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The inertial navigation system (INS) is widely used in spacecraft, missiles, and airplanes for its full autonomy and complete information. Skewed inertial measurement unit (IMU) is a typical device for INS. This IMU is of great significance for key projects such as manned spacecraft or heavy-lift rockets. Calibration of the IMU parameters has always been the key technology for INS. However, the calibration for skewed IMU is difficult to conduct for the skewed placement of the sensors and the complicated model. An all-parameter calibration for six-axis skewed IMU is proposed in this article. The error model is established. The measurement equations for Kalman filter are deduced. The calculation can be accomplished in two steps: The calibration for three gyros+ three accelerometers and the parameter compensation and iteration calculation. Simulations and experiments results indicate that: The complexity of calibration data and procedure is the same level as that of traditional IMU; all the parameters can be precisely estimated with the designed 12 positions sequence. [ABSTRACT FROM AUTHOR]

The principle of compensating size effect by special structure design or structure design value has its limitation. In this article, the internal size effect and the rotation center size effect are considered, and the coupling between them is eliminated by the constraint of virtual point position. Moreover, in order to avoid the influence of other error sources on the size effect error calibration, such as the gyro-accelerometer asynchronous time and quadratic error of the accelerometer are considered in the state equation to develop a 46D Kalman filter self-calibration model. The calibration accuracy of the size effect is better than 1.2 mm by comparing the design values of the high precision inertial measurement unit physical structure dimensions. The navigation experiment shows that the accuracy of velocity can be increased by more than 4.4 times after the size effect error is compensated. [ABSTRACT FROM AUTHOR]

This article is concerned with the estimation problem of Toolface for dynamic point-the-bit rotary steerable drilling tool systems. First, considering the unknown frequency and amplitude of vibration during the drilling process, the Toolface system is modeled as a time-varying stochastic system with unknown and time-varying noise covariance matrices. Under the assumption that the noises and their covariance matrices, respectively, obey the Gaussian distribution and the inverse Wishart distribution, the variational Bayesian-based moving horizon estimation algorithm is proposed. Then, the state and the noise covariance matrices are inferred by iteratively updating their approximate posterior probability distributions. Finally, simulations and experiments are provided to demonstrate the effectiveness and superiority of the developed estimation scheme. [ABSTRACT FROM AUTHOR]

This work advances the research on continuous mode-reversal frequency modulated accelerometers by characterizing in depth some of their nonidealities, proposing methods for their compensation, and exploiting a renovated sensor design that enables closed-loop compensation of shock and vibrations. The scale factor nonlinearity is first investigated and compensated through a cubic calibration term, yielding a 50% larger range for a 2.5-fold larger scale factor than in former implementations. Additionally, temperature effects on the zero-g offset are compensated by exploiting the value of the low-pass filtered resonance frequency, which yields a valuable information of the silicon sensor temperature. This enables reducing the zero-g-offset temperature dependence by a factor 5 over previous works on similar structures. Finally, a closed-loop compensation of the quality factor, implemented through additional driving electrodes in the structure, guarantees a 25-fold reduction on effects of shocks and vibrations exciting the acceleration-sensitive mode. [2022-0117] [ABSTRACT FROM AUTHOR]

MEMS resonant accelerometers are robust to electromagnetic interference and have higher sensitivity and resolution. However, they are susceptible to temperature variation, which may limit this type of sensor used in high-end applications. This paper proposes a TDS-PLL with MEMS VCO for MEMS resonant accelerometers to be immune to temperature drift. The theoretical analysis of TDS-PLL MRA demonstrates it can suppress the temperature drift, which is verified by simulation and measurement results. Measurement results of the TDS-PLL MRA show that the bias instability is $13.15~\mu \text{g}$ and the noise level is $7.04~\mu \text{g}/\surd $ Hz with 0.32g full scale at room temperature. The temperature coefficient of bias stability in TDS-PLL MRA achieves 2.21 mg/°C in a temperature range of 300K to 360K, which is lower 5.5 times than that of the traditional PLL MRA with frequency shift. [2022-0098] [ABSTRACT FROM AUTHOR]

Herein, a novel in-plane dual-axis micro-electro-mechanical system piezoresistive accelerometer was proposed based on theoretical and simulation analyses. Its self-support piezoresistive beam (SPB) possessed the features of force amplification and axial deformation to obtain high sensitivity. To clarify the influence of structural dimensions on the performance of the accelerometer and the pure axial-deformation condition of the SPB, a theoretical model was established to analyze mechanical behavior. The theoretical results were in well accordance with the simulation ones, displaying a maximum relative error of less than 10%. The theoretical and simulation results suggested that the accelerometer with thin and long support/hinge beams possesses a higher sensitivity but a lower frequency. However, the sensitivity and natural frequency increased simultaneously before the distance between SPB and support beam $D$ reached a certain value. The simulated longitudinal stress of SPB achieved the average value of 32.473 MPa, and the first-order natural frequency was nearly 13.6 kHz. Furthermore, the fabricated accelerometer was tested in a simple packaging case. The experimental results demonstrated that the proposed accelerometer had a measuring sensitivity of 0.198 mV/g/V with a maximum non-linearity of 1.00% FS and a resonant frequency of 12.93 kHz. The cross sensitivities were 11.93 and $15.97~\mu \text{V}$ /g/5V in the $y$ and $z$ axes, respectively, which were 1.21% and 1.61% of the prime-axis sensitivity. The zero offset with varying temperatures was 0.00296%/°C, and the zero drift in the long-term static stability test was 0.02 mV. All the results above revealed the promising application potential of the proposed accelerometer in vibration detection in the high-speed rotating spindle of machine tools. [2022-0057] [ABSTRACT FROM AUTHOR]

Piezoelectric ceramics have been widely used in high precision sensors such as vibration detection, but piezoelectric accelerometers in high-temperature applications are very rare. We prepared ($1- {x}$) BiFeO3- ${x}$ BaTiO3-0.0035MnO2-0.001Li2CO3(BF- ${x}$ BT) ceramics by a solid state approach, and investigated the effect of BT content(${x}$) on the phase structure, microstructure, dielectric properties, ferroelectric properties, piezoelectric properties, temperature stability, and especially the sensitivity of the piezoelectric accelerometer. The crystal structure of the sample is pure perovskite structure with the MPB (R and P phases) locating in a composition range of $0.28\le {x} \le0.32$ for BF-xBT ceramics, and the single R phase exist at $0.20\le {x} < 0.28$. When ${x} $ = 0.30, the ceramic presents both high Curie temperature and ${d}_{33}$. Notably, the sensitivity of BF-0.30BT piezoelectric accelerometer reaches the highest value of about 40 pC/g and shows an excellent stability until 400 °C, indicating that this material is a promising candidate for high-temperature piezoelectric accelerometer applications. [ABSTRACT FROM AUTHOR]

Arm-and-hand tracking by technological means allows gathering data that can be elaborated for determining gesture meaning. To this aim, machine learning (ML) algorithms have been mostly investigated looking for a balance between the highest recognition rate and the lowest recognition time. However, this balance comes mainly from statistical models, which are challenging to interpret. In contrast, we present $\mu C^{1}$ and $\mu C^{2}$ , two geometric model-based approaches to gesture recognition which support the visualization and geometrical interpretation of the recognition process. We compare $\mu C^{1}$ and $\mu C^{2}$ with respect to two classical ML algorithms, k-nearest neighbor (k-NN) and support vector machine (SVM), and two state-of-the-art (SotA) deep learning (DL) models, bidirectional long short-term memory (BiLSTM) and gated recurrent unit (GRU), on an experimental dataset of ten gesture classes from the Italian Sign Language (LIS), each repeated 100 times by five inexperienced non-native signers, and gathered with wearable technology (a sensory glove and inertial measurement units). As a result, we achieve a compromise between high recognition rates ($>90\%$) and low recognition times ($ < 0.1 {\mathrm{ s}}$) that is adequate for human–computer interaction. Moreover, we elaborate on the algorithms’ geometric interpretation based on geometric algebra, which supports some understanding of the recognition process. [ABSTRACT FROM AUTHOR]

We present a theoretical model for transient analysis of the micro thermal convective accelerometer (MTCA). By leveraging this model, efficient parametric prediction can be calculated compared with time-consuming and complex CFD simulation and experimental testing. Based on the calculation results, it is recommended that a device with a thin film, a small size, and a small distance between detectors and microheater will achieve a faster frequency response, which is demonstrated by the experimental results using a serial of MTCAs fabricated with a CMOS compatible method. In addition, combined with the optimization of sensitivity, a guideline can be found that in order to improve the frequency response and sensitivity, the detector should be placed closer to the microheater when the sensor length of the device is larger. While, for the device with a smaller sensor length, detectors should be designed in the moderate region of the ratio of D/L. Except for frequency improvement, the relationship between the sensor size and sensitivity is also described and a device with an outstanding sensitivity of 7, $500~\mu \text{V}$ /g is created with a novel parallel stake method. Combining sensitivity and frequency variation with sensor size, a trade-off should be made when designing the MTCA. Above all, the recovery of this work is meaningful for the researchers to optimize their designs for the improvement of both sensitivity and frequency response. [2022-0112] [ABSTRACT FROM AUTHOR]

Camera–inertial measurement unit (IMU) sensor fusion has been extensively studied in recent decades. Numerous observability analysis and fusion schemes for motion estimation with self-calibration have been presented. However, it has been uncertain whether the intrinsic parameters of both the camera and the IMU are observable under general motion. To answer this question, by using the Lie derivatives, we first prove that for a rolling shutter (RS) camera–IMU system, all the intrinsic and extrinsic parameters, camera time offset, and readout time of the RS camera are observable with an unknown landmark. To our knowledge, we are the first to present such a proof. Next, to validate this analysis and to solve the drift issue of a structureless filter during standstills, we develop a keyframe-based sliding window filter (KSWF) for odometry and self-calibration, which works with a monocular RS camera or stereo RS cameras. Though the keyframe concept is widely used in vision-based sensor fusion, to our knowledge, the KSWF is the first of its kind to support self-calibration. Our simulation and real data tests have validated that it is possible to fully calibrate the camera–IMU system using the observations of opportunistic landmarks under diverse motion. Real data tests confirmed previous allusions that keeping landmarks in the state vector can remedy the drift in standstill and showed that the keyframe-based scheme is an alternative solution. [ABSTRACT FROM AUTHOR]

In this article, a model-based cooperative navigation system for a group of quadrotors, while no absolute position data is accessible, is proposed. The growth of position errors in a GNSS-denied condition is such that the navigation methods are not reliable. In the proposed method, instead of the conventional inertial navigation equations, a different dynamic equation based on drag forces is used. Together with the relative position measurements between agents, the acceleration measurements on the plane of the rotors of at least one of the agents are also used in the observation process. For evaluation of the proposed method, an algorithm-in-loop 6DOF simulation environment is developed. The results illustrate that the growth rate of navigation errors is significantly reduced; 97% in the horizontal plane and 40% in the vertical one. In short, integrating the proposed model-based and cooperative navigation methods is more effective than using each of them separately. [ABSTRACT FROM AUTHOR]

The vehicle industry’s primary theme is the increase in automating and integrating more robust intelligent electronic systems to transform towards Industry 4.0, enhance safe functioning with more flexibility, and extend the virtual productive age of the vehicular systems. Further, significant efforts focus on developing monitoring systems for abnormal driving conditions such as wheel spinning and sliding that can increase the operating environment’s hazards and speed up the wearing-off process. Thus, these events play a primary role in influencing the safety of the involved humans and determining the vehicle’s lifespan and the incurred maintenance costs because of the encumbrances by the generated vibrations and shocks. However, the existing measurement systems are not entirely digitized, and their initial development did not concern the concept of functional safety like redundancy to be used in safety-critical environments. Further, the current systems depend on a costly sensory system that requires strict and complex installation procedures. Therefore, the prevention of wheel spinning and sliding events by a safety-related miniaturized digital intelligent monitoring system comes to a corresponding meaning. Accordingly, this research work investigates the development of a novel safety-related platform following the standard IEC 61508 for monitoring and controlling abnormal driving conditions through vibration sensors incorporated with rotation sensors. The research work tests the novel system on a prototype vehicle where the conducted experiments prove the system’s capability to detect the events with reasonable accuracy. Further, this novel approach’s evaluation shows that the system represents a significant enhancement for various similar researches and applications. [ABSTRACT FROM AUTHOR]

In this work, the processing error including the overetching error and residual stresses in the manufacturing process of a microelectromechanical system (MEMS) resonant accelerometer is identified. The performance of the accelerometer is affected by the processing error, especially the residual stress due to the multiple temperature loads in the manufacturing process. Thus, it is essential to identify the processing error for a high-precision MEMS resonant accelerometer. The overetching error is assessed using the sensitivity of the accelerometer resonator. With the multi-step measurement of the resonator frequency variation in the packaging process, the residual stresses of the MEMS accelerometer at different stages are decomposed and identified, combined with surface morphology measurement and finite element analysis simulations. The results of the analysis demonstrate that: 1) The processing error distribution of the accelerometer at the wafer level is related to the morphology of the wafer, which is decided by the low-order main vibrating modes. 2) The residual stress is distributed from high to low from the center to the edge in the wafer. 3) The residual stress at the wafer level is the main part of the MEMS accelerometer when low-stress packaging technology is adopted at the chip level. 4) The variation of the residual stresses in the manufacturing process is a non-monotonic process. The accurate identification of the processing error provides an effective foundation for the improvement and optimization of the MEMS resonant accelerometer. [2022-0041] [ABSTRACT FROM AUTHOR]

In this article, the estimation problem of Toolface is investigated for the dynamic point-the-bit rotary steerable system (DPRSS), and it is essentially a nonlinear filtering problem under strong interference. First, the DPRSS is modeled by a continuous-time nonlinear system, which involves polynomial nonlinearities and strong interference whose amplitude is much larger than that of system states. By employing the Carleman approximation approach, a linear parameter varying system is derived from the polynomial nonlinear system, and a novel polynomial filter is constructed. Subsequently, several sufficient conditions in terms of parameter-dependent linear matrix inequalities (PDLMIs) are obtained to ensure the input-to-state stability of the filtering error system. Furthermore, the filter parameter can be calculated by solving PDLMIs through the sum of squares decomposition method. Finally, an experimental study on the DPRSS prototype is provided to show the effectiveness and applicability of the developed filtering scheme. [ABSTRACT FROM AUTHOR]

Accurate objective, automated limb lameness detection and pose estimation play an important role for animal well-being and precision livestock farming. We present a wearable sensor-based limb lameness detection and pose estimation for horse walk and trot locomotion. The gait event and lameness detection are first built on a recurrent neural network (RNN) with long short-term memory (LSTM) cells. Its outcomes are used in the limb pose estimation. A learned low-dimensional motion manifold is parameterized by a phase variable with a Gaussian process dynamic model. We compare the RNN-LSTM-based lameness detection method with a feature-based multi-layer classifier (MLC) and a multi-class classifier (MCC) that are built on support vector machine/K-nearest-neighbors and deep convolutional neural network methods, respectively. Experimental results show that using only accelerometer measurements, the RNN-LSTM-based approach achieves 95% lameness detection accuracy and also outperforms the feature-based MLC or MCC in terms of several assessment criteria. The pose estimation scheme can predict the 24 limb joint angles in the sagittal plane with average errors less than 5 and 10 degs under normal and induced lameness conditions, respectively. The presented work demonstrate the successful use of machine learning techniques for high performance lameness detection and pose estimation in equine science. Note to Practitioners—Automation technologies are increasingly used for precision agriculture but few have focused on monitoring individual animals in open field for precision livestock farming. Limb lameness detection and pose estimation in open field is labor-intensive, unsafe for farmers, and inefficient. The presented machine learning-enabled, wearable inertial sensor-based design provides an effective and efficient approach for horse limb lameness detection and pose estimation applications. We present an RNN-LSTM for lameness detection and an integrated manifold learning model is used to predict the horse limb joint angles in walk and trot gaits under normal and induced lameness conditions. We also present a systematic analysis and experiments to demonstrate the impacts of the wearable sensor locations and signal information on lameness detection and pose estimation performance. Several other machine learning-based lameness detection methods are also presented and compared. The extensive multi-horse testing results are presented to demonstrate the superior accuracy and higher performance than other types of machine learning methods. One attractive feature of the proposed design lies in its high performance and fast computational capability for potential real-time applications in open field. [ABSTRACT FROM AUTHOR]

Parkinson’s disease (PD) is a chronic, non-reversible neurodegenerative disorder, and freezing of gait (FOG) is one of the most disabling symptoms in PD as it is often the leading cause of falls and injuries that drastically reduces patients’ quality of life. In order to monitor continuously and objectively PD patients who suffer from FOG and enable the possibility of on-demand cueing assistance, a sensor-based FOG detection solution can help clinicians manage the disease and help patients overcome freezing episodes. Many recent studies have leveraged deep learning models to detect FOG using signals extracted from inertial measurement unit (IMU) devices. Usually, the latent features and patterns of FOG are discovered from either the time or frequency domain. In this study, we investigated the use of the time-frequency domain by applying the Continuous Wavelet Transform to signals from IMUs placed on the lower limbs of 63 PD patients who suffered from FOG. We built convolutional neural networks to detect the FOG occurrences, and employed the Bayesian Optimisation approach to obtain the hyper-parameters. The results showed that the proposed subject-independent model was able to achieve a geometric mean of 90.7% and a F1 score of 91.5%. [ABSTRACT FROM AUTHOR]

As one type of the sensors, piezoelectric accelerometer may transform the acceleration signal into the electric signal, which are widely utilized for measurement, monitoring, or controlment. The mostly utilized piezoelectric materials are piezoelectric ceramics [e.g., PZT, ((1 – x)Pb(Mg1/3Nb2/3)O3 – xPbTiO3) (PMN-PT)] for their large piezoelectric stress constant (${d}_{{33}}$). However, their high dielectric permittivity ($\varepsilon _{r}$) leads to the relatively small piezoelectric voltage constant (${g}_{{33}}$), thus the low response sensitivity. In an effort to meet the demand of sensors with high sensitivity, low weight, and small size, in this work, a series of poly(vinylidene-trifluoroethylene) (P(VDF-TrFE)) films are annealed at elevated temperature and fabricated into sensors. Thanks to its rather low permittivity, P(VDF-TrFE) sensor exhibits much larger ${g}_{{33}}$ and sensitivity than that of PMN-PT. Its sensitivity closely depends on the annealing temperature and film thickness. On a hand-made touch sensing instrument, about 10%–20% larger output voltage is obtained in P(VDF-TrFE) sensor than that of PMN-PT under the same stimulating force, although the thickness of P(VDF-TrFE) is only one twenty-fifth of PMN-PT. The results show the excellent piezoelectric sensing performance and great commercial potential of P(VDF-TrFE) for not only its excellent sensitivity but also the feasible processability for thin film in large scale. [ABSTRACT FROM AUTHOR]

Limited by manufacturing technology, in moving-base gravity gradiometry, accelerometer mounting errors and mismatch cause a rotating accelerometer gravity gradiometer (RAGG) to be susceptible to its own motion. In this study, we comprehensively consider accelerometer mounting errors, accelerometer linear scale factors imbalances, accelerometer second-order error coefficients, RAGG internal movement, and construct three RAGG models—namely, a numerical model, an 54 parameters analytical model, and a 25 parameters simplified analytical model. Moreover, by setting the gravitational gradient excitation of RAGG to zero, the RAGG motion error model is derived. The analytical model and motion error model are used to interpret the error propagation mechanism and develop RAGG technical solutions, such as motion error compensation, fault diagnosis, etc. The numerical model is used to facilitate the verification of the RAGG analytical model and RAGG technical solutions. We performed multifrequency gravitational gradient simulation experiment and dynamic sweep frequency experiments to verify the three RAGG models, and evaluate the dynamic noise floor of the analytical models. In dynamic experiments, standard deviation of RAGG vertical specific force grows from 10 to 40 mg (1 g = 9.8 m/s2); that of the lateral specific force grows from 5 to 20 mg; the noise density of the analytical model remains stable within 2.5 E/✓ Hz, and that of the simplified analytical model is in the range of 2.64–41.27 E/✓Hz. [ABSTRACT FROM AUTHOR]

Aiming at the problem of large navigation errors for unmanned vehicle based on MEMS inertial navigation systems (MEMS-INS, MINS) due to low output accuracy of MEMS inertial sensors, a redundant MINS is designed and a high precision positioning algorithm is proposed. In redundant MINS, the output of the redundant gyroscopes is estimated by the improved virtual gyro algorithm based on improved current statistical model and interacting multiple model Kalman filter (ICS-IMMKF). The weighted fusion algorithm is used to fuse the redundant accelerometer information, thus the virtual micro inertial navigation system (Virtual-MINS) is obtained. In the process of vehicle positioning based on virtual MINS, the centripetal acceleration is solved based on the kinematic constraints. Combined with the navigation information of Virtual-MINS and odometer (OD), the difference of centripetal acceleration and velocity are used as observation, and a Virtual-MINS/OD positioning algorithm based on kinematic nonholonomic constraints (NHC/Virtual-MINS/OD) is proposed. The simulation results show that the ICS-IMMKF algorithm can achieve high precision fusion of redundant gyro. In the test, azimuth maneuver is not constrained by the ground, therefore the ICS-IMMKF algorithm can obtain the azimuth rate with high precision. The experimental results show that the root mean square (RMS) of the East and North positioning errors, as well as the RMS of pitch, roll and azimuth estimation error based on the NHC/Virtual-MINS/ OD positioning algorithm are reduced to 34.15%, 22.65%, 55.11%, 64.93% and 41.37% of the NHC/#1-MINS/OD positioning algorithm, respectively. The method will provide a solution for high-precision positioning when GNSS fails. [ABSTRACT FROM AUTHOR]

The available road grade information makes a significant impact on improving the quality of vehicle control. In order to solve the limited application scenario and insufficient accuracy of current road grade estimation methods, this paper presents a novel model-based road grade estimation approach. First, a Quaternion Unscented Kalman Filter (QUKF) using the three-axle angular velocities and three-axle accelerations from a low-cost Inertial Measurement Unit (IMU) and the vehicle speed from CAN bus is designed to estimate the pitch angle of the vehicle. In particular, the measurement noise of UKF is analyzed by integrating Allan variance method. Second, a simplified vehicle-road model is derived to represent the road grade with the estimated pitch angle and longitudinal acceleration. Then, the performance of the proposed algorithm is tested by co-simulation of MATLAB/Simulink and CarSim, which indicates that the error rate of estimation is within 4%. Finally, the feasibility and accuracy of the proposed method implemented in the embedded prototype are verified in experiments conducted on standard slopes. [ABSTRACT FROM AUTHOR]

Growing impact of poststroke upper extremity (UE) functional limitations entails newer dimensions in assessment methodologies. This has compelled researchers to think way beyond traditional stroke assessment scales during the out-patient rehabilitation phase. In concurrence with this, sensor-driven quantitative evaluation of poststroke UE functional limitations has become a fertile field of research. Here, we have emphasized an instrumented wearable for systematic monitoring of stroke patients with right-hemiparesis for evaluating their grasp abilities deploying intelligent algorithms. An instrumented glove housing 6 flex sensors, 3 force sensors, and a motion processing unit was developed to administer 19 activities of Action Research Arm Test (ARAT) while experimenting on 20 voluntarily participating subjects. After necessary signal conditioning, meaningful features were extracted, and subsequently the most appropriate ones were selected using the ReliefF algorithm. An optimally tuned support vector classifier was employed to classify patients with different degrees of disability and an accuracy of 92% was achieved supported by a high area under the receiver operating characteristic score. Furthermore, selected features could provide additional information that revealed the causes of grasp limitations. This would assist physicians in planning more effective poststroke rehabilitation strategies. Results of the one-way ANOVA test conducted on actual and predicted ARAT scores of the subjects indicated remarkable prospects of the proposed glove-based method in poststroke grasp ability assessment and rehabilitation. [ABSTRACT FROM AUTHOR]

Objective: Nocturnal recordings of heart rate and respiratory rate usually require several separate sensors or electrodes attached to different body parts – a disadvantage for at-home screening tests and for large cohort studies. In this paper, we demonstrate that a state-of-the-art accelerometer placed at subjects’ wrists can be used to derive reliable signal reconstructions of heartbeat (pulse wave intervals) and respiration during sleep. Methods: Based on 226 full-night recordings, we evaluate the performance of our signal reconstruction methodology with respect to polysomnography. We use a phase synchronization analysis metrics that considers individual heartbeats or breaths. Results: The quantitative comparison reveals that pulse-wave signal reconstructions are generally better than respiratory signal reconstructions. The best quality is achieved during deep sleep, followed by light sleep N2 and REM sleep. In addition, a suggested internal evaluation of multiple derived reconstructions can be used to identify time periods with highly reliable signals, particularly for pulse waves. Furthermore, we find that pulse-wave reconstructions are hardly affected by apnea and hypopnea events. Conclusion: During sleep, pulse wave and respiration signals can simultaneously be reconstructed from the same accelerometer recording at the wrist without the need for additional sensors. Reliability can be increased by internal evaluation if the reconstructed signals are not needed for the whole sleep duration. Significance: The presented methodology can help to determine sleep characteristics and improve diagnostics and treatment of sleep disorders in the subjects’ normal sleep environment. [ABSTRACT FROM AUTHOR]

With the rapid development of vehicle information systems, liquid crystal displays (LCDs) have been widely used to enhance information experience. A linear vibration motor is usually placed behind the LCD to provide sufficient haptic feedback. A linear vibration motor with a large permanent magnet causes massive flux leakage around the display. As a result, it may cause unpredictable harm to people with implanted medical devices. Therefore, in this article, a novel magnetic circuit design of a slim-width type linear vibration motor is introduced to reduce the static magnetic flux leakage. The haptic performance of the slim-width type linear vibration motor was analyzed using the electromagnetic–mechanical coupling method. Three types of experimental results were obtained to verify the analysis results. In conclusion, compared with the prototype motor, the slim-width type linear vibration motor proposed in this article exhibits significant flux leakage reduction of 94.01% and 96.34% at the measuring positions. Furthermore, the slim-width type linear vibration motor exhibits an acceleration similar to that of the prototype motor. [ABSTRACT FROM AUTHOR]

Recently developed efficient driving schemes usually solve a predictive optimization problem or determining the vehicle control input, and at the expense of high computational cost, they improve the overall traffic flows and individual driving performances on urban roads. This paper presents a more practical technique for automated vehicles’ predictive driving by extending the existing adaptive cruise control (ACC) scheme with a look-ahead functionality. Such a look-ahead driving scheme (LDS) predicts the states of the preceding vehicle at an adaptive look-ahead time step and, with negligible computation costs, computes the vehicle control input more circumspectly for efficient driving in urban traffic. The proposed LDS is evaluated in typical urban traffic at the signalized intersections by observing the intersection utilization, flowing characteristics, and individual vehicles’ fuel efficiency. Furthermore, we also evaluate the influences of the LDS-vehicles’ penetration rates on overall traffic performances at various traffic volumes. [ABSTRACT FROM AUTHOR]

In this article, a new scheme for range-based localization is proposed. The main goal is to estimate the position of a mobile point based on distance measurements from fixed devices, called anchors, and on inertial measurements. Due to the nonlinear nature of the problem, an analytic relation to compute the position starting from these measurements does not exist, and often trilateration methods are used, generally based on least-square algorithms. The proposed scheme is based on the modeling of the localization process as a parallel robot, thereby methodologies and control algorithms used in the robotic area can be exploited. In particular, a closed-loop control system is designed for tracking the position of a mobile point based on range measurements from fixed anchors, and it is shown a peculiar structure of the tracking error dynamics, whose allows an intuitive gain tuning and ensures global exponential stability. Moreover, it is also shown a nice connection between tuning parameters and rate of convergence of the estimation error. Experimental results confirm the validity of the proposed localization method. [ABSTRACT FROM AUTHOR]

Smartphone-based inertial and magnetic sensors can be the basis for pedestrian navigation, whenever external positioning signals are limited or unavailable. Such navigation solutions are typically accomplished by a practice known as pedestrian dead reckoning, wherein the step length and the heading angle are estimated to form the horizontal trajectory of the user. One of the main challenges in these methods is the unknown misalignment between the user’s forward axis and the device’s frame, which imposes a great difficulty in finding the user’s heading. In this article, the problem of estimating the walking direction is addressed by a learning-based approach. Specifically, a novel deep network architecture is designed for extracting the motion vector in the device coordinates using accelerometer measurements. It consists of temporal convolutions and multiscale attention layers and involves a rotation-invariant property that is analytically derived. The network model is integrated with a geometric calculation, employing gravity and geomagnetic directions, to convert the motion vector into the heading angle relative to north. Extensive experiments of natural walking activity were conducted by a single pedestrian, with smartphones placed freely in various pockets. These were used for training the proposed model, in a user-specific approach. Finally, the entire framework was evaluated on unseen data, comparing the deep network against a mechanistic baseline method. Using the proposed network, with merely 5500 parameters, the resulting heading errors had a median value of 9.8°, which was lower by 2.5° compared with the baseline method. [ABSTRACT FROM AUTHOR]

The problem of frequency tuning a cantilever piezoharvester to the typical vibrations of a bicycle is here investigated. This is a key to optimize the energy harvesting performance. Road tests with a bicycle on a typical city track are first carried out to measure the power spectral density of bike vibrations. Both analytical and experimental methods are used to estimate the average electric power that can be harvested by a piezoharvester with a solid tip mass. This solution is compared with a tuning strategy based on a liquid tip mass by using a novel analytical method for simulating frequency response. [ABSTRACT FROM AUTHOR]

To improve the accuracy and robustness of the inertial navigation systems (INS) for wheeled robots without adding additional component cost, we propose Wheel-INS, a complete dead reckoning (DR) solution based on a wheel-mounted microelectromechanical system (MEMS) inertial measurement unit (IMU) in this article. There are two major advantages of mounting IMU to the center of a non-steering wheel. Firstly, the gyroscope outputs can be used to calculate the wheel speed, so as to replace the traditional odometer to mitigate the error drift of INS. Secondly, with the rotation of the wheel, the constant bias error of the inertial sensor can be canceled to some extent. The installation scheme of the wheel-mounted MEMS IMU (Wheel-IMU), the system characteristics, and the DR error analysis are all discussed in this paper. Experimental results show that the maximum position drift of Wheel-INS in the horizontal plane is less than 1.8% of the total traveled distance, reduced by 23% compared to the conventional odometer-aided INS (ODO/INS). In addition, Wheel-INS outperforms ODO/INS because of its inherent immunity to constant bias error of gyroscopes. The source code and experimental datasets have been made available to the community (https://github.com/i2Nav-WHU/Wheel-INS). [ABSTRACT FROM AUTHOR]

This article presents a novel stepped suspended gate field-effect transistor (SSGFET) array-based ${z}$ -axis accelerometer with an enhanced detection range. The stepped gate electrode structure of SGFET aids in extending the stable driving range beyond 33.33% of the initial air gap. The stable driving range is extended to 50% of the initial air gap with ~90% increase in the pull-in voltage. Mechanical, electrical, and electromechanical analytical models are developed. These models are validated through microelectromechanical systems (MEMS) simulations using CoventorMP and transistor simulations in Synopsys TCAD. An SSGFET-based common source (CS) amplifier with diode-connected p-MOSFET load is designed and simulated in Cadence Virtuoso using the lookup table (LUT) approach. The $z$ -axis accelerometer exhibits a sensitivity of 38 (mV/g) with a supply voltage of 3.3 V for a dynamic range (DR) of ±6 g with the nonlinearity of about 5.3% comparedtoSGFETswith a planar gate electrode,whichcan detect up to ±4 g with the same sensitivity. The 3-dB bandwidth of the accelerometer is 412 Hz with a noise-limited resolution of 109.31 $\mu$ g/(Hz)1/2. This article also presents a detailed analysis of the relation between the number of gate fragments, the pull-in voltage, stable driving range, and the DR along with a feasible fabrication integration plan. [ABSTRACT FROM AUTHOR]

In order to decrease the noise and vibration of operation in railway tracks, it is necessary to inspect the status of them frequently. A main task in inspection of railway tracks is measurement of rail corrugation. The rail corrugation occurs during operation due to wear. The dynamic component of the vertical wheel-rail contact force, as induced by rail corrugation is an important source to vibration. Also, according to the corrugation wavelength and railway vehicle speed, a dynamic excitation is generated which is an important source to noise. It is essential to utilize a system to check the rail corrugation. In this article, a new measurement system based on image processing is proposed to perform the corrugation measurement in a reliable manner, while traditional measurement systems are based on acceleration measurement or chord measurement and they are not reliable due to physical contact or need for knowing the characteristics of the track. The proposed system is based on laser triangulation principle using image processing to measure the rail corrugation precisely and quickly. This approach is an efficient technique for high-speed and high-accurate rail corrugation measurement. Experimental results show the capability of the system for practical usages. [ABSTRACT FROM AUTHOR]

In this paper, a liquid-based micro thermal convective accelerometer (MTCA) is optimized by the Rayleigh number (Ra) based compact model and fabricated using the 0.35 μm CMOS MEMS technology. To achieve water-proof performance, the conformal Parylene C coating was adopted as the isolation layer with the accelerated life-testing results of a 9-year-lifetime for liquid-based MTCA. Then, the device performance was characterized considering sensitivity, response time, and noise. Both the theoretical and experimental results demonstrated that fluid with a larger Ra number can provide better performance for the MTCA. More significantly, Ra based model showed its advantage to make a more accurate prediction than the simple linear model to select suitable fluid to enhance the sensitivity and balance the linear range of the device. Accordingly, an alcohol-based MTCA was achieved with a two-order-of magnitude increase in sensitivity (43.8 mV/g) and one-order-of-magnitude decrease in the limit of detection (LOD) (61.9 μg) compared with the air-based MTCA. [2021-0092] [ABSTRACT FROM AUTHOR]

Autonomous land navigation has been studied extensively in recent years to improve vehicle location accuracy in the satellite-denial environment. However, many previous studies of strapdown inertial navigation system/odometer (SINS/ODO) integrated navigation mainly focus on the horizontal location and neglect the height positioning since the troublesome problem of vertical channel divergency without extra height information assistance. In contrast, this article considered both horizontal and vertical positioning accuracy by SINS/ODO/laser doppler velocimeter (LDV)-based integration framework and proposed a novel algorithm named Schmidt ST-EKF which takes advantage of both ST-EKF’s consistency by nonlinear error definition and Schmidt filter’s improvement on system observability. Detailed system equations and observation equations of Schmidt ST-EKF-based SINS/ODO/LDV integrated navigation are derived and followed by concrete experiment results, which showed that the SINS/ODO/LDV integration method had better horizontal location accuracy than SINS/ODO integration by 35% and SINS/LDV integration by 26% and that applying Schmidt ST-EKF could efficiently reduce height positioning error. A further experiment about the correlating time of pitch mounting angle’s influence is conducted and revealed that the Schmidt ST-EKF is more robust than ST-EKF. Consequently, the algorithm proposed in this article for SINS/ODO/LDV has strong engineering applicability in the vast class of land vehicle applications. [ABSTRACT FROM AUTHOR]

The verticality of the borehole determines the firmness of the superstructure, so it is very important to measure the verticality of the borehole. However, the existing static manual measurement technology has low efficiency and poor accuracy. To realize dynamic measurement, reduce cost, and improve measurement accuracy, this article proposes the error mutual correction method for low-cost IMU according to the working characteristics of dynamic and static combination of drill pipe and establishes an error online compensation model and verticality measurement model. The mutual compensation method of gyroscope and accelerometer errors suppresses the accumulation of measurement errors. In addition, the pile digger works in a complex environment, and a verticality measurement sensor that is waterproof, vibration-proof, high-temperature-proof, with online solution and wireless transmission, is designed to improve the reliability of the device. Experiments show that the mutual correction verticality measurement method in this article can achieve a measurement accuracy of 7 ‰($3\sigma $). [ABSTRACT FROM AUTHOR]

In this article, aortic-ejected blood flow and aortic pressure are investigated as an independent tool for diagnosing cardiovascular risk. This study presents vibrocarotidography (ViCG), a novel noninvasive and nonintrusive way to measure aortic blood-flow variations in each heartbeat through carotid arteries. Most of the existing state-of-the-art works suggested to use contact-based pressure sensors and noncontact sensing devices, including wave radar and laser Doppler vibrometer (LDV) for carotid pulse acquisition. However, these sensors have operational design limitations and poor immunity against environmental noises. To address these issues, the proposed method uses a miniaturized and cost-efficient microelectromechanical system (MEMS)-based accelerometer sensor to record the vibrational pulsations on common carotid artery. This article presents our developed electronic circuitry for ViCG signal acquisition and signal processing perspective for estimating indispensable cardiac events. The study focuses to show the ViCG signal as an alternative measure of central blood-flow variations. Significance of the ViCG signal is exhibited by assessing the rate of pulsations and comparing with the heart rhythms measured from the reference electrocardiogram (ECG) and photoplethysmogram (PPG) signals. A quantitative Bland–Altman analysis shows a mean difference (MD) of −0.01 ms and a correlation coefficient of 0.93 ($R$ -squared) between the cardiac intervals measured from the ViCG and ECG signals, whereas they are found to be 0.03 ms and 0.92 for the ViCG–PPG signal pair. They reveal a highly strong correlation and agreement for heart cycle estimation. The performance analysis suggests that the ViCG signal acquired through a simple MEMS-based accelerometer can be utilized as a surrogate of central blood flow measurement and may be employed for continuous health monitoring in personalized-, home-, and hospital-healthcare systems. [ABSTRACT FROM AUTHOR]

There exist two structures of tri-axis rotational inertial navigation systems (TRINSs), namely, the outer-azimuth and inner-azimuth systems. Accurate calibration is one of the key endeavors toward high-precision TRINS. Regarding the TRINS calibration, there is a strict requirement of the navigation platform definition for the rotation excitation sequence. It means that one rotation excitation sequence cannot be applied to both outer-azimuth and inner-azimuth TRINSs. In this article, a virtual platform is constructed that is different from the usual navigation platform, the virtual platform is obtained by performing specific orthogonal transformation according to the requirements of the application scenario: one is to perform an orthogonal transformation on the $g$ -frame and $a$ -frame, and the other is to perform an orthogonal transformation on the $p$ -frame. Based on the self-calibration rotation excitation sequence of the inner-azimuth TRINS, the Kalman filtering is employed to estimate the error parameters in the virtual platform that are then used to calibrate the outer-azimuth TRINS. Real tests with an outer-azimuth TRINS show that the proposed calibration method achieves the scale-factor accuracy of 0.51 ppm and installation accuracy of $0.11''$ for gyroscopes, and the scale-factor accuracy of 1.56 ppm, and the installation accuracy of $0.17''$ for accelerometers, the proposed method can improve the observability of self-calibration. [ABSTRACT FROM AUTHOR]

Attitude information obtained from low-cost magnetic, angular rate, and gravity (MARG) sensors is prone to be deteriorated by disturbances such as external acceleration and magnetic anomaly in operational environments. To address the problem, a robust attitude estimation algorithm using low-cost MARG sensors is proposed. An error state Kalman filter (ESKF) is used for data fusing, in which the attitude errors are interpreted as a small rotation vector, and the gyro bias variation is also estimated. The nominal state and the error state are propagated with unit quaternion and rotation vector, respectively. An attitude correction is followed which combines the nominal and error parts by quaternion multiplication. The robustness of the proposed algorithm is embodied in resisting accuracy degradation of the attitude estimation caused by both external acceleration and magnetic anomalies. While external accelerations and magnetic disturbances are detected online, the accuracy reduction in attitude estimation is prevented by adaptively adjusting the related measurement noise covariance matrix. Besides, a two-step measurement update strategy is designed to guarantee that the roll and pitch update is separated from the yaw update. Various rotation and land vehicle dynamic tests have been conducted to validate the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This article proposes two novel covariance-tuning methods to form a robust Kalman filter (RKF) algorithm for attitude (i.e., roll and pitch) estimation using the measurements of only an inertial measurement unit (IMU). KF-based and complementary filtering (CF)-based approaches are the two common methods for solving the attitude estimation problem. Efficiency and optimality of the KF-based attitude filters are correlated with appropriate tuning of the covariance matrices. Manual tuning process is a difficult and time-consuming task. Specifically, the IMU-only attitude estimation filters are prone to the external accelerations unless their covariances are adapted to gain robustness. The proposed algorithms provide an adaptive method for tuning the measurement noise covariance such that they can accurately estimate the attitude in the two axes. The first method relies on a single tuning factor, whereas the second one tunes the covariance with different (multiple) factors for each measurement axis. The proposed methodologies are tested and compared with other existing filtering algorithms in the literature under different dynamical conditions and using real-world experimental datasets in order to validate their effectiveness. Results show that highly dynamic scenarios, especially the multiple tuning factor strategy, can increase the attitude estimation accuracy more than two-times compared to the competitive algorithms. [ABSTRACT FROM AUTHOR]

Freezing of gait (FoG) leads to imbalance and falls in Parkinson’s disease. Cues potentially prevent FoG occurrence and unfreeze the patient from FoG. Although FoG detection and its prediction have been intensively studied, its termination is mostly neglected. Continuous cueing after the termination of freezing is annoying and has potential side effects in the long term. In this article, for the first time, we attempt to develop a machine learning approach for the prediction of start and termination of freezing, which can potentially provide automated controlled cueing in Parkinson’s individuals. We hypothesize certain attributes that correspond to the transition from walking to freezing and vice versa. To this end, we propose unique labeling of classes to predict freezing termination as follows: no FoG, pre-FoG (immediate state preceding FoG), FoG, and pre of post FoG (state just before unfreezing). Daphnet dataset, freely available online, was utilized to develop customized attributes measured from accelerometers with the data recorded from ten participants. The high-dimensional attributes were reduced using a principal component (PC) analysis before being fed to the k-nearest neighbor (kNN) classifier for prediction. With 45 PCs, we achieved average (SD) precision, sensitivity, specificity, f1 score, and accuracy of 95.55% (4.60%), 94.97% (4.86%), 99.19% (0.85%), 95.25% (4.72%), and 98.92% (1.56%), respectively, across the four classes while utilizing the attributes from 60 data points ($\approx 0.93$ s) prior to a given instant. Specifically, for the newly introduced labeling—pre of post FoG, we observed average (SD) precision, sensitivity, specificity, and f1 score of 92.73% (10.15%), 91.50% (10.34%), 99.83% (0.32%), and 92.10% (10.25%), respectively, with 60 previous data points and 45 PCs. These results show the potential of the present approach in the timely deactivation of cues in real time to avoid any side effects of cueing. Enhanced clinical benefit for Parkinson’s patients is the major contribution of this study. [ABSTRACT FROM AUTHOR]

We realized and tested a novel system aimed at discriminating different hand poses by means of an active actuating and sensing approach realized by converting electromagnetic to mechanical waves (and vice versa) and analyzing the characteristics of the waves after their travel through the bones and cartilage of the hand. The actuating part is realized through a transducer, placed on the dorsal part of the hand, performing electromagnetic-to-mechanical energy conversion. The sensing part is realized through an accelerometer, placed on a finger, performing mechanical-to-electromagnetic energy conversion. The way the sound propagates through the fingers mainly depends on the traveled path, the angles of fingers’ flexion, and the (un)matched conditions with surroundings. Therefore, the investigation of the characteristics of the returned signal (in phase shifts and power spectral densities) can furnish information for discriminating among different finger poses and different densities of touched materials. The system highly performed in repeatability and reproducibility of the measures, well-discriminating among hand poses and among densities of touched materials. [ABSTRACT FROM AUTHOR]

A complete analytical solution to the problem of aligning the sensitivity axes (coordinate frames) of a three-axis accelerometer and a three-axis magnetometer, fixed on the same rigid platform, is introduced. It exploits the magnetic inclination phenomenon to analytically derive the axes alignment rotation matrix and the inclination angle. Starting from a popular formulation of the problem as a constrained optimization one, it introduces a transformation and a parameterization, converting it to an unconstrained one within the special orthogonal group. In contrast to existing methods using the same principle, it guarantees the orthogonality of the axes-alignment rotation matrix and achieves best-of-class accuracy at the same time. It is two orders of magnitude faster than the gradient descent and Newton–Raphson-based methods and about three times faster than state-of-the-art semianalytical approaches achieving the same accuracy. Multiple sets of the sensor data are used to demonstrate the method’s accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

In this article, we introduce a pedestrian dead reckoning (PDR)-based navigation device that does not require global navigation satellite system (GNSS) signals or beacons and works with an inertial measurement unit (IMU) mounted on its waist belt. The system identifies the individual by their walking pattern to use the proper gains in the computations, estimates the attitude by applying an unscented Kalman filter, and finally derives the position in three dimensions with the help of a step detection algorithm. The experimental results show an outdoor 4.7-km walk resulting in an error of 0.96%. [ABSTRACT FROM AUTHOR]

Nano- $g$ accelerometers are widely used in space exploration and measurement of the earth’s gravitational field. It is essential to precisely evaluate error effects at high orders such as cross-coupling for applications in a dynamic environment. Nevertheless, it remains challenging to meet the precision requirements using conventional calibration measures. In this article, we propose a method to separate the cross-coupling coefficients of a linear single-axis accelerometer by mounting it on a steadily rotating rate table that is tilted at a fixed deviation angle with respect to the horizontal plane. The gravity component is periodically modulated along the input axis per revolution. Simultaneously, a series of centripetal acceleration is applied along the cross axis in sequence while adjusting the rotation frequency of the rate table by steps. Thus, the cross-coupling coefficient can be separated by its dependence both on the modulated gravity acceleration and the centripetal acceleration. In comparison to the static multipoint angular rotation test on a tilted dividing head, the proposed dynamic modulation method demonstrates improved robustness against corruption from bias drift, with an improved uncertainty. This method to separate the cross-coupling coefficient is suitable for testing high-resolution accelerometers, without requiring high bias stability or sensitive response sustaining at ultralow frequency. [ABSTRACT FROM AUTHOR]

Self-calibrating an array of 3-D field sensors, such as three-axis magnetometers and accelerometers, requires estimation of two variable sets—each sensor’s intrinsic model that maps its input field to the corresponding measurement and each sensor’s coordinates relative to a common frame of reference within the array. In this work, we propose the first unified self-calibration method for arrays of same-type 3-D field sensors, which is robust to anomalous sensor measurements unlike previous algorithms. The method breaks down the array calibration task into three steps of more easily subproblems, first estimating the intrinsic variables of each sensor independently, second computing the sensor coordinates with respect to a common reference frame, and last refining both these intrinsics and orientations jointly to minimize physically meaningful sensor estimation errors. Each stage has been carefully designed to maintain robustness to anomalies without compromising estimation quality. The performance of our method is compared against other state-of-the-art algorithms on both simulation and real data from a magnetometer array and accelerometer array, demonstrating significant improvements in accuracy and precision of the estimated array variables in versatile real-world self-calibration environments. [ABSTRACT FROM AUTHOR]

At the near-vertical position, the system error is a major factor lowering the attitude accuracy in the attitude determination system, and correction technology is often used to compensate all kinds of system errors. First, the absolute errors of the computed attitude angle including the measurement error and the correction error are analyzed, and the correction error caused by the correction matrix is emphatically derived and discussed. Then, a novel balance correction method with a nonunit correction matrix is proposed and verified theoretically. Finally, an inertial measurement unit (IMU) is tested for the near-vertical position and corrected with the conventional unit matrix and the proposed balance correction. The experimental results suggest that the balance correction under the near-vertical position can enhance the accuracies of the attitude angles over four times than those with the conventional correction. [ABSTRACT FROM AUTHOR]

In this article, a novel methodology to tune parameters of a resonant accelerometer realized by a flexible structure is presented. A model describing the behavior of the tuning mechanism has been proposed and experimentally validated as well as a suitable design flow is presented through case studies. The device under test, whose dimensions are in the mesoscale, adopts a resistive sensing readout strategy implemented through strain gauges, directly inkjet printed on the flexures (springs) supporting the central membrane. The tuning mechanism, which exploits the interaction between an external magnetic field and a magnetic fluid (ferrofluid) embedded in the device’s springs, has been experimentally investigated by using a lab-scale prototype. The procedure for the optimal design of the sensor has also been addressed. The prototype shows a responsivity of 721.0 $\mu $ strain/m/s2 at the resonance frequency of 30.5 Hz and a noise floor of 7.5e $- 5 { \text {m/}}\text {s}^{{2}} {/}\sqrt {\text {Hz}} $ estimated in a bandwidth of 500 Hz. Responsivities of $657.0~\mu $ strain/m/s2 and $580.0~\mu $ strain/m/s2 as well as noise floor of 8.7e $- 5 { \text {m/}}\text {s}^{{2}} {/}\sqrt {\text {Hz}} $ and 10.9e $- 5 { \text {m/}}\text {s}^{{2}} {/}\sqrt {\text {Hz}} $ have been estimated in case of the tuning magnetic fields of $H =6.8\text{e}-3$ T and $H =11.4\text{e}-3$ T, respectively, showing resonance frequencies of 31.0 and 32.5 Hz. The main novelty of the proposed approach arises from the tuning strategy that is important for many real applications requiring a fine-tuning of the sensor parameters while the use of a cheap inkjet printing technology for the device realization represents a substantial advantage in terms of costs and to enable the rapid prototyping of customizable devices on flexible substrates. [ABSTRACT FROM AUTHOR]

In this article, we develop a novel method to improve the accuracy of the low-cost vehicle attitude estimation using singular spectrum analysis (SSA) and independent component analysis (ICA). The microelectromechanical system (MEMS) accelerometers and the MEMS gyros are fused by the Kalman filtering algorithm to provide a low-cost attitude estimation. The low-frequency vibration noise and the maneuvering acceleration disturbance in the measurements of the MEMS accelerometers introduce large errors to the attitude estimation. To mitigate the low-frequency vibration noise, we propose a method based on SSA to separate the quasi-periodic components in accelerometer measurements and keep the trend remained as the true attitude measurement. SSA is effective in decomposing original signal into trend, periodic or quasi-periodic components, and thus, the low-frequency vibration noise that behaves as a quasi-periodic signal can be well separated. However, the acceleration disturbance still exists in the trend. Thus, a method based on ICA is then proposed to remove the acceleration disturbance by the aid of the turning rate measurement. The proposed method is verified with driving tests under city road and off-road conditions, showing a significant improvement of the attitude accuracy. [ABSTRACT FROM AUTHOR]

Shape measurement plays an important role in the condition monitoring and operation control of inflatable rubber dams. This article presents a method to measure the cross-sectional shape of a rubber dam using an array of inertial measurement units (IMUs) placed on the circumference of the dam. Accelerometer and gyroscope measurements are combined using an adaptive complementary filter to determine the tangent angles of the dam circumference. The adaptive complementary filter adjusts the weights of the accelerometer and gyroscope measurements dynamically in order to reduce the uncertainty in orientation estimation due to external acceleration under dynamic conditions. A natural cubic spline that interpolates the measured tangent angles at discrete locations is used to represent the tangent angles along the dam circumference as a continuous function of the arc length. Finally, the cross-sectional shape is reconstructed by integrating the continuous tangent angle function along the circumference of the dam. Experimental assessment of the measurement system was performed on a purpose-built test rig using a digital camera as a reference measuring device. Results under a typical static condition show that the measured and reference shapes agree well with each other, with a similarity index being 3.74%, a mismatch distance of the last IMU node being 12.3 mm, and a relative error of height measurement being −2.44%. Under dynamic conditions, the measurement results deteriorate due to external acceleration, but considerable improvement is achieved in comparison with an accelerometer-only approach. In addition, the elimination of faulty nodes from shape reconstruction has negligible influence on the results, suggesting that the measurement system enjoys a high degree of fault tolerance. [ABSTRACT FROM AUTHOR]

Treatment of low back pain usually includes exercise, analgesics, prostheses, and, in severe cases, surgery. Early treatments based on postural control are essential to prevent low back pain and mitigate permanent damage. We present a wearable device, with an estimated cost below U.S. $100, which uses inertial units with triaxial accelerometers, gyroscopes, and magnetometers to measure the orientation of three sections of the spine. The device integrates the absolute and relative orientation from the sensors to estimate the posture of the back in real-time and uses a fuzzy system to control a vibration unit that indicates the user when to correct the posture of the back. We validated the device in controlled conditions, obtaining an rms deviation ≤1.24° and conducted a preliminary clinical pilot study with patients afflicted by lumbar hyperlordosis or lumbar hypolordosis. We observed an improved postural control and a reduction of low back pain in all cases. These results show a promising potential of the device to reduce pain, improve postural therapies, and raise postural awareness in patients affected with low back pain. [ABSTRACT FROM AUTHOR]

The integrated electronics piezoelectric (IEPE) standard is a common interfacing strategy for industrial accelerometers that are used for vibration and acoustic measurements. The IEPE interface is an untapped resource with opportunities for expanded sensing, including measurements with frequency content down to dc such as temperature or pressure. This article proposes a new IEPE-compatible transducer, built around an embedded acquisition device, which supports a wide range of sensing elements. The proposed system stretches the range of applications for the IEPE interface. In addition, an inexpensive IEPE-compatible microphone is presented, with detailed modeling and experimental verification. A discussion of a condition monitoring system and strategy highlights the benefits of the proposed hardware. [ABSTRACT FROM AUTHOR]

With the development of Internet of Things (IoT), people’s demand for location-based services is increasingly urgent. Based on magnetometer, accelerometer, and rate gyro, i.e., MARG sensors, the position and attitude estimation methods such as complementary filter (CF), Kalman filter (KF), and their various modifications have been the research hot spot. However, the CF-based methods are empirical and lack robustness; the KF-based methods are memory-free observers, whose solution may diverge when the filter lacks uniform observability. In this article, a virtual-measurement-combined extended KF (VMC-EKF) method is proposed by fusing the carrier’s motion state with EKF method. Similar to the graph optimization, the proposed method can measure the key information in VMC phase, and thus remove the requirement of uniform observability. The number of virtual measurements can be estimated based on carrier’s motion state and its gradient, which determines the number of iterations of the prediction phase and the correction phase. In order to verify the performance of the proposed method, a series of numerical simulation experiments, turntable experiments, and foot-mounted experiments are carried out. The corresponding testing platform is set up based on MPU9250, which is a typical and low-cost motion tracking integrated circuit (IC) of MARG sensors. The raw data of MARG sensors can communicate with the host computer via wired or wireless communication, and then be imported into MATLAB for processing and analysis by the compared methods. The test results show that the proposed method can achieve fast convergence of attitude estimation and avoid the divergence of position estimation compared with the state-of-the-art methods. [ABSTRACT FROM AUTHOR]