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This paper presents the design and testing of a one-axis piezoelectric accelerometer made from cellulose paper and piezoelectric zinc oxide nanowires (ZnO NWs) hydrothermally grown on paper. The accelerometer adopts a cantilever-based configuration with two parallel cantilever beams attached with a paper proof mass. A piece of U-shaped, ZnO-NW-coated paper is attached on top of the parallel beams, serving as the strain sensing element for acceleration measurement. The electric charges produced from the ZnO-NW-coated paper are converted into a voltage output using a custom-made charge amplifier circuit. The device fabrication only involves cutting of paper and hydrothermal growth of ZnO NWs, and does not require the access to expensive and sophisticated equipment. The performance of the devices with different weight growth percentages of the ZnO NWs was characterized. [ABSTRACT FROM AUTHOR]

OBJECTIVE: To develop an automated algorithm to detect, quantify, and differentiate between tremor using pen-on-paper spirals. METHODS: Patients with essential tremor (n = 25), dystonic tremor (n = 25), Parkinson’s disease (n = 25), and healthy volunteers (HV, n = 25) drew free-hand spirals. The algorithm derived the mean deviation (MD) and tremor variability from scanned images. MD and tremor variability were compared with 1) the Bain and Findley scale, 2) the Fahn–Tolosa–Marin tremor rating scale (FTM–TRS), and 3) the peak power and total power of the accelerometer spectra. Inter and intra loop widths were computed to differentiate between the tremor. RESULTS: MD was higher in the tremor group (48.9±26.3) than in HV (26.4±5.3; p < 0.001). The cut-off value of 30.3 had 80.9% sensitivity and 76.0% specificity for the detection of the tremor [area under the curve: 0.83; 95% confidence index (CI): 0.75, 0.91, p < 0.001]. MD correlated with the Bain and Findley ratings (rho = 0.491, p = 0 < 0.001), FTM–TRS part B (rho = 0.260, p = 0.032) and accelerometric measures of postural tremor (total power, rho = 0.366, p < 0.001; peak power, rho = 0.402, p < 0.001). Minimum Detectable Change was 19.9%. Inter loop width distinguished Parkinson’s disease spirals from dystonic tremor (p < 0.001, 95% CI: 54.6, 211.1), essential tremor (p = 0.003, 95% CI: 28.5, 184.9), or HV (p = 0.036, 95% CI: -160.4, -3.9). CONCLUSION: The automated analysis of pen-on-paper spirals generated robust variables to quantify the tremor and putative variables to distinguish them from each other. SIGNIFICANCE: This technique maybe useful for epidemiological surveys and follow-up studies on tremor. [ABSTRACT FROM AUTHOR]

The article focuses on the application of participation-focused therapy in children with cerebral palsy for improvement in physical activity goal performance, satisfaction, and confidence.

The paper presents the noise analysis of a MEMS and ASIC readout integrated circuit (ROIC) constituting the accelerometer developed in the frame of the InnoReh project, aiming at the development of methods for monitoring patients with imbalance disorders. Several experiments were performed at different temperatures and in different configurations: ROIC alone, ROIC with emulated parasitic capacitances, MEMS and ROIC in separate packages, and MEMS and ROIC in a single package. Many noise/interference sources were considered. The results obtained experimentally were compared to the results of theoretical investigations and were within the same order of magnitude, although in practice, the observed noise was always greater than the theoretical estimation. The paper also includes an in-depth analysis to explain these differences. Moreover, it is argued that, in terms of noise, the MEMS sensing element, and not the ROIC, is the quality-limiting factor. [ABSTRACT FROM AUTHOR]

Accelerometers are mainly used to measure the non-conservative forces at the center of mass of gravity satellites and are the core payloads of gravity satellites. All kinds of disturbances in the satellite platform and the environment will affect the quality of the accelerometer data. This paper focuses on the quality assessment of accelerometer data from the GRACE-FO satellites. Based on the ACC1A data, we focus on the analysis of accelerometer data anomalies caused by various types of disturbances in the satellite platform and environment, including thruster spikes, peaks, twangs, and magnetic torque disturbances. The data characteristics and data accuracy of the accelerometer in different operational states and satellite observation modes are analyzed using accelerometer observation data from different time periods. Finally, the data consistency of the accelerometer is analyzed using the accelerometer transplantation method. The results show that the amplitude spectral density of three-axis linear acceleration is better than the specified accuracy (above 10−1 Hz) in the accelerometer's nominal status. The results are helpful for understanding the characteristics and data accuracy of GRACE-FO accelerometer observations. [ABSTRACT FROM AUTHOR]

This paper illustrates a novel and cost-effective wireless monitoring system specifically developed for operational modal analysis of bridges. The system employs battery-powered wireless sensors based on MEMS accelerometers that dynamically balance power consumption with high processing features and a low-power, low-cost Wi-Fi module that ensures operation for at least five years. The paper focuses on the system's characteristics, stressing the challenges of wireless communication, such as data preprocessing, synchronization, system lifetime, and simple configurability, achieved through the integration of a user-friendly, web-based graphical user interface. The system's performance is validated by a lateral excitation test of a model structure, employing dynamic identification techniques, further verified through FEM modeling. Later, a system composed of 30 sensors was installed on a concrete arch bridge for continuous OMA to assess its behavior. Furthermore, emphasizing its versatility and effectiveness, displacement is estimated by employing conventional and an alternative strategy based on the Kalman filter. [ABSTRACT FROM AUTHOR]

Heavy machinery allows for the efficient, precise, and safe management of large-scale operations that are beyond the abilities of humans. Heavy machinery breakdowns or failures lead to unexpected downtime, increasing maintenance costs, project delays, and leading to a negative impact on personnel safety. Predictive maintenance is a maintenance strategy that predicts possible breakdowns of equipment using data analysis, pattern recognition, and machine learning. In this paper, vibration-based condition monitoring studies are reviewed with a focus on the devices and methods used for data collection. For measuring vibrations, different accelerometers and their technologies were investigated and evaluated within data collection contexts. The studies collected information from a wide range of sources in the heavy machinery. Throughout our review, we came across some studies using simulations or existing datasets. We concluded in this review that due to the complexity of the situation, we need to use more advanced accelerometers that can measure vibration. [ABSTRACT FROM AUTHOR]

Maintenance of mechanical elements is considered to be the most crucial task in industry and affects the economics, predominantly. There are many techniques available for fault diagnosis and condition monitoring of rotatory machine elements and one of the popular tools is vibration monitoring. Analyzing vibrations would be useful in supervising equipment/machine components in an attempt to detect the significant change which is the cause of growing failures or faults. Broadly speaking, it comes under prognostics and health monitoring (PHM) and prevents unexpected failure and costly repair. Proficient predictive monitoring and on-board recognition of failures is a need of the Fourth Industrial Revolution. Several types of vibration analyzers and sensors are commercially available, however, their total budget is not affordable. This motivates reviewing the suitability of low-cost sensors for vibration monitoring. An inclusive review of a variety of applications wherein microelectromechanical system (MEMS)-based accelerometer is used is presented in this paper. This would support an open-design movement for creating open. [ABSTRACT FROM AUTHOR]

Vertical drilling is the first stage of petroleum exploitation and directional well technology. The near-vertical attitude at each survey station directly determines the whole direction accuracy of the borehole trajectory. However, the attitude measurement for near-vertical wells has poor azimuth accuracy because the poor signal-to-noise ratio of radial accelerometers hardly obtains the correct horizontal attitude, especially the roll angle. In this paper, a novel near-vertical attitude measurement method was proposed to address this issue. The redundant micro-electromechanical system (MEMS) accelerometers were employed to replace the original accelerometers from MEMS inertial measurement unit (IMU)/magnetometers for calculating horizontal attitude under near-vertical conditions. In addition, a simplified four-position calibration method for the redundant accelerometers was proposed to compensate for the installation and non-orthogonal error. We found that the redundant accelerometers enhanced the signal-to-noise ratio to upgrade the azimuth accuracy at the near-vertical well section. Compared with the traditional method, the experiment results show that the average azimuth errors and roll errors are reduced from 34.45° and 27.09° to 5.7° and 0.61°, respectively. The designed configuration scheme is conducive to the miniaturized design and low-cost requirements of wellbore measuring tools. The proposed attitude measurement method can effectively improve the attitude accuracy of near-vertical wells. [ABSTRACT FROM AUTHOR]

This paper presents a micromachined thermal convective accelerometer with low power and high reliability. This accelerometer comprises a heater and two thermistors. The central heater elevates the temperature of the chip above ambient levels while the symmetrically arranged thermistors monitor the temperature differentials induced by acceleration. The heater and thermistors are fabricated on a glass substrate using a standard micro-electromechanical systems (MEMS) process flow, and the fabricated sensor is installed on a rotation platform and a shaking table experimental setup to perform the experiment. The results indicate that the sensor has the capability to measure accelerations surpassing 80 m/s2, with an approximate linear sensitivity of 110.69 mV/g. This proposed thermal convective accelerometer offers promising potential for various applications requiring precise acceleration measurements in environments where low power consumption and high reliability are paramount. [ABSTRACT FROM AUTHOR]

This paper presents a non-invasive methodology for structural health monitoring (SHM) integrated with inertial sensors and signal conditioning techniques. The proposal uses the signal of an IMU (inertial measurement unit) tri-axial accelerometer and gyroscope to continuously measure the displacements of a structure in motion due to seismic vibrations. A system, called the "Inertial Displacement Monitoring System" or "IDMS", is implemented to attenuate the signal error of the IMU with methodologies such as a Kalman filter to diminish the influence of white noise, a Chebyshev filter to isolate the frequency values of a seismic motion, and a correction algorithm called zero velocity observation update (ZVOB) to detect seismic vibrations and diminish the influence of external perturbances. As a result, the IDMS is a methodology developed to measure displacements when a structure is in motion due to seismic vibration and provides information to detect failures opportunely. [ABSTRACT FROM AUTHOR]

In this paper, we present an unscented Kalman filter (UKF) for fusion of information from an accelerometer, global navigation satellite system (GNSS) instrumentation, and rotational sensor recordings of structural motion. Seismic and structural motions do not only include translations, but further incorporate torsion and twisting of the ground and/or structural components. Accelerometer and GNSS positions are known to be prone to errors introduced by rotation, such as (1) gravitational leakage, (2) misorientation, and (3) antenna pole tilt. In alleviating such effects, we propose fusion of information from six component (6C) data—3C translation and 3C rotation—and demonstrate its applicability for motion tracking on a flexible pedestrian bridge. To simulate a variety of load effects, the bridge was subjected to various sources of excitation such as hammer impulses, jumping, twisting, and running, as well as a combination thereof named the "artificial coupled forcing." The rotation errors of both the accelerometer and GNSS-estimated positions are corrected via a UKF-based fusion. We further identify the modal properties of the monitored bridge, excited by the different excitation sources, using a covariance driven stochastic subspace identification. The twisting of the bridge is shown to be a primary source of rotation errors. These errors ought to be corrected because their order of magnitude can be as large as the actual signal in the case of GNSS positions and up to 10% for accelerometer sensors. We compare the proposed UKF-based fusion for 6C motion tracking against a simplified linear Kalman filter and demonstrate the potential of the former for real-time, broadband, rotation-free displacement, velocity, and rotations tracking. [ABSTRACT FROM AUTHOR]

In this paper, the viability of MEMS accelerometers is investigated to measure vibration parameters related to different locations of a vehicle with respect to the automotive dynamic functions. The data is collected to compare the accelerometer performances in different locations on the vehicle, including on the hood above the engine, on the hood above the radiator fan, over the exhaust pipe, and on the dashboard. The power spectral density (PSD), together with the time and frequency domain results, confirm the strength and frequencies of the sources of vehicle dynamics. The frequencies obtained from the vibrations of the hood above the engine and radiator fan are approximately 44.18 Hz and 38 Hz, respectively. In terms of the vibration amplitude, the measured amplitudes are between 0.5 g and 2.5 g in both cases. Furthermore, the time domain data collected on the dashboard during driving mode reflects the road condition. Overall, the knowledge obtained from the various tests conducted in this paper can be advantageous for further control and development of vehicle diagnostics, safety, and comfort. [ABSTRACT FROM AUTHOR]

This paper presents the mathematical basis and related procedures for the regression of the upper bound of the dynamic error produced by charge-mode accelerometers. The integral-square error obtained in response to simulation signals with one constraint appearing at the input of the accelerometer is considered. Physical models of accelerometers are presented with related equations and mathematical formulae that make it possible to obtain the error and the corresponding constrained signal. Examples of the regression for predefined values of the accelerometer parameters are also discussed. The solutions presented in this paper may contribute to increasing the accuracy of the charge-mode accelerometers commonly used in energy systems. Development of the functions approximating the integral-square error for the given ranges of accelerometer parameters constitutes the original contribution of this paper. [ABSTRACT FROM AUTHOR]

The piezoelectric coupling principle is widely used (along with capacitive coupling and piezoresistive coupling) for MEMS accelerometers. Piezoelectric MEMS accelerometers are used primarily for vibration monitoring. Polymer piezoelectric MEMS accelerometers offer the merits of heavy-metal-free structure material and simple microfabrication flow. More importantly, polymeric piezoelectric MEMS accelerometers may be the basis of novel applications, such as fully organic inertial sensing microsystems using polymer sensors and organic integrated circuits. This paper presents a novel polymer piezoelectric MEMS accelerometer design using PVDF films. A simple and rapid microfabrication flow based on laser micromachining of thin films and 3D stereolithography was developed to fabricate three samples of this design. During proof-of-concept experiments, the design achieved a sensitivity of 21.82 pC/g (equivalent open-circuit voltage sensitivity: 126.32 mV/g), a 5% flat band of 58.5 Hz, and a noise density of 6.02 µg/√Hz. Thus, this design rivals state-of-the-art PZT-based counterparts in charge sensitivity and noise density, and it surpasses the performance capabilities of several commercial MEMS accelerometers. Moreover, this design has a 10-times smaller device area and a 4-times larger flat band than previous state-of-the-art organic piezoelectric MEMS accelerometers. These experimentally validated performance metrics demonstrate the promising application potential of the polymeric piezoelectric MEMS accelerometer design presented in this article. [ABSTRACT FROM AUTHOR]

This paper presents an extended calibration procedure for mode accelerometers, which makes it possible to compare the accuracy of sensors of this type from different manufacturers. This comparison involves determining the upper bound on dynamic error for a given quality criterion, i.e., the integral square error and absolute error. Therefore, this article extends the standard calibration implemented in engineering practice using tests, providing a value for the upper bound on dynamic error as an additional parameter describing the accelerometer under consideration. This paper presents the theoretical basis for this type of solution, which is partly based on measurement data obtained from a standard calibration process and on the results of parametric identification. The charge mode accelerometer is considered here because this type of sensor is commonly used in the energy industry, as it can operate over a wide range of temperatures. The calculation results presented in this paper were obtained using MathCad 5.0 software, and the tests were carried out using an accelerometer of type 357B21. In the experimental part of this article (Results of Extended Calibration section), values for the upper bound of the dynamic error were determined for two error criteria and constrained simulation signals related to these errors. The impact of interference on the results of accelerometer tests was omitted in this paper. [ABSTRACT FROM AUTHOR]

The aim of this scoping review is to evaluate and summarize the existing literature that considers the validity and/or reliability of smartphone accelerometer applications when compared to 'gold standard' kinematic data collection (for example, motion capture). An electronic keyword search was performed on three databases to identify appropriate research. This research was then examined for details of measures and methodology and general study characteristics to identify related themes. No restrictions were placed on the date of publication, type of smartphone, or participant demographics. In total, 21 papers were reviewed to synthesize themes and approaches used and to identify future research priorities. The validity and reliability of smartphone-based accelerometry data have been assessed against motion capture, pressure walkways, and IMUs as 'gold standard' technology and they have been found to be accurate and reliable. This suggests that smartphone accelerometers can provide a cheap and accurate alternative to gather kinematic data, which can be used in ecologically valid environments to potentially increase diversity in research participation. However, some studies suggest that body placement may affect the accuracy of the result, and that position data correlate better than actual acceleration values, which should be considered in any future implementation of smartphone technology. Future research comparing different capture frequencies and resulting noise, and different walking surfaces, would be useful. [ABSTRACT FROM AUTHOR]

A capacitance-to-voltage converter (CVC) is proposed in this paper and applied to a readout circuit for a micro-electro-mechanical system (MEMS) accelerometer to improve the power efficiency. In a traditional readout circuit, the front-end CVC has to operate at a high sampling frequency to resist thermal noise deterioration due to the large parasitic capacitance introduced by the mechanical sensing element. Thus, the back-end analog-to-digital converter (ADC) also has to operate at a high sampling frequency to avoid noise aliasing when sampling the output signal of the CVC, which leads to high power consumption. The average CVC technique is proposed in this paper to reduce the sampling frequency requirement of the back-end ADC and thus reduce the power consumption. Both the traditional readout circuit and the proposed readout circuit are simulated with a commercial 0.18 μm BCD process. The simulation results show that noise aliasing occurs, and the noise power spectral density (PSD) of the traditional readout circuit increases by 12 dB when the sampling frequency of back-end ADC is reduced by 24 dB. However, in the proposed readout circuit, a noise aliasing effect does not occur. Moreover, the proposed readout circuit reduces the power consumption by 53% without thermal noise deterioration. In addition, the proposed CVC circuits are fabricated in an 0.18 μm BCD process, and the test results show that the presented readout circuit based on the average CVC technique can obtain better performance than the traditional CVC-based readout circuit. [ABSTRACT FROM AUTHOR]

In order to improve the performance of a micro-electro-mechanical system (MEMS) accelerometer, three algorithms for compensating its temperature drift are proposed in this paper, including deep long short-term memory recurrent neural network (DLSTM-RNN, short DLSTM), DLSTM based on sparrow search algorithm (SSA), and DLSTM based on improved SSA (ISSA). Moreover, the piecewise linear approximation (PLA) method is employed in this paper as a comparison to evaluate the impact of the proposed algorithm. First, a temperature experiment is performed to obtain the MEMS accelerometer's temperature drift output (TDO). Then, we propose a real-time compensation model and a linear approximation model for neural network methods compensation and PLA method compensation, respectively. The real-time compensation model is a recursive method based on the TDO at the last moment. The linear approximation model considers the MEMS accelerometer's temperature and TDO as input and output, respectively. Next, the TDO is analyzed and optimized by the real-time compensation model and the three algorithms mentioned before. Moreover, the TDO is also compensated by the linear approximation model and PLA method as a comparison. The compensation results show that the three neural network methods and the PLA method effectively compensate for the temperature drift of the MEMS accelerometer, and the DLSTM + ISSA method achieves the best compensation effect. After compensation by DLSTM + ISSA, the three Allen variance coefficients of the MEMS accelerometer that bias instability, rate random walk, and rate ramp are improved from 5.43 × 10 − 4 mg , 4.33 × 10 − 5 mg / s 1 2 , 1.18 × 10 − 6 mg / s to 2.77 × 10 − 5 mg , 1.14 × 10 − 6 mg / s 1 2 , 2.63 × 10 − 8 mg / s , respectively, with an increase of 96.68% on average. [ABSTRACT FROM AUTHOR]

Temperature variation is an important factor affecting the performance of Quartz flexible accelerometer (QFA). Performance deterioration of QFA degrades the navigation accuracy of inertial navigation system (INS). Normally dramatic change in temperature causes both thermal effect and severe creep effect on the performance of QFA. Previous papers have proved that part of errors caused by thermal effect can be restrained through simple temperature compensation. However, error caused by severe creep effect is seldom considered. In this paper, creep effect and thermal effect in QFA are detailed analyzed, respectively. Furthermore, based on the analysis of thermal effect and creep effect, the novel temperature model based on nonlinear auto-regressive with external input (NARX) improved by wavelet transform (WT) is proposed to address the retardation problem caused by thermal effect. Creep error causing the ruleless deformation of QFA's structure is separated from overall errors, and only the thermal error whose effect has strong relationship with temperature is compensated by proposed model. Moreover, a 4-point dumpling experiment in temperature control oven is conducted to train and verify the proposed temperature model. The result of the comparative experiments shows that the performance of proposed method is the best among the comparative models. The compensation result based on proposed method improves stability of 1 g output from 776 to 14.6 µg. The proposed temperature compensation method improves the performance of QFA effectively and feasibly, which could be promoted to other applications of INS in temperature changing environment. [ABSTRACT FROM AUTHOR]

This paper extends prior work on magnetic and inertial sensor frame calibration with an optimization study for the calibration of tilt-compensated electronic compasses. The novelty of this paper consists in formally demonstrating the optimal calibration orientations that maximize the observability of the various calibration coefficients. The optimal orientations are determined by analyzing the partial derivatives of the cost functions with respect to the calibration coefficients, and finding those orientations that produce their maximum slopes. This approach leads to the preservation of the information needed to determine the calibration coefficients by avoiding geometric dilution. Calibration schemes including optimal orientations for the accelerometers and magnetometers are determined not only for the alignment of the magnetometers and accelerometers sensor reference frames but also for the prerequisite calibrations in the sensor reference frames and partial body frames, respectively. This paper also shows that the optimal accelerometer orientations are different from the optimal magnetometer orientations, however, leaves the analysis of a possible compromise to future work. While confirming some intuitions about possible optimal orientations, this paper also offers an insight into those cases not supported by empirical wisdom, such as the misalignments, eliminating therefore the guesswork from the selection of the calibration profiles. [ABSTRACT FROM AUTHOR]

Accelerometers, devices that measure body movements, have become valuable tools for studying the fragmentation of rest-activity patterns, a core circadian rhythm dimension, using metrics such as inter-daily stability (IS), intradaily variability (IV), transition probability (TP), and self-similarity parameter (named α ). However, their use remains mainly empirical. Therefore, we investigated the mathematical properties and interpretability of rest-activity fragmentation metrics by providing mathematical proofs for the ranges of IS and IV, proposing maximum likelihood and Bayesian estimators for TP, introducing the activity balance index (ABI) metric, a transformation of α , and describing distributions of these metrics in real-life setting. Analysis of accelerometer data from 2,859 individuals (age=60-83 years, 21.1% women) from the Whitehall II cohort (UK) shows modest correlations between the metrics, except for ABI and α . Sociodemographic (age, sex, education, employment status) and clinical (body mass index (BMI), and number of morbidities) factors were associated with these metrics, with differences observed according to metrics. For example, a difference of 5 units in BMI was associated with all metrics (differences ranging between -0.261 (95% CI -0.302, -0.220) to 0.228 (0.18, 0.268) for standardised TP rest to activity during the awake period and TP activity to rest during the awake period, respectively). These results reinforce the value of these rest-activity fragmentation metrics in epidemiological and clinical studies to examine their role for health. This paper expands on a set of methods that have previously demonstrated empirical value, improves the theoretical foundation for these methods, and evaluates their empirical use in a large dataset. [ABSTRACT FROM AUTHOR]

A sleeper, or more generally a "bearer", moves vertically under a passing train load. The extent of this motion depends on the static and dynamic load of the train, the train speed, and the support conditions at the bearer and its neighbours. Excessive motion, typically from voiding see-sawing, low support stiffness or possibly excessive stiffness, or even too little stiffness, are all of interest to maintainers. Typically, problems arise around transition zones, switches and crossings, but plain track with poor support can also be a problem. Within the last decade, low-cost micro-electro-mechanical system (MEMS) accelerometers have been used to capture the time history of vertical motion for use in condition monitoring. Existing condition monitoring systems often overlook or sometimes even ignore the possibility of problematic data, which seem to be common in monitored locations. It is essential to understand whether such "bad" data require further attention. Three problematic sites are presented, focussing on examples where the acceleration was higher than expected or the computed displacement was not as expected. Potential causes include wheel defects, hammering of the ballast by a hanging bearer, or high acceleration at some structural resonant frequency. The present paper aims to show the challenges of using MEMS accelerometers to collect data for condition monitoring and offers insights into the sort of problematic data that may be collected from real sites. [ABSTRACT FROM AUTHOR]

Accelerometers play a crucial role in the railway industry, especially in track monitoring. Traditionally, they are placed on the railway tracks or often on bridges to monitor the health and condition of the infrastructure. Recently, there has been an increased focus on using regular trains to monitor the condition of railway infrastructure. Often, the sensors are placed based on certain assumptions without much scientific evidence or support. This paper utilizes the multibody simulation software GENSYS to identify the optimal placement of accelerometers on a passenger train for monitoring railway switch wear. Switch wear profiles were generated systematically and used as input for the simulations, studying acceleration at a total of 93 locations distributed among the wheelsets, bogies, and carbody. Based on both time and frequency domain analyses, optimal sensor locations were identified, generally close to the first bogie or wheelset at the leading carbody. Accelerations generated by the wheelset passing the switch can also be captured in the carbody, but it is important to note that these are several orders lower in magnitude compared to the acceleration on the wheelset. If accelerometers are to be placed in the carbody, correct sensitivity must be chosen, and a high-pass filter should be applied to capture the acceleration signals associated with switch wear. The study confirms that there is a direct correlation between the depth of switch wear and the magnitude of the acceleration. It remains effective even under various curve radii and train speeds. [ABSTRACT FROM AUTHOR]

The reduction of non‐gravitational accelerations through an onboard accelerometer is a vital part of accurately estimating gravity fields for Gravity Recovery and Climate Experiment (GRACE) Follow‐On (GRACE‐FO). Current accelerometer quality, specifically a defect on the GRACE‐D accelerometer, necessitates the use of extensive calibration techniques to optimally utilize the accelerometer measurements. This paper describes these techniques and correction algorithms as they are used for the official GRACE‐FO accelerometer products, which rest on the information present in the partially functional GRACE‐D accelerometer. First, transplanted data from GRACE‐C is used to remove common effects between the spacecraft. Then, GRACE‐D data is utilized to compute a correction to the transplanted data to account for solar radiation pressure (SRP) and albedo using a parity argument and to account for non‐common drag and thruster leaks in the attitude control system. The SRP and albedo term predominantly improves C3,0 while the drag and leaky thruster term improves the gravity field across the entire spectrum. Finally, some comments on alternative data usage, initial modifications to the algorithms to account for other spacecraft operational modes, and future outlook are provided. Key Points: GRACE‐D accelerometer data, while noisy, is calibrated to recover useable signals and optimize science dataUpdated accelerometer calibrations improve gravity field recovery [ABSTRACT FROM AUTHOR]

Today, identity verification is required in many common activities, and it is arguably true that most people would like to be authenticated in the easiest and most transparent way, without having to remember a personal identification number. To this regard, this paper presents a multibiometric system based on the observation that the instinctive gesture of responding to a phone call can be used to capture two different biometrics, namely ear and arm gesture, which are complementary due to their, respectively, physical and behavioral nature. We conducted a comprehensive set of experiments aimed at assessing the contribution of each of the two biometrics as well as the advantage in their fusion to the system’s overall performance. Experiments also provide objective measurement of both saliency and correlation of data captured by each sensor involved (accelerometer, gyroscope, and camera) according to various features extraction, features matching, and data-fusion techniques. The reports provide evidences about the potential of the proposed system and method for user authentication “in-the-wild,” whilst its eventual usage for person identification is also investigated. All of the experiments have been carried out on a specifically built, publicly available ear–arm database, including multibiometric captures of more than 100 subjects performed during different sessions, that represents an additional contribution of this paper. [ABSTRACT FROM AUTHOR]

This article deals with the issue of online chatter detection during milling. The aim is to achieve a verification of the reliability and robustness of selected methods for the detection of chatter that can be evaluated on the machine tool in real time by using the accelerometer signal. In the introductory part of the paper, an overview of the current state of the art in the field of chatter detection is summarized. Entropic methods have been selected that evaluate the presence of chatter from the qualitative behavior of the signal rather than from the magnitude of its amplitude, because the latter can be affected by the transmission of vibrations to the accelerometer position. Another criterion for selection was the potential for practical implementation in a real-time evaluation of the accelerometer signal, which is nowadays quite commonly installed on machine tools. The robustness of the methods was tested with respect to tool compliance, which affects both chatter occurrence and vibration transfer to the accelerometer location. Therefore, the study was carried out on a slender milling tool with two different overhangs and on a rigid roughing tool. The reference stability assessment for each measurement was based on samples of the machined surface. The signals obtained from the accelerometer were then post-processed and used to calculate the chatter indicators. In this way, it was possible to compare different methods in terms of their ability to achieve reliable in-process detection of chatter and in terms of the computational complexity of the indicator. [ABSTRACT FROM AUTHOR]

This extensive review examines sarcopenia, a condition characterized by a loss of muscle mass, stamina, and physical performance, with a particular emphasis on its detection and management using contemporary technologies. It highlights the lack of global agreement or standardization regarding the definition of sarcopenia and the various techniques used to measure muscle mass, stamina, and physical performance. The distinctive criteria employed by the European Working Group on Sarcopenia in Older People (EWGSOP) and the Asian Working Group for Sarcopenia (AWGSOP) for diagnosing sarcopenia are examined, emphasizing potential obstacles in comparing research results across studies. The paper delves into the use of machine learning techniques in sarcopenia detection and diagnosis, noting challenges such as data accessibility, data imbalance, and feature selection. It suggests that wearable devices, like activity trackers and smartwatches, could offer valuable insights into sarcopenia progression and aid individuals in monitoring and managing their condition. Additionally, the paper investigates the potential of blockchain technology and edge computing in healthcare data storage, discussing models and systems that leverage these technologies to secure patient data privacy and enhance personal health information management. However, it acknowledges the limitations of these models and systems, including inefficiencies in handling large volumes of medical data and the lack of dynamic selection capability. In conclusion, the paper provides a comprehensive summary of current sarcopenia research, emphasizing the potential of modern technologies in enhancing the detection and management of the condition while also highlighting the need for further research to address challenges in standardization, data management, and effective technology use. [ABSTRACT FROM AUTHOR]

The section detection of the pavement is the data basis for measuring the road smoothness, rutting, lateral slope, and structural depth. The detection of the Pavement-Section includes longitudinal-section inspection and cross-section inspection. In this paper, based on multiple laser displacement sensors, fused accelerometers and attitude sensors, and using vehicle-mounted high-speed detection, we design a sensor-fused pavement section data acquisition method, establish the relevant mathematical model, and realize the automatic acquisition of pavement longitudinal and transverse sections. The acceleration sensor is filtered to improve the accuracy of data acquisition, and the error of the detection system is calculated and analyzed. Through the actual measurement, the vehicle-mounted high-speed pavement profile detection method adopted in this paper can not only accurately detect the profile of the pavement profile, but also improve the detection efficiency, providing a cost-effective detection mode for road surface detection. [ABSTRACT FROM AUTHOR]

This paper presents a MEMS electrochemical angular accelerometer with a silicon-based four-electrode structure, which was made of thousands of interconnected microchannels for electrolyte flow, anodes uniformly coated on structure surfaces and cathodes located on the sidewalls of flow holes. From the perspective of device fabrication, in this study, the previously reported multi-piece assembly was simplified into single-piece integrative manufacturing, effectively addressing the problems of complex assembly and manual alignment. From the perspective of the sensitive structure, in this study, the silicon-based four-electrode structure featuring with complete insulation layers between anodes and cathodes can enable fast electrochemical reactions with improved sensitivities. Numerical simulations were conducted to optimize the geometrical parameters of the silicon-based four-electrode structure, where increases in fluid resistance and cathode area were found to expand working bandwidths and improve device sensitivity, respectively. Then, the silicon-based four-electrode structure was fabricated by conventional MEMS processes, mainly composed of wafer-level bonding and wafer-level etching. As to device characterization, the MEMS electrochemical angular accelerometer with the silicon-based four-electrode structure exhibited a maximum sensitivity of 1458 V/(rad/s2) at 0.01 Hz and a minimum noise level of −164 dB at 1 Hz. Compared with previously reported electrochemical angular accelerometers, the angular accelerometer developed in this study offered higher sensitivities and lower noise levels, indicating strong potential for applications in the field of rotational seismology. [ABSTRACT FROM AUTHOR]

Estimation of temporospatial clinical features of gait (CFs), such as step count and length, step duration, step frequency, gait speed, and distance traveled, is an important component of community-based mobility evaluation using wearable accelerometers. However, accurate unsupervised computerized measurement of CFs of individuals with Duchenne muscular dystrophy (DMD) who have progressive loss of ambulatory mobility is difficult due to differences in patterns and magnitudes of acceleration across their range of attainable gait velocities. This paper proposes a novel calibration method. It aims to detect steps, estimate stride lengths, and determine travel distance. The approach involves a combination of clinical observation, machine-learning-based step detection, and regression-based stride length prediction. The method demonstrates high accuracy in children with DMD and typically developing controls (TDs) regardless of the participant's level of ability. Fifteen children with DMD and fifteen TDs underwent supervised clinical testing across a range of gait speeds using 10 m or 25 m run/walk (10 MRW, 25 MRW), 100 m run/walk (100 MRW), 6-min walk (6 MWT), and free-walk (FW) evaluations while wearing a mobile-phone-based accelerometer at the waist near the body's center of mass. Following calibration by a trained clinical evaluator, CFs were extracted from the accelerometer data using a multi-step machine-learning-based process and the results were compared to ground-truth observation data. Model predictions vs. observed values for step counts, distance traveled, and step length showed a strong correlation (Pearson's r = −0.9929 to 0.9986, p < 0.0001). The estimates demonstrated a mean (SD) percentage error of 1.49% (7.04%) for step counts, 1.18% (9.91%) for distance traveled, and 0.37% (7.52%) for step length compared to ground-truth observations for the combined 6 MWT, 100 MRW, and FW tasks. Our study findings indicate that a single waist-worn accelerometer calibrated to an individual's stride characteristics using our methods accurately measures CFs and estimates travel distances across a common range of gait speeds in both DMD and TD peers. [ABSTRACT FROM AUTHOR]

This paper presents an innovative integrated sensor that combines GNSS and a low-cost accelerometer for bridge health monitoring. GNSS and accelerometers are both significant and effective sensors for structural monitoring, but they each have limitations. The sampling rate of GNSS data is relatively low, making it challenging to capture high-frequency vibrations, while accelerometers struggle with low-frequency signals and are susceptible to environmental changes. Additionally, GNSS receivers and accelerometers are often installed separately, leading to challenges in data fusion processing due to differing temporal and geospatial references. The proposed integrated sensor addresses these issues by synchronizing GNSS and an accelerometer's time and geospatial coordinate reference. This allows for a more accurate and reliable deformation and vibration measurement for bridge monitoring. The performance of the new sensor was assessed using a high-quality/cost Leica GM30 GNSS receiver and a Sherborne A545 accelerometer. Experiments conducted on the Wilford suspension bridge demonstrate the effectiveness of this innovative integrated sensor in measuring deformation and vibration for bridge health monitoring. The limitation of the low-cost MEMS (Micro Electromechanical System) accelerometer for the weak motion frequency detection is also pointed out. [ABSTRACT FROM AUTHOR]

This paper reports on noise modeling of a piezoelectric charge accelerometer with a signal conditioning circuit. The charge output is converted into voltage and amplified using a JFET operational amplifier that has high input resistance and low noise. The noise sources in the whole system include electrical and mechanical thermal noises of the accelerometer, thermal noises of resistors, and voltage and current noises of the operational amplifier. Noise gain of each source is derived from small signal circuit analysis. It is found that the feedback resistor of the operational amplifier is a major source of noise in low frequencies, whereas electrical thermal noise of the accelerometer dominates the rest of spectrum. This method can be used to pair a highly sensitive sensor with a single JFET operational amplifier instead of a multi-stage signal conditioning circuit. [ABSTRACT FROM AUTHOR]

This paper characterizes the sensitivity of a time domain MEMS accelerometer. The sensitivity is defined by the increment in the measured time interval per gravitational acceleration. Two sensitivities exist, and they can be enhanced by decreasing the amplitude and frequency. The sensitivity with minor nonlinearity is chosen to evaluate the time domain sensor. The experimental results of the developed accelerometer demonstrate that the sensitivities span from −68.91 μs/g to −124.96 μs/g and the 1σ noises span from 8.59 mg to 6.2 mg (amplitude of 626 nm: −68.91 μs/g and 10.21 mg; amplitude of 455 nm: −94.51 μs/g and 7.76 mg; amplitude of 342 nm: −124.96 μs/g and 6.23 mg), which indicates the bigger the amplitude, the smaller the sensitivity and the bigger the 1σ noise. The adjustable sensitivity provides a theoretical foundation for range self-adaption, and all the results can be extended to other time domain inertial sensors, e.g., a gyroscope or an inclinometer. [ABSTRACT FROM AUTHOR]

This paper will introduce a simple locating system to track a stent when it is deployed into a human artery. The stent is proposed to achieve hemostasis for bleeding soldiers on the battlefield, where common surgical imaging equipment such as fluoroscopy systems are not available. In the application of interest, the stent must be guided to the right location to avoid serious complications. The most important features are its relative accuracy and the ease by which it may be quickly set up and used in a trauma situation. The locating approach in this paper utilizes a magnet outside the human body as the reference and a magnetometer that will be deployed inside the artery with the stent. The sensor can detect its location in a coordinate system centered with the reference magnet. In practice, the main challenge is that the locating accuracy will be deteriorated by external magnetic interference, rotation of the sensor, and random noise. These causes of error are addressed in the paper to improve the locating accuracy and repeatability under various conditions. Finally, the system's locating performance will be validated in benchtop experiments, where the effects of the disturbance-eliminating procedures will be addressed. [ABSTRACT FROM AUTHOR]

This paper presents a novel method for the performance of an all-silicon accelerometer by adjusting the ratio of the Si-SiO2 bonding area, and the Au-Si bonding area in the anchor zone, with the aim of eliminating stress in the anchor region. The study includes the development of an accelerometer model and simulation analysis which demonstrates the stress maps of the accelerometer under different anchor–area ratios, which have a strong impact on the performance of the accelerometer. In practical applications, the deformation of the comb structure fixed by the anchor zone is influenced by the stress in the anchor region, causing a distorted nonlinear response signal. The simulation results demonstrate that when the area ratio of the Si-SiO2 anchor zone to the Au-Si anchor zone decreases to 0.5, the stress in the anchor zone decreases significantly. Experimental results reveal that the full-temperature stability of zero-bias is optimized from 133 μg to 46 μg when the anchor–zone ratio of the accelerometer decreases from 0.8 to 0.5. At the same time, the full-temperature stability of the scale factor is optimized from 87 ppm to 32 ppm. Furthermore, zero-bias full-temperature stability and scale factor full-temperature stability are improved by 34.6% and 36.8%, respectively. [ABSTRACT FROM AUTHOR]

Human body posture recognition has become the focus of many researchers in recent years. Recognition of body posture is used in various applications, including surveillance, security, and health monitoring. However, these systems that determine the body's posture through video clips, images, or data from sensors have many challenges when used in the real world. This paper provides an important review of how most essential hardware technologies are used in posture recognition systems. These systems capture and collect datasets through accelerometer sensors or computer vision. In addition, this paper presents a comparison study with state-of-the-art in terms of accuracy. We also present the advantages and limitations of each system and suggest promising future ideas that can increase the efficiency of the existing posture recognition system. Finally, the most common datasets applied in these systems are described in detail. It aims to be a resource to help choose one of the methods in recognizing the posture of the human body and the techniques that suit each method. It analyzes more than 80 papers between 2015 and 2020. [ABSTRACT FROM AUTHOR]

We used the Swarm-C accelerometer data to invert the orbital atmospheric density in this study. First, the Swarm-C satellite mission data were obtained from the ESA's public data platform, and preliminary data error correction was performed. This paper refers to the calibration method of GRACE-A satellite accelerometer data. It adds linear temperature correction on the original basis. Moreover, this study's accelerometer data correction results were compared with the data correction results published by the ESA. In order to explore the influence of light radiation on the accelerometer, we established a geometric model of Swarm-C to simulate the physical shape of the satellite surface. The light radiation pressure model and the shadow area judgment model were established, the change in the light radiation acceleration during the transition process of the satellite from the umbra area to the penumbra area and then to the shadowless area was studied, and the state transition during the transition process was analyzed. Finally, the atmospheric drag coefficient was calculated based on the Sentman model. Atmospheric density inversion calculations were performed using the above data. We show the spatial distribution of atmospheric density at a fixed latitude, testing our results during geomagnetic storms. We compared the density results with existing research data, demonstrating the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

The application of MEMS accelerometers used to measure inclination is constrained by their temperature dependence, and each accelerometer needs to be calibrated individually to increase stability and accuracy. This paper presents a calibration and thermal compensation method for triaxial accelerometers that aims to minimize cost and processing time while maintaining high accuracy. First, the number of positions to perform the calibration procedure is optimized based on the Levenberg-Marquardt algorithm, and then, based on this optimized calibration number, thermal compensation is performed based on the least squares method, which is necessary for environments with large temperature variations, since calibration parameters change at different temperatures. The calibration procedures and algorithms were experimentally validated on marketed accelerometers. Based on the optimized calibration method, the calibrated results achieved nearly 100 times improvement. Thermal drift calibration experiments on the triaxial accelerometer show that the thermal compensation scheme in this paper can effectively reduce drift in the temperature range of −40 °C to 60 °C. The temperature drifts of x- and y-axes are reduced from −13.2 and 11.8 mg to −0.9 and −1.1 mg, respectively. The z-axis temperature drift is reduced from −17.9 to 1.8 mg. We have conducted various experiments on the proposed calibration method and demonstrated its capacity to calibrate the sensor frame error model (SFEM) parameters. This research proposes a new low-cost and efficient strategy for increasing the practical applicability of triaxial accelerometers. [ABSTRACT FROM AUTHOR]

The fourth European Space Agency Earth Explorer (Swarm) was launched in 2013 and it comprises a constellation of three identical satellites. The primary mission objective is analyzing and modeling the geomagnetic field and its temporal evolution, based on the measurements of a vector and a scalar magnetometer on board. Each spacecraft (A, B, and C) carries an accelerometer, supposed to measure the non‐gravitational forces. However, this instrument did not work as expected; the processing of the related data sets has a significant latency. The focus was primarily on Swarm C, because its signal‐to‐noise ratio is the best amongst the three spacecrafts. For this satellite, the data are available for the whole mission timeline. At the end of 2021, a batch of Swarm A accelerometer data set was disseminated. Swarm A and C have flown side‐by‐side for most of the mission and, therefore, their measurements are supposed to be nearly identical after calibration. The work presented in this paper shows that such a correspondence occurs: at high frequency similar signatures are visible in both accelerometer data sets, near the equator and the poles. These features agree with measurements of other instruments on board and with earlier findings based on CHAllenging Minisatellite Payload and Gravity Recovery and Climate Experiment data. The equatorial structures show a correlation between the equatorial mass anomaly and the equatorial ionization anomaly. At the poles, the data are related to the polar cap index and to field‐aligned‐currents enhancements measured by the magnetometers on board. This research shows, for the first time, scientific results obtained from the Swarm accelerometers. Key Points: European Space Agency Swarm accelerometers data deliver for the first time scientific results in the field of thermosphere and ionosphere couplingEquatorial ionization anomaly and equatorial mass anomaly coupling visible when comparing Swarm accelerometers and Langmuir probes data setsA statistical relationship between observed field aligned current and neutral density variations is observed in Swarm accelerometers and magnetometers data [ABSTRACT FROM AUTHOR]

The study of the vibration phenomenon of pebbles under turbulence is still a gap despite recent technological advancements in measurement capabilities. In this study, the vibration process of a fully exposed, isolated smart pebble on a rough bed was measured using a miniature inertial accelerometer and combined with simultaneous local measurements of the near-bed velocities. The paper conducts a series of experimental studies with different flow conditions. The test data match well with the manually observed phenomena, indicating the authenticity of the sediment vibration data collected by the measurement system. The test results show that the pebble motion (before entrainment) subjected to turbulence is a nonlinear vibration process, and its vibration types include in-situ vibration and ex-situ strong vibration. The probability distribution for the amplitude of vibration acceleration is well parameterized by the normal distribution. The vibration intensity tends to increase before approaching the threshold, but it weakens when approaching the point. The sediment vibration frequency is within 20–25 Hz, similar to the flow pulsation frequency and belongs to low-frequency vibration. The data indicate that the near-bed flow velocity is most directly related to the particle vibration events. [ABSTRACT FROM AUTHOR]

In this paper, a means of generating residuals based on a fault isolation observer (FIO) and evaluating them using a support vector machine (SVM) is proposed. The proposed FIO generates the isolated residual signals and they shows robust performance regardless of unknown road surface conditions. This FIO is designed using a linear time-invariant quarter-car model. While quarter-car models have the form of a bilinear system, in this study the authors convert this bilinear model to a linear model with model uncertainty based on the assumption that the control input is limited. Therefore, the proposed FIO can be used regardless of the type of damper or controller. Furthermore, the SVM based residual evaluator without empirically set thresholds is used to evaluate the generated residuals. The proposed fault diagnosis algorithm is expected to reduce the effort required in the design procedure and it can also detect a small amount of sensor fault that cannot be detected by traditional limit-checking method. The proposed fault diagnosis algorithm is verified using low cost production accelerometers and a quarter-car test rig. Consequently, the fault diagnosis algorithm proposed in this paper can detect the faults of a sprung mass accelerometer and an unsprung mass accelerometer independently, and this algorithm can reduce the effort required in designing the diagnosis algorithm greatly. [ABSTRACT FROM AUTHOR]

This paper is a nontrivial extension of prior work on rate and position table leveling and inertial and magnetic sensors calibration techniques. The table leveling is essential for a class of calibration techniques using known stimuli, herein referred to as classical techniques. Such techniques are effective for the calibration of inertial sensors as they usually lead to closed-form solutions and body frame calibrations. However, the classical techniques are negatively affected by table leveling and azimuth orientation errors or by changes in the direction of the reference field. These errors are collectively referred to as field alignment errors in this paper. The impact of field alignment errors is usually avoided by using reference-free calibrations which rely on satisfying the invariance of the field modulus constraint. This constraint works well for orthogonal sensor triads as it leads to unique solutions. There is a great body of work dedicated to studying such calibration techniques for orthogonal sensor triads. However, redundant sensor configurations have too many degrees of freedom to satisfy the invariance of the modulus constraint; therefore, require special techniques. This paper introduces a novel approach that can be used to produce body frame calibrations for skew redundant sensor configurations in the presence of field alignment errors. Unlike prior work, no simplifying assumptions are made on the redundant sensor configuration (e.g., multiple orthogonal triads). The new method is showcased for the dodecahedron configuration, one of the most complex skew redundant configurations. [ABSTRACT FROM AUTHOR]

This paper proposes a novel algorithm to determine the optimal orientation of sensing axes of redundant inertial sensors such as accelerometers and gyroscopes (gyros) for increasing the sensing accuracy. In this paper, we have proposed a novel iterative algorithm to find the optimal sensor configuration. The proposed algorithm utilizes the majorization-minimization (MM) algorithm and the duality principle to find the optimal configuration. Unlike the state-of-the-art approaches which are mainly geometrical in nature and restricted to sensors’ noise being uncorrelated, the proposed algorithm gives the exact orientations of the sensors and can easily deal with the cases of correlated noise. The proposed algorithm has been implemented and tested via numerical simulation in the MATLAB. The simulation results show that the algorithm converges to the optimal configurations and shows the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]