suitability of smartphones with built-in inertial sensors for biofeedback applications. Biofeedback systems use various sensors to measure body functions and parameters. These sensor data are analyzed, and the results are communicated back to the user, who then tries to act on the feedback signals. Smartphone inertial sensors can be used to capture body movements in biomechanical biofeedback systems. These sensors exhibit various inaccuracies that induce significant angular and positional errors. We studied deterministic and random errors of smartphone accelerometers and gyroscopes, primarily focusing on their biases. Based on extensive measurements, we determined accelerometer and gyroscope noise models and bias variation ranges. Then, we compiled a table of predicted positional and angular errors under various biofeedback system operation conditions. We suggest several bias compensation options that are suitable for various examples of use in real-time biofeedback applications. Measurements within the developed experimental biofeedback application show that under certain conditions, even uncompensated sensors can be used for real-time biofeedback. For general use, especially for more demanding biofeedback applications, sensor biases should be compensated. We are convinced that real-time biofeedback systems based on smartphone inertial sensors are applicable to many similar examples in sports, healthcare, and other areas.

Collaborative WiFi Fingerprinting Using Sensor-Based Navigation on Smartphones.

This paper presents a method that trains the WiFi fingerprint database using sensor-based navigation solutions. Since micro-electromechanical systems (MEMS) sensors provide only a short-term accuracy but suffer from the accuracy degradation with time, we restrict the time length of available indoor navigation trajectories, and conduct post-processing to improve the sensor-based navigation solution. Different middle-term navigation trajectories that move in and out of an indoor area are combined to make up the database. Furthermore, we evaluate the effect of WiFi database shifts on WiFi fingerprinting using the database generated by the proposed method. Results show that the fingerprinting errors will not increase linearly according to database (DB) errors in smartphone-based WiFi fingerprinting applications.

Design and implementation of DATA logging and stabilization system for a UAV.

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.

3D-Printed MEMS in Italy.

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.

Toward the Real-Time and Rapid Quantification of Bacterial Cells Utilizing a Quartz Tuning Fork Sensor.

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.

Fabrication of 2-D Capacitive Micromachined Ultrasonic Transducer (CMUT) Array through Silicon Wafer Bonding.

Capacitive micromachined ultrasound transducers (CMUTs) have broad application prospects in medical imaging, flow monitoring, and nondestructive testing. CMUT arrays are limited by their fabrication process, which seriously restricts their further development and application. In this paper, a vacuum-sealed device for medical applications is introduced, which has the advantages of simple manufacturing process, no static friction, repeatability, and high reliability. The CMUT array suitable for medical imaging frequency band was fabricated by a silicon wafer bonding technology, and the adjacent array devices were isolated by an isolation slot, which was cut through the silicon film. The CMUT device fabricated following this process is a 4 × 16 array with a single element size of 1 mm × 1 mm. Device performance tests were conducted, where the center frequency of the transducer was 3.8 MHz, and the 6 dB fractional bandwidth was 110%. The static capacitance (29.4 pF) and center frequency (3.78 MHz) of each element of the array were tested, and the results revealed that the array has good consistency. Moreover, the transmitting and receiving performance of the transducer was evaluated by acoustic tests, and the receiving sensitivity was -211 dB @ 3 MHz, -213 dB @ 4 MHz. Finally, reflection imaging was performed using the array, which provides certain technical support for the research of two-dimensional CMUT arrays in the field of 3D ultrasound imaging.

A Novel Algorithm for Scenario Recognition Based on MEMS Sensors of Smartphone.

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.