Microelectromechanical Systems Inertial Sensors Research Articles

Vehicle Surroundings Perception Using Micro‐electromechanical Systems Inertial Sensors

Sensors enable vehicles to perceive their surroundings even while parked. Camera‐based systems for vehicle surroundings perception are already available as of today. However, camera‐based systems result in excessive power consumption and data collection. In this article, a study on always‐on surroundings perception of parked vehicles is done using micro‐electromechanical systems (MEMS) inertial sensors as an alternative to camera‐based systems. The measurements of the MEMS inertial sensors are used to detect and classify 17 different types of events. These events include accidents, vandalism, rain, as well as normal use. A convolutional neural network (CNN) is used to classify the events. The data used to train the CNN is recorded using a test vehicle in a laboratory setting and on a test track. The CNN is quantized for deployment on a CNN hardware accelerator which is packaged with the sensor as a system‐in‐package (SiP). The novel sensor system achieves a classification accuracy of 88% at a total system power dissipation of 27 μW.

Smartphone MEMS Accelerometer and Gyroscope Measurement Errors: Laboratory Testing and Analysis of the Effects on Positioning Performance.

Embedding various sensors with powerful computing and storage capabilities in a small communication device, smartphones have become a prominent platform for navigation. With the increasing popularity of Apple CarPlay and Android Auto, smartphones are quickly replacing built-in automotive navigation solutions. On the other hand, smartphones are equipped with low-performance Micro Electro Mechanical Systems (MEMS) sensors to enhance their navigation performance in Global Navigation Satellite System (GNSS)-degraded or -denied environments. Compared with higher-grade inertial navigation systems (INS), MEMS-based INSs have a poor navigation performance due to large measurement errors. In this paper, we present laboratory test results on the stochastic and deterministic errors observed in MEMS inertial sensor measurements of five different smartphones from different manufacturers. Then, we describe and discuss the short-term effects of these errors on the pure inertial navigation performance and also on the navigation performance based on the tight coupling of INS with GNSS measurements using a smartphone.

On the suitability of redundant accelerometers for the implementation of smart oscillation monitoring system: Preliminary assessment

Structural health monitoring (SHM) is an essential aspect to ensure the safety and longevity of civil infrastructure. In recent years, there has been a growing interest in developing SHM systems based on Micro-Electro-Mechanical Systems (MEMS) technology. MEMS-based sensors are small, low-power, and cost-effective, making them ideal for large-scale deployment in structural monitoring systems. However, the use of MEMS-based sensors in SHM systems can be challenging due to their inherent errors, such as drift, noise, and bias instability; these errors can affect the accuracy and reliability of the measured data, leading to false alarms or missed detections. Therefore, several methods have been proposed to compensate for these errors and improve the performance of MEMS-based SHM systems. For this purpose, the authors propose the combined of a redundant configuration of cost-effective MEMS accelerometers and a Kalman Filter approach to compensate MEMS inertial sensor errors and data filtering; the performance of the method is preliminarily assessed by means of a custom-controlled oscillation generator and compared with that granted by a high-cost, high-performance MEMS reference system where amplitude differences of 0.02 m/s2 have been experienced. Finally, a sensor node for real-time monitoring has been proposed that exploits LoRaWAN and NFC protocols to access the structure information to be monitored.

Optimal Path Planning Method for IMU System-Level Calibration Based on Improved Dijkstra’s Algorithm

The calibration path of system-level calibration directly affects the incentive effect of the error term and thus the calibration accuracy. Currently, the planning of system-level calibration paths is predominantly designed based on personal experience, resulting in insufficient incentive for error terms, low calibration accuracy, and long calibration times. Therefore, this study proposes a system-level calibration optimal path planning method based on an improved Dijkstra’s algorithm. First, the system-level calibration optimal path planning problem was modeled as a multi-fork regular root tree model, and the adaptability of Dijkstra’s algorithm was improved. Second, a 30-dimensional Kalman filter model was designed for system-level calibration. Then, simulation experiments were conducted, and the results demonstrated that the calibration accuracy of the error term reached 90% within 330 s. Finally, a Micro-Electro-Mechanical system (MEMS) inertial sensor, model PA-IMU488B, was used for experimental verification, and the results were compared with the discrete calibration results. The results indicate that the bias and scale factor errors of the MEMS inertial sensor reached the target accuracy within 5 min. The optimal path planning method for system-level calibration proposed in this study is not dependent on a high-precision turntable, is applicable to sensors of different accuracies, and decreases calibration time while ensuring calibration accuracy.

Research on Identification and Suppression of Vibration Error for MEMS Inertial Sensor in Near-Bit Inclinometer

The measurement information of the traditional conventional measurement while drilling (MWD) method lags seriously because the installation position is far from the bit. However, the near-bit inclinometer will generate a large number of vibration interference signals when the bit breaks the rock in the drilling process, resulting in the continuous increase in errors measured by the microelectromechanical system (MEMS) inertial sensor. Aiming at the problems of low accuracy and poor stability of near-bit inclinometer under the interference of long-term complex vibration signals, a vibration error identification and suppression method of MEMS inertial sensor of near-bit inclinometer is proposed in this article. Firstly, the characteristics of the vibration signal of near-bit inclinometer are analyzed. The near-bit vibration errors of first-order linear vibration and high-order nonlinear vibration are identified, and the vibration characteristics are analyzed, the error model of gyroscope and accelerometer under complex vibration interference is established, and then the vibration error suppression model of inclinometer gyroscope and accelerometer is established based on the hybrid Kalman filter (KF) and unscented KF (UKF) algorithm. Finally, the hardware and software experimental platform of near-bit inclinometer is built. The experimental results show that the dynamic measurement errors of inclination, toolface, and azimuth are kept within ±0.6°, and the vibration errors of gyroscope and accelerometer are effectively suppressed.

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.

Python-Based Open-Source Electro-Mechanical Co-Optimization System for MEMS Inertial Sensors.

The surge in fabrication techniques for micro- and nanodevices gave room to rapid growth in these technologies and a never-ending range of possible applications emerged. These new products significantly improve human life, however, the evolution in the design, simulation and optimization process of said products did not observe a similarly rapid growth. It became thus clear that the performance of micro- and nanodevices would benefit from significant improvements in this area. This work presents a novel methodology for electro-mechanical co-optimization of micro-electromechanical systems (MEMS) inertial sensors. The developed software tool comprises geometry design, finite element method (FEM) analysis, damping calculation, electronic domain simulation, and a genetic algorithm (GA) optimization process. It allows for a facilitated system-level MEMS design flow, in which electrical and mechanical domains communicate with each other to achieve an optimized system performance. To demonstrate the efficacy of the methodology, an open-loop capacitive MEMS accelerometer and an open-loop Coriolis vibratory MEMS gyroscope were simulated and optimized—these devices saw a sensitivity improvement of 193.77% and 420.9%, respectively, in comparison to their original state.

Feature Extraction and Data Analysis of Basketball Motion Postures: Acquisition With an Inertial Sensor

Abstract This paper mainly analyzed the application of inertial sensors in basketball posture analysis. The data of 20 basketball players in different postures were collected by Micro-electromechanical systems inertial sensors. The mean, variance, and skewness were taken as features to compare the performance of C4.5, random forest (RF), k-nearest neighbor, and support vector machine (SVM) algorithms in analyzing posture data. It was found that the classification accuracy of the k-nearest neighbor algorithm was around 90%, and the classification accuracy of C4.5, RF, and SVM algorithms was all above 90%. The classification accuracy of the RF algorithm was the highest (98.72%), which was significantly higher than C4.5 and SVM algorithms. The results verified the advantages of the RF algorithm in basketball posture analysis. The research results confirm the reliability of the inertial sensor in the field of motion posture analysis and make some contributions to its application in sport training. This paper provides support for the analysis of motion posture.

Online Nonlinear Error Compensation Circuit Based on Neural Networks

Nonlinear errors of sensor output signals are common in the field of inertial measurement and can be compensated with statistical models or machine learning models. Machine learning solutions with large computational complexity are generally offline or implemented on additional hardware platforms, which are difficult to meet the high integration requirements of microelectromechanical system inertial sensors. This paper explored the feasibility of an online compensation scheme based on neural networks. In the designed solution, a simplified small-scale network is used for modeling, and the peak-to-peak value and standard deviation of the error after compensation are reduced to 17.00% and 16.95%, respectively. Additionally, a compensation circuit is designed based on the simplified modeling scheme. The results show that the circuit compensation effect is consistent with the results of the algorithm experiment. Under SMIC 180 nm complementary metal-oxide semiconductor (CMOS) technology, the circuit has a maximum operating frequency of 96 MHz and an area of 0.19 mm2. When the sampling signal frequency is 800 kHz, the power consumption is only 1.12 mW. This circuit can be used as a component of the measurement and control system on chip (SoC), which meets real-time application scenarios with low power consumption requirements.

Attitude determination in integrated GNSS-inertial navigation systems in information and measurement insufficiency conditions

The article presents the attitude determination algorithm for multi-antenna global navigation satellite and inertial integrated navigation system operating under information and measurement insufficiency conditions. The information and measurement insufficiency conditions are understood as the impossibility of the inertial system alignment on a fixed base and the absence of a redundant number of satellites to resolve the carrier phase integer ambiguity. The ability of the navigation system to operate in such conditions is achieved by combining several existing attitude determination methods and using the new method of attitude determination using ambiguous satellite phase measurements and absolute angular rate measurements. The hardware-in-the-loop simulation results using a 2-axis motion simulator and microelectromechanical systems inertial sensors are presented. The results confirm the efficiency of the proposed approach to the design of the navigation system algorithm.

A Transfer Learning Based Unmanned Aerial Vehicle MEMS Inertial Sensors Fault Diagnosis Method

In this paper, we propose a novel transfer learning based micro-electromechanical system (MEMS) inertial sensors fault diagnosis method. First, the MEMS inertial sensors fault diagnosis method is formulated to a deep transfer learning problem in which the offline samples are deemed as source domain and the online samples are set to target domain features. Second, the bidirectional long short-term memory and Hilbert-Huang transformation-based feature transfer model is designed to decrease the discrepancy between SD and TD, that performs the transfer operation using intrinsic mode function features. Then we propose a convolutional neuro network-based transfer learning algorithm to further decrease deep features discrepancy and perform the fault classification tasks on TD. According to the experiments, the proposed FD method has achieved excellent fault classification performance and significantly improvement comparing with the state-of-the art methods.

A hybrid noise removal algorithm for MEMS sensors

The use of microelectromechanical systems (MEMS) in electronics, automotive, consumer and medical sector is increasing rapidly. It is expected that by 2024 the Global MEMS and sensor market will reach the value of 93 billion dollars. MEMS inertial measurement sensors (accelerometer and gyroscopes) are used in low end consumer electronics such as smartphones to high end products such as drones used in military applications. MEMS IMU (Inertial Measurement Unit) are often deployed in dynamic environment such as in machining process for condition monitoring, vehicles for identifying driving behaviors, wearable technology for fall detection and many others. Consequently, Gaussian noise is always present in the output signal of such sensors. Denoising the output signal is critical for feature extraction and data analysis. Recently hybrid approaches for noise removal have gained much research attention. The aim of this paper is to present a hybrid noise removal algorithm for MEMS IMU sensors. In this technique, the signal to noise ratio has been improved and remove glitches by minimizing the hybrid noise from the original output signal of the MEMS IMU sensor.

Random Error Reduction Algorithms for MEMS Inertial Sensor Accuracy Improvement-A Review.

Research and industrial studies have indicated that small size, low cost, high precision, and ease of integration are vital features that characterize microelectromechanical systems (MEMS) inertial sensors for mass production and diverse applications. In recent times, sensors like MEMS accelerometers and MEMS gyroscopes have been sought in an increased application range such as medical devices for health care to defense and military weapons. An important limitation of MEMS inertial sensors is repeatedly documented as the ease of being influenced by environmental noise from random sources, along with mechanical and electronic artifacts in the underlying systems, and other random noise. Thus, random error processing is essential for proper elimination of artifact signals and improvement of the accuracy and reliability from such sensors. In this paper, a systematic review is carried out by investigating different random error signal processing models that have been recently developed for MEMS inertial sensor precision improvement. For this purpose, an in-depth literature search was performed on several databases viz., Web of Science, IEEE Xplore, Science Direct, and Association for Computing Machinery Digital Library. Forty-nine representative papers that focused on the processing of signals from MEMS accelerometers, MEMS gyroscopes, and MEMS inertial measuring units, published in journal or conference formats, and indexed on the databases within the last 10 years, were downloaded and carefully reviewed. From this literature overview, 30 mainstream algorithms were extracted and categorized into seven groups, which were analyzed to present the contributions, strengths, and weaknesses of the literature. Additionally, a summary of the models developed in the studies was presented, along with their working principles viz., application domain, and the conclusions made in the studies. Finally, the development trend of MEMS inertial sensor technology and its application prospects were presented.

Low-Cost MEMS-Based NARX Model for GPS-Denied Areas

Autonomous vehicle navigation has witnessed a huge revolutionary revision regarding development in Micro-Electro Mechanical System (MEMS) technology. Most recently, Strapdown Inertial Navigation System (SDINS) has successfully been integrated with Global Positioning System (GPS). However, different grades of MEMS inertial sensors are available and choosing the convenient grade is quite important. Noises in inertial sensor are mostly treated through de-noising the additive errors to improve the precision of SDINS output. Unfortunately, integration in SDINS mechanization causes a growing in SDINS error output which considered the main challenge in integrating MEMS inertial sensors with GPS. This paper aims to promote the long-term performance of the MEMS-SDINS/GPS integrated system. A new integrated structure is proposed to model the nonlinearities that exist in SDINS dynamics in addition to the error uncertainty in the inertial sensors’ measurements. A robust Nonlinear AutoRegressive models with eXogenous inputs (NARX) based algorithm are designed for data fusion in the proposed GPS/INS integrated system. Validation for the proposed integrated system has been carried out using different field tests data in order to assess the accuracy of the system during GPS denied environment. The results obtained demonstrate that the proposed NARX model is applicative and satisfactory which shows a desired prediction performance.

Method for Fault Diagnosis of Temperature-Related MEMS Inertial Sensors by Combining Hilbert-Huang Transform and Deep Learning.

In this paper, we propose a novel method for fault diagnosis in micro-electromechanical system (MEMS) inertial sensors using a bidirectional long short-term memory (BLSTM)-based Hilbert–Huang transform (HHT) and a convolutional neural network (CNN). First, the method for fault diagnosis of inertial sensors is formulated into an HHT-based deep learning problem. Second, we present a new BLSTM-based empirical mode decomposition (EMD) method for converting one-dimensional inertial data into two-dimensional Hilbert spectra. Finally, a CNN is used to perform fault classification tasks that use time–frequency HHT spectrums as input. According to our experimental results, significantly improved performance can be achieved, on average, for the proposed BLSTM-based EMD algorithm in terms of EMD computational efficiency compared with state-of-the-art algorithms. In addition, the proposed fault diagnosis method achieves high accuracy in fault classification.

Implementation and Performance of a Deeply-Coupled GNSS Receiver with Low-Cost MEMS Inertial Sensors for Vehicle Urban Navigation.

In urban environments, Global Navigation Satellite Systems (GNSS) signals are frequently attenuated, blocked or reflected, which degrades the positioning accuracy of GNSS receivers significantly. To improve the performance of GNSS receiver for vehicle urban navigation, a GNSS/INS deeply-coupled software defined receiver (GIDCSR) with a low cost micro-electro-mechanical system (MEMS) inertial measurement unit (IMU) ICM-20602 is presented, in which both GPS and BDS constellations are supported. Two key technologies, that is, adaptive open-close tracking loops and INS aided pseudo-range weight control algorithm, are applied in the GIDCSR to enhance the signal tracking continuity and positioning accuracy in urban areas. To assess the performance of the proposed deep couple solution, vehicle field tests were carried out in GNSS-challenged urban environments. With the adaptive open-close tracking loops, the deep couple output carrier phase in the open sky, and improved pseudo-range accuracy before and after GNSS signal blocked. Applying the INS aided pseudo-range weight control, the pseudo-range gross errors of the deep couple decreased caused by multipath. A popular GNSS/INS tightly-coupled vehicle navigation kit from u-blox company, M8U, was tested side by side as benchmark. The test results indicate that in the GNSS-challenged urban areas, the pseudo-range quality of GIDCSR is at least 25% better than that of M8U, and GIDCSR’s horizontal positioning results are at least 69% more accurate than M8U’s.

MEMS Inertial Sensor Fault Diagnosis Using a CNN-Based Data-Driven Method

In this paper, we propose a novel fault diagnosis (FD) approach for micro-electromechanical systems (MEMS) inertial sensors that recognize the fault patterns of MEMS inertial sensors in an end-to-end manner. We use a convolutional neural network (CNN)-based data-driven method to classify the temperature-related sensor faults in unmanned aerial vehicles (UAVs). First, we formulate the FD problem for MEMS inertial sensors into a deep learning framework. Second, we design a multi-scale CNN which uses the raw data of MEMS inertial sensors as input and which outputs classification results indicating faults. Then we extract fault features in the temperature domain to solve the non-uniform sampling problem. Finally, we propose an improved adaptive learning rate optimization method which accelerates the loss convergence by using the Kalman filter (KF) to train the network efficiently with a small dataset. Our experimental results show that our method achieved high fault recognition accuracy and that our proposed adaptive learning rate method improved performance in terms of loss convergence and robustness on a small training batch.

(Invited) CMOS-MEMS Based Microgravity Sensor and Its Application

Microelectromechanical systems (MEMS) inertia sensors, such as MEMS accelerometers, are now widely used for a variety of applications in automotive, industrial, healthcare, entertainment, consumer handheld electronics, etc. In this paper, we present our recent progress of a highly sensitive CMOS-MEMS inertia sensor and its application to early-stage diagnosis of Parkinson’s disease.(1) Highly sensitive CMOS-MEMS inertia sensor using Au as proof massFigure 1 shows the conceptual image of the CMOS-MEMS inertia sensor. The MEMS structure is fabricated onto standard CMOS wafer using so-called post-CMOS process. Electroplating Au is used. The feature MEMS device is the use of Au proof mass, which contributes to reduce the BN in the inertia sensor. The BN of moving structures determines the sensitivity of MEMS accelerometer. BN is expressed as BN=(4kB T b)1/2/m, where kB , T, b and m are the Boltzmann constant, the absolute temperature, the viscous damping coefficient and the mass of the movable structures of an inertial sensor, respectively. Large the density of the proof mass results in suppressing BN without sacrificing the device footprint.The reason why we use Au as proof masu is that the density of Au is almost an order of magnitude higher than the density of Si that is used as the standard material for proof masses.Today, commercial MEMS inertia sensors accommodate typically more than 1G (1G = 9.8 m/s2: gravitational acceleration). On the other hand, accurate sub-1G to micro-G sensing by using MEMS inertial sensors has potential to realize various unexplored applications, such as precise monitoring of human and non-human objects for healthcare and medical purposes or integrated inertial measurement unit for advanced navigation systems. At present time, 0.1micro-G/Hz^(1/2) Brownian Noise MEMS inertia sensor is available in our research group. Our MEMS sensor has enough performance for detecting microgravity.Figure 2 shows a photo of typical CMOS-MEMS inertia sensor. Control of crystal structure of Au is essential when Au is used as proof mass. Analysis of Au crystal structure and novel Au electroplating technology will be presented at the conference. Also, detail process flow of CMOS-MEMS inertia sensor is presented.(2) Early-stage diagnosis of Parkinson’s diseaseParkinson’s disease is originated from lack of dopamine secretion. Early-stage diagnosis enables the delay of symptom progress and/or the interval extension of Levodopa dosing, resulting in quality-of-life (QoL) improvement of Patient. Parkinson’s disease (PD) is categorized by Hoehn-Yahr (HY) scale from 1(light) to 5 (heavy).We have developed diagnosis of Parkinson’s disease using CMOS-MEMS inertia sensor. So far, walking trajectory and postural abnormality of PD patients and healthy person are analyzed based on machine learning technique. PD patients and healthy individuals can be classified with an accuracy of over 90%.Recently, we have succeeded in detecting the micro musclar sound of the index finger for the first time using a high-sensitivity inertia sensor whose noise level is 10 dB lower than commercially available sensors. These techniques will open a new door of Early-stage diagnosis of Parkinson’s disease. Details of diagnosis of Parkinson’s disease using CMOS-MEMS inertia sensor will be discussed in the presentation.Acknowledgement: This work was financially supported by JST CREST Grant Number JPMJCR1433, Japan, and JSPS KAKENHI Grant Number 19K05232. Figure 1

On the Usage of Low-Cost MEMS Sensors, Strapdown Inertial Navigation, and Nonlinear Estimation Techniques in Dynamic Positioning

In this article, we suggest that a strapdown inertial navigation system based on microelectromechanical system (MEMS) inertial sensors is a useful addition to a vessel with dynamic positioning (DP). We conduct full-scale experiments with MEMS inertial sensors on board a DP vessel operating off the Norwegian coast. The vessel operates in different scenarios, and the purpose is to showcase how low-cost MEMS sensors may complement or replace existing DP sensor systems. Employing nonlinear observers for estimating attitude, heave, velocity, and position, we go through the benefits and disadvantages, and some caveats, for the sensors and methods used in this article. Two different MEMS units are evaluated, aided by gyrocompasses and position reference systems. We evaluate the attitude, heave, and dead reckoning capabilities obtained with the presented estimators, in relation to relevant class notation, ultimately motivating the inclusion of new sensors and methods for dynamic positioning. The results related to attitude and heave are compared with data from well-proven industry standard vertical reference units while dead reckoning is evaluated with respect to the onboard position reference systems.

Compounded Calibration Based on FNN and Attitude Estimation Method Using Intelligent Filtering for Low Cost MEMS Sensor Application

Micro electro mechanical system (MEMS) inertial sensors have advantages, including small size and low power consumption. The performances of Micro Inertial measurement unit (IMU), which is composed of MEMS inertial sensors, degrade, and error, will become larger in high dynamic environment. In order to solve the problem, a novel combined calibration method for compensating the deterministic error of MEMS sensors is proposed. Considering the rotation of different sensitive axes in high dynamic and low dynamic environment, the compounded calibration based on fuzzy neural network (FNN) is adopted to identify the coupling coefficients to eliminate the adverse coupling effects between different rotation axes. Furthermore, the self-developed Micro IMU and magnetometer are applied in attitude estimation system. Considering the large attitude error occurred in most cases, the approach utilizing the estimation of error quaternion vector could avoid the calculation error due to inaccurate modeling in the skew symmetric matrix that comprises attitude error vector components. The intelligent Kalman filter (IKF) based on complexity state equation of error quaternion is designed to improve the performance by adjusting the parameters of filter on line. The experimental results show that the proposed approach could have a higher level of stability and accuracy in comparison to other attitude estimation algorithms.