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With the continuous progress of society, people's living consumption needs are also constantly changing. The traditional vehicle positioning technology can no longer meet the new positioning needs of travelers, so a more accurate positioning system is urgently needed. This paper aims to study a more accurate positioning system and proposes a vehicle positioning system based on radio frequency identification (RFID). After comparing several common positioning methods, the use of LANDMARC positioning algorithm makes the vehicle positioning more accurate. This paper has effectively integrated advanced computer and artificial intelligence technology and applies it to control and management and finally established a comprehensive traffic positioning system that can effectively alleviate traffic congestion, reduce traffic accidents and quickly deal with traffic emergencies. The experimental results have shown that when the vehicle runs at a speed of 40 km/h and the communication rate is 860 kpbs, the average positioning accuracy of the improved algorithm is doubled compared with the traditional algorithm, and the error is reduced by nearly one. Therefore, compared with the traditional positioning system and artificial intelligence, the RFID vehicle positioning system under cloud computing has more accurate positioning information and more timely and convenient services. [ABSTRACT FROM AUTHOR]

Accurately estimating single-axis rotational angle changes is crucial in many high-tech domains. However, traditional angle measurement techniques are often constrained by sensor limitations and environmental interferences, resulting in significant deficiencies in precision and stability. Moreover, current methodologies typically rely on fixed-axis rotation models, leading to substantial discrepancies between measured and actual angles due to axis misalignment. To address these issues, this paper proposes an innovative method for single-axis rotational angle estimation. It introduces a calibration technique for installation errors between inertial measurement units and the overall measurement system, effectively translating dynamic rotational inertial outputs to system enclosure outputs. Subsequently, the method employs triaxial accelerometers combined with zero-velocity detection technology to estimate the rotation axis position. Finally, it delves into analyzing the relationship between quaternion and axis–angle, aimed at reducing noise interference for precise rotational angle estimation. Based on this proposed methodology, a Low-Cost, a High Accuracy Measurement System (HAMS) integrating sensor fusion was designed and implemented. Experimental results demonstrate static measurement errors below ±0.15° and dynamic measurement errors below ±0.5° within a ±180° range. [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]

This paper reports the design, fabrication, and characterization of a MEMS capacitive accelerometer with an asymmetrical comb finger arrangement. By optimizing the ratio of the gaps of a rotor finger to its two adjacent stator fingers, the sensitivity of the accelerometer is maximized for the same comb finger area. With the fingers' length, width, and depth at 120 μm, 4 μm, and 45 μm, respectively, the optimized finger gap ratio is 2.5. The area of the proof mass is 750 μm × 560 μm, which leads to a theoretical thermomechanical noise of 9 μg/√Hz. The accelerometer has been fabricated using a modified silicon-on-glass (SOG) process, in which a groove is pre-etched into the glass to hold the metal electrode. This SOG process greatly improves the silicon-to-glass bonding yield. The measurement results show that the resonant frequency of the accelerometer is about 2.05 kHz, the noise floor is 28 μg/√Hz, and the nonlinearity is less than 0.5%. [ABSTRACT FROM AUTHOR]

In this paper, serial elastic actuators (SEAs) in conjunction with an accelerometer are proposed as force sensors to detect the intention of movement, and the SEA is proposed as a gentle actuator of a patient's upper-limb exoskeleton. A smooth trajectory is proposed to provide comfortable performance. There is an offset trajectory between the link and the motor, which increases safety by preventing sudden movements, and the offset is equivalent to the torsional elastic spring constant. The proposed control law is based on a backstepping approach tested in real-time experiments with robust results in a 2-DoF upper-limb rehabilitation exoskeleton. The experimental results showed a sensitivity of 100% and a positive predictive value of 97.5% for movement intention detection. [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 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 experimentally assesses the efficacy of the indirect Structural Health Monitoring (iSHM) framework on a full‐scale short‐span bridge of nine meters long, using an instrumented vehicle with non‐negligible mass with respect to the mass of the bridge. Emphasis is given to the dynamic identification of the two mechanical systems through Experimental Modal Analysis (EMA) on both the vehicle and the bridge. The EMA vehicle testing is among the main contributions of this paper, as such data become available in experimental iSHM implementations for the first time in the literature. Thus, new insights are brought on the vehicle's dual role as a roving sensing unit and a vibrating mechanical system. A wireless sensor network is adopted that supports a dual monitoring system, i.e., an indirect system with accelerometers on the vehicle and a conventional system with fixed sensors on the bridge. Under a stationary vehicle's position on the bridge, it is shown that a strong dynamic coupling occurs between the two systems due to their high mass ratio and the vehicle's function as a Spring Mass Damper (SMD). In vehicle's moving state, it is demonstrated that transfer of energy occurs between the vehicle and the bridge, which both oscillate under multiple modes of vibration that change over time. It is identified that four main parameters influence the quality of the extracted bridge natural frequencies from the vehicle‐acquired data, i.e., (i) the filtering properties of the vehicle, (ii) the effective signals length in the presence of road discontinuities, (iii) the speed trade‐offs, and (iv) the level of vehicle‐induced bridge excitation and its transmissibility level. The careful consideration of those parameters determines the effectiveness of iSHM implementations in short‐span bridges. [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 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]

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]

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]

Throughout their lifetimes, bridges are exposed to various forces that may lead to displacement and deformation, potentially compromising their structural integrity. Monitoring their structural integrity under environmental factors is essential for safety and maintenance under these conditions. This aspect is a fundamental component of Structural Health Monitoring (SHM). Many studies focus on Structural Health Monitoring (SHM), employing various theories, methodologies, and technologies that have advanced rapidly due to the expansion of information technology. The objective of this study is to pinpoint areas where research is lacking in the existing literature on the environmental factors that impact the displacement of bridges, along with the techniques and technology used to monitor these structures. To achieve this objective, the most critical environmental factors and technologies, particularly those that are sensor-based, have been identified through a systematic search of the most popular databases. Subsequently, the study utilized a bibliometric analysis, exploring the challenge and prospective research areas reflected in the specialized literature. The findings indicate a lack of scholarly investigation of environmental factors that influence the Structural Health Monitoring (SHM) of bridges, in particular studies regarding the effect of uneven sunlight on structures. The research provides a comprehensive understanding of the Structural Health Monitoring (SHM) of bridges and has practical implications for developing effective monitoring methodologies. [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]

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]

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 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]

This paper analyzes the influence of three different shape memory alloy (SMA) wires (i.e., Ni-Ti, Cu-Al-Ni, and Cu-Zn wires) for mitigating model cable vibration. Shape memory alloy (SMA) wires because of their superelasticity or shape memory properties can promise to be one of the effective damping materials in suppressing stay cable oscillations. The investigation explores through experimental works, the capabilities of three Ni-Ti, Cu-Al-Ni, and Cu-Zn SMA wires by estimating the vibration amplitude suppression of the model stay cable. The effective location of the SMA wires on the model cable for vibration suppression was estimated in free as well as forced vibration cases. To impart free and forced vibration, respectively, in the cable, a transducer-based hammer and exciter were used. An accelerometer and data acquisition system was used to record and acquire vibration data. It is also very appropriate to ascertain which wire among one of the three SMA wires performs well. It is found that the Ni-Ti SMA wire has a better performance in vibration suppression as compared to Cu-Al-Ni and Cu-Zn wires. It is also established that the attachment of the SMA wire at the mid-length of the cable gives better vibration suppression for the first mode. [ABSTRACT FROM AUTHOR]

In recent decades, much work has been implemented in heart rate (HR) analysis using electrocardiographic (ECG) signals. We propose that algorithms developed to calculate HR based on detected R-peaks using ECG can be applied to seismocardiographic (SCG) signals, as they utilize common knowledge regarding heart rhythm and its underlying physiology. We implemented the experimental framework with methods developed for ECG signal processing and peak detection to be applied and evaluated on SCGs. Furthermore, we assessed and chose the best from all combinations of 15 peak detection and 6 preprocessing methods from the literature on the CEBS dataset available on Physionet. We then collected experimental data in the lab experiment to measure the applicability of the best-selected technique to the real-world data; the abovementioned method showed high precision for signals recorded during sitting rest (HR difference between SCG and ECG: 0.12 ± 0.35 bpm) and a moderate precision for signals recorded with interfering physical activity—reading out a book loud (HR difference between SCG and ECG: 6.45 ± 3.01 bpm) when compared to the results derived from the state-of-the-art photoplethysmographic (PPG) methods described in the literature. The study shows that computationally simple preprocessing and peak detection techniques initially developed for ECG could be utilized as the basis for HR detection on SCG, although they can be further improved. [ABSTRACT FROM AUTHOR]

This cross-sectional study aimed to identify and validate cut-points for measuring physical activity using Axivity AX6 accelerometers positioned at the shank in older adults. Free-living physical activity was assessed in 35 adults aged 55 and older, where each participant wore a shank-mounted Axivity and a waist-mounted ActiGraph simultaneously for 72 hours. Optimized cut-points for each participant's Axivity data were determined using an optimization algorithm to align with ActiGraph results. To assess the validity between the physical activity assessments from the optimized Axivity cut-points, a leave-one-out cross-validation was conducted. Bland-Altman plots with 95% limits of agreement, intraclass correlation coefficients (ICC), and mean differences were used for comparing the systems. The results indicated good agreement between the two accelerometers when classifying sedentary behaviour (ICC = 0.85) and light physical activity (ICC = 0.80), and moderate agreement when classifying moderate physical activity (ICC = 0.67) and vigorous physical activity (ICC = 0.70). Upon removal of a significant outlier, the agreement was slightly improved for sedentary behaviour (ICC = 0.86) and light physical activity (ICC = 0.82), but substantially improved for moderate physical activity (ICC = 0.81) and vigorous physical activity (ICC = 0.96). Overall, the study successfully demonstrated the capability of the resultant cut-point model to accurately classify physical activity using Axivity AX6 sensors placed at the shank. [ABSTRACT FROM AUTHOR]

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]

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]

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]

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]

Physical activity is the most important lifestyle factor to contribute to a healthy early life. International recommendations are that children should be vigorously physically active for at least 1 h per day, and in Greenland, authorities have claimed a goal of being the world's most active population. Since 1994, the Health Behaviour in School-aged Children (HBSC) has measured physical activity among school-children in Greenland by questionnaire data, but the measurement properties of self-reported data in comparison to more objectively measured accelerometer data are unknown. The present paper describes the first study using clinical measures of physical activity among Greenlandic youth. Seventy-two school-children (56% girls) with a mean age of 12 years (range 25%; 75% was 11.5; 12.25) wore accelerometers for seven consecutive days, and data produced significant and informative findings for both practice and future research. Fifty-two per cent of the school-children met the international recommendations of being physically active >1 h per day, when measured by accelerometers, but self-reported data reported a prevalence of 10% meeting the recommendations. A majority of this sample of Greenlandic school-children lives up to international recommendations, which indicates that the goal of being the world's most active population is within reach. [ABSTRACT FROM AUTHOR]

In the last few years, the manufacturing of microelectromechanical systems (MEMS) by means of innovative tridimensional and bidimensional printing technologies has significantly catalyzed the attention of researchers. Inkjet material deposition, in particular, can become a key enabling technology for the production of polymer-based inertial sensors characterized by low cost, high manufacturing scalability and superior sensitivity. In this paper, a fully inkjet-printed polymeric accelerometer is proposed, and its manufacturing steps are described. The manufacturing challenges connected with the inkjet deposition of SU-8 as a structural material are identified and addressed, resulting in the production of a functional spring-mass sensor. A step-crosslinking process allows optimization of the final shape of the device and limits defects typical of inkjet printing. The resulting device is characterized from a morphological point of view, and its functionality is assessed in performing optical readout. The acceleration range of the optimized device is 0–0.7 g, its resolution is 2 × 10−3 g and its sensitivity is 6745 nm/g. In general, the work demonstrates the feasibility of polymeric accelerometer production via inkjet printing, and these characteristic parameters demonstrate their potential applicability in a broad range of uses requiring highly accurate acceleration measurements over small displacements. [ABSTRACT FROM AUTHOR]

Background: The PROMIS® Early Childhood Physical Activity (PROMIS EC PA) scale is a recently developed PROMIS Early Childhood measure to assess PA among children aged 1–5 years. The purpose of this study was to examine test-retest reliability and convergent validity of the PROMIS EC PA scale among toddlers. Method: An ancillary study was conducted in the toddler-mother dyad sample of the Child and Mother Physical Activity Study. Mothers completed the 7-item PROMIS EC PA scale twice: during a study visit (test) and on the last day when their child's wore an ActiGraph accelerometer on the hip for 7 days (retest). The PROMIS EC PA summed score was calculated by totaling scores from items 1–5. Test-retest reliability was assessed using intraclass correlation coefficient (ICC) for test and retest PROMIS EC PA. Convergent validity was assessed using rank correlation coefficients (rho) between PROMIS EC PA scores and accelerometer-measured moderate- and vigorous-intensity PA (MVPA). Results: Among 74 participants (56% female; 19 ± 4 months of mean age with range of 12–30 months), average accelerometer-measured MVPA was 76 ± 24 min/day. The median number of days between PROMIS EC PA test and retest was 8 days (IQR = 6 to 8), with an average PROMIS EC PA summed score of 11.0 ± 3.5 at test and 10.5 ± 3.4 at retest. ICC for the test-retest PROMIS EC PA summed scores was 0.72 (95% CI = 0.59–0.82). The rank correlation between the PROMIS EC PA summed score and accelerometer-measured MVPA was 0.13 (95% CI=-0.10 to 0.35; p = 0.28). Conclusion: In a sample of children aged 12–30 months, test-retest reliability for the PROMIS EC PA scale was moderate and its convergent validity against accelerometer-measured MVPA was poor. Prior to a widespread use of the PROMIS EC PA scale in large-scale research and clinical practice, the tool should be further refined and validated to elucidate how young children's lived PA experience as measured in the PROMIS EC PA scale is relevant to their health and wellbeing outcomes. [ABSTRACT FROM AUTHOR]

Dynamic compaction is a soil improvement technique which involves the repeated application of an impact load to a contact area of the soil surface induced by heavy weights. Experimental measurements were prepared for evaluation of the ground wave propagation induced by the rapid impact compaction. Influence of increase of subsoil stiffness during compaction and terrain configuration were investigated. When the shear strength of soil is exceeded during the penetration of the compaction footing, increase of peak particle velocities is slower but still clearly visible along the measurement line. The peak particle velocity is damped by the subsoil below 5 mm s−1 within 40–50 m from the source of excitation. Attenuation radius is affected by the terrain configuration, when terrain elevations cause the increase of acceleration amplitudes. Rigid bodies in the ground, such as foundation structures, could have similar influence. Dominant frequencies are in the interval from 20 to 40 Hz regardless the distance from the source of excitation or the density of the treated subsoil. Obtained results are preferably related to the given boundary conditions. However, wave propagation pattern and change of its characteristics during densification of the subsoil are applicable at another construction sites considering their particularities. [ABSTRACT FROM AUTHOR]

It has been hypothesized that key lifestyle behaviors of physical activity and sleep worsened in response to the Coronavirus disease (COVID-19) pandemic. However, there have been inconsistencies in findings of changes in these key lifestyle behaviors across populations likely due to the wide variety of assessment methods. The purpose of the study was to compare physical activity and sleep before and after the COVID-19 pandemic using accelerometers and self-reported behaviors. A longitudinal follow-up was conducted on students, faculty, and staff at a university campus in the United States. In the periods before March 2020 (covering the academic years of 2018-2019 or 2019-2020) and again in April-June 2021, participants completed surveys to evaluate their physical activity and sleep behaviors and wore an accelerometer. A total of 44 participants completed the survey at both timepoints and 32 completed accelerometer assessment at both timepoints. Fifty-seven percent of participants reported a perceived decline in physical activity, while 30% reported a worsening in sleep. From self-reported data, overall physical activity did not change, but there was a decrease in active transport (p < 0.001) and increase in domestic physical activity (p = 0.012). Sleep quality decreased as evidenced by an increase in Pittsburgh Sleep Quality Index scores (p = 0.045). There were no changes in accelerometer measured physical activity or sleep. There were no changes in physical or mental health. While perceptions of physical activity declined from prior to the COVID-19 pandemic, there were no changes in device-measured physical activity, and changes in self-reported physical activity differed by domain. [ABSTRACT FROM AUTHOR]

Background/Objectives: Although there is a need for evidence-based physical activity programs in rural communities, evaluating such programs is often challenging due to access-related barriers and measurement tools that are not designed for rural contexts. This study aimed to explore and better understand the day-to-day experiences of rural-dwelling children using wrist-worn PA trackers as part of a study to develop a health promotion program. Methods: Ten caregivers and child dyads were enrolled (n = 20). The children wore accelerometers pre- and post-intervention. Semi-structured interviews were completed post-intervention and were audio recorded, transcribed, and summary reports were generated based on recurring themes. Results: The children had a mean age of 8.7 (SD = 1.4) years and the majority were male (80%). The caregivers were female, white, and had a mean age of 43.6 (SD = 8.5) years, with an annual income of ≥USD 40,000. Factors contributing to device wear times included low caregiver burden, device functioning as a watch, and device interactivity. The children reported that the devices were acceptable, but may have changed their physical activity behaviors, with children regularly checking their step count. The caregivers preferred devices that monitored the children's activity levels without sharing location data. Conclusions: Identifying acceptable and feasible strategies to measure physical activity is vital to developing effective health promotion efforts. The lessons learned may help develop evaluation plans for implementing rural physical activity programming. [ABSTRACT FROM AUTHOR]

A MEMS piezoresistive sensor for measuring accelerations greater than 100,000 g (about 106 m/s2) is described in this work. To enhance the performance of the sensor, specifically widening its measurement range and natural frequency, a cross-beam construction with a center block was devised, and a Wheatstone bridge was formed by placing four piezoresistors at the ends of the fixed beams to convert acceleration into electricity. The location of the varistor was determined using the finite element approach, which yielded the optimal sensitivity. Additionally, a reliable Pt-Ti-Pt-Au electrode was designed to solve the issue of the electrode failing under high impact and enhancing the stability of the ohmic contact. The accelerometer was fabricated using MEMS technology, and the experiment with a Hopkinson pressure bar and hammering was conducted, and the bias stability was measured. It had a sensitivity of 1.06 μV/g with good linearity. The simulated natural frequency was 633 kHz The test result revealed that the accelerometer can successfully measure an acceleration of 100,000 g. [ABSTRACT FROM AUTHOR]

Animal welfare is a daily concern for livestock farmers. It is known that the activity of cows characterizes their general physiological state and deviations from the normal parameters could be an indicator of different kinds of diseases and conditions. This pilot study investigated the application of machine learning for identifying the behavioral activity of cows using a collar-mounted gyroscope sensor and compared the results with the classical accelerometer approach. The sensor data were classified into three categories, describing the behavior of the animals: "standing and eating", "standing and ruminating", and "laying and ruminating". Four classification algorithms were considered—random forest ensemble (RFE), decision trees (DT), support vector machines (SVM), and naïve Bayes (NB). The training relied on manually classified data with a total duration of 6 h, which were grouped into 1s, 3s, and 5s piles. The obtained results showed that the RFE and DT algorithms performed the best. When using the accelerometer data, the obtained overall accuracy reached 88%; and when using the gyroscope data, the obtained overall accuracy reached 99%. To the best of our knowledge, no other authors have previously reported such results with a gyroscope sensor, which is the main novelty of this study. [ABSTRACT FROM AUTHOR]

Temperature exerts a profound influence on the fidelity of photosensors, making the attainment of reliable temperature compensation a formidable task within engineering realms. This research delves into the intricacies of photosensors used in high-precision accelerometers, proposing an innovative, high-precision, adaptive, closed-loop compensation mechanism. Our design stands in stark contrast to traditional open-loop models, demonstrating superior performance by achieving a remarkable reduction in compensation error—nearly 98%. This advancement in consistency and precision marks a significant leap forward for the application of high-precision photosensors in engineering contexts. [ABSTRACT FROM AUTHOR]

Migraine is one of the most common neurological disorders, characterized by moderate-to-severe headache episodes. Autonomic nervous system (ANS) alterations can occur at phases of migraine attack. This study investigates patterns of ANS changes during the pre-ictal night of migraine, utilizing wearable biosensor technology in ten individuals. Various physiological, activity-based, and signal processing metrics were examined to train predictive models and understand the relationship between specific features and migraine occurrences. Data were filtered based on specified criteria for nocturnal sleep, and analysis frames ranging from 5 to 120 min were used to improve the diversity of the training sample and investigate the impact of analysis frame duration on feature significance and migraine prediction. Several models, including XGBoost (Extreme Gradient Boosting), HistGradientBoosting (Histogram-Based Gradient Boosting), Random Forest, SVM, and KNN, were trained on unbalanced data and using cost-sensitive learning with a 5:1 ratio. To evaluate the changes in features during pre-migraine nights and nights before migraine-free days, an analysis of variance (ANOVA) was performed. The results showed that the features of electrodermal activity, skin temperature, and accelerometer exhibited the highest F-statistic values and the most significant p-values in the 5 and 10 min frames, which makes them particularly useful for the early detection of migraines. The generalized prediction model using XGBoost and a 5 min analysis frame achieved 0.806 for accuracy, 0.638 for precision, 0.595 for recall, and 0.607 for F1-score. Despite identifying distinguishing features between pre-migraine and migraine-free nights, the performance of the current model suggests the need for further improvements for clinical application. [ABSTRACT FROM AUTHOR]

Background: Children have alarmingly low levels of competency in fundamental motor skills (FMS) and high levels of physical inactivity. e:health interventions, interventions delivered electronically, are useful tools for intervention in the home through parents, but less is known about the effects of these interventions in early childhood education centers or settings. Therefore, we created the Motor skills At Playtime (MAP) e:health intervention (e:MAP) to be delivered in an early childhood education setting. The goals of this pilot study on e:MAP are to (1) determine the intervention effects on children's FMS and physical activity and (2) explore the teachers' perceptions and ability to facilitate e:MAP. Methods: This pilot study uses a pretest/posttest randomized cluster control design. We will recruit at least 64 children (3.5–5 years of age) enrolled in a single early childhood education center. Children will be randomly assigned at the level of the classroom to an e:MAP group (n~30) or a control group (n~30). Children in classrooms assigned to e:MAP will complete an 8-week intervention. We will collect measures of child FMS and physical activity, and teacher's perceptions of the program before (pretest) and after the intervention (posttest). FMS measures include process (Test of Gross Motor Development-3rd Edition) and product-oriented scores. Physical activity will be assessed using a 7-day accelerometer wear protocol. Teachers' perceptions will be assessed through a brief survey. Lastly, we will collect data on teachers' ability to facilitate e:MAP through a daily survey. Potential significance: This study will yield novel insights into the effectiveness and feasibility of a health intervention in an early childhood education setting. Results from this work will expand our knowledge of how to harness e:health modalities, which have the potential to significantly expand the distribution and scalability of FMS interventions. [ABSTRACT FROM AUTHOR]

Traditional measurements of gait are typically performed in clinical or laboratory settings where functional assessments are used to collect episodic data, which may not reflect naturalistic gait and activity patterns. The emergence of digital health technologies has enabled reliable and continuous representation of gait and activity in free-living environments. To provide further evidence for naturalistic gait characterization, we designed a master protocol to validate and evaluate the performance of a method for measuring gait derived from a single lumbar-worn accelerometer with respect to reference methods. This evaluation included distinguishing between participants' self-perceived different gait speed levels, and effects of different floor surfaces such as carpet and tile on walking performance, and performance under different bouts, speed, and duration of walking during a wide range of simulated daily activities. Using data from 20 healthy adult participants, we found different self-paced walking speeds and floor surface effects can be accurately characterized. Furthermore, we showed accurate representation of gait and activity during simulated daily living activities and longer bouts of outside walking. Participants in general found that the devices were comfortable. These results extend our previous validation of the method to more naturalistic setting and increases confidence of implementation at-home. [ABSTRACT FROM AUTHOR]

This research aims to develop and validate a fiber Bragg grating (FBG) accelerometer, designed with a bearing and flexure hinge structure, to accurately measure medium‐ and high‐frequency vibrations caused by wheel‐rail excitation. The structural parameters of the accelerometer are optimized through theoretical mechanics analysis, and its dynamic characteristics are verified by experimental vibration testing and compared with the finite element simulated results. Key findings reveal that the proposed sensor has a wide operational frequency range of 10–1200 Hz and a high acceleration sensitivity of 3 pm/m·s−2, in addition to excellent linearity and repeatability. Moreover, the sensor demonstrates immunity to temperature variations, making it suitable for use in fluctuating temperature environments. Laboratory model experiment tests of high‐speed train tracks show that the FBG accelerometer effectively identifies medium‐ to high‐frequency vibration signals caused by wheel‐rail excitation, corroborated by traditional piezoelectric accelerometers. The results confirm the sensor's ability to capture vertical axle box vibration acceleration (ABVA) and its potential for assessing axle box structural dynamics in high‐speed railway applications. [ABSTRACT FROM AUTHOR]