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Some applications, such as aerospace testing and monitoring the operating conditions of equipment on space missions, require mechanical sensors capable of measuring accelerations at frequencies of several hundred hertz. For such measurements, optomechanical sensors can be used, providing the ability to measure accelerations without calibration. To enable such measurements, improved designs of drum-type sensors with the assigned performance have been elaborated. Such designs make it possible to provide the necessary levels of natural frequencies for optomechanical sensors and eliminate crosstalk. Using mathematical modeling, the dependencies of the mechanical characteristics of the proposed types of acceleration sensors versus their parameters were obtained. The use of such sensor designs ensures their compactness, making their manufacturing more technologically sound and suitable for use, in particular, in space missions. [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]

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 study investigates the feasibility and functionality of accelerometer and gyroscope sensors for gesture-based interactions in mobile app user experience. The core of this innovative approach lies in introducing a dynamic and intuitive user interaction model with the device sensors. The Android app developed for this purpose has been created for its use in controlled experiments. Methodologically, it was created as a stand-alone tool to both capture quantitative (time, automatically captured) and qualitative (behavior, collected with post-task questionnaires) variables. The app's setting features a set of modules with two levels each (randomized presentation applied, minimizing potential learning effects), allowing users to interact with both sensor-based and traditional touch-based scenarios. Preliminary results with 22 participants reveal that tasks involving sensor-based interactions tend to take longer to complete when compared to the traditional ones. Remarkably, many participants rated sensor-based interactions as a better option than touch-based interactions, as seen in the post-task questionnaires. This apparent discrepancy between objective completion times and subjective user perceptions requires a future in-depth exploration of factors influencing user experiences, including potential learning curves, cognitive load, and task complexity. This study contributes to the evolving landscape of mobile app user experience, emphasizing the benefits of considering the integration of device sensors (and gesture-based interactions) in common mobile usage. [ABSTRACT FROM AUTHOR]

Ocean waves play a significant role in coastal dynamics and the management of coastal development activities. However, there are limited instruments to measure the wave height, particularly in developing countries. This study introduces a novel instrument design for simple and cost-effective wave height measurement. The proposed instrument combines a Hall-effect sensor and an accelerometer to capture the rise and fall of a buoy in response to wave movements. The Hall-effect sensor as a rotary encoder detects the rotation of the buoy while the accelerometer measures the tilt of the instrument caused by ocean waves. The instrument is constructed using PVC pipes and incorporates long-range (LoRa) communication technology for real-time monitoring. Experimental tests were conducted at a study site in Banyuwangi Regency, Indonesia, where the performance of the instrument was compared with an ultrasonic sensor-based instrument. The results validate the effectiveness of the proposed instrument design for wave height measurement. The collected data were transmitted via the LoRa communication system, enabling convenient monitoring and analysis of sea-level changes. The valuable contribution of the instrument to the field stems from its precision in measuring the wave height, adaptability to diverse conditions, and potential applicability in shallow waters. [ABSTRACT FROM AUTHOR]

Accelerometers have been widely used for motion analysis. The effect of initial sensor orientation (ISO) on the derived range of motion (ROM) is currently unexplored, limiting clarity in understanding error. This two-step study systematically explored the effect of ISO on the error of accelerometer-derived range of motion (ROM) and the effect of a proposed correction algorithm. Accelerometer data were used to compute peak and through-range ROM across a range of ISO and movement angular velocities up to 148° s−1 compared to an optoelectronic gold-standard. Step 1 demonstrated that error increased linearly with increasing ISO offsets and angular velocity. Average peak ROM RMSE at an ISO of 20° tilt and twist was 5.9° for sagittal motion, and for an ISO of 50° pitch and 20° twist, it was 7.5° for frontal plane ROM. Through-range RMSE demonstrated errors of 7–8° for similar ISOs. Predictive modeling estimated a 3.2° and 3.7° increase in peak and through-range sagittal plane error for every 10° increase in tilt and twist ISO. Step 2 demonstrated error reduction utilizing mathematical correction for ISO, resulting in <1° mean peak error and <1.2° mean through-range ROM error regardless of ISO. Accelerometers can be used to measure cardinal plane joint angles, but initial orientation is a source of error unless corrected. [ABSTRACT FROM AUTHOR]

Background: Balance parameters derived from wearable sensor measurements during postural sway have been shown to be sensitive to experimental variables such as test duration, sensor number, and sensor location that influence the magnitude and frequency-related properties of measured center-of-mass (COM) and center-of-pressure (COP) excursions. In this study, we investigated the effects of test duration, the number of sensors, and sensor location on the reliability of standing balance parameters derived using body-mounted accelerometers. Methods: Twelve volunteers without any prior history of balance disorders were enrolled in the study. They were asked to perform two 2-min quiet standing tests with two different testing conditions (eyes open and eyes closed). Five inertial measurement units (IMUs) were employed to capture postural sway data from each participant. IMUs were attached to the participants' right legs, the second sacral vertebra, sternum, and the left mastoid processes. Balance parameters of interest were calculated for the single head, sternum, and sacrum accelerometers, as well as, a three-sensor combination (leg, sacrum, and sternum). Accelerometer data were used to estimate COP-based and COM-based balance parameters during quiet standing. To examine the effect of test duration and sensor location, each 120-s recording from different sensor locations was segmented into 20-, 30-, 40-, 50-, 60-, 70-, 80-, 90-, 100-, and 110-s intervals. For each of these time intervals, time- and frequency-domain balance parameters were calculated for all sensor locations. Results: Most COM-based and COP-based balance parameters could be derived reliably for clinical applications (Intraclass-Correlation Coefficient, ICC ≥ 0.90) with a minimum test duration of 70 and 110 s, respectively. The exceptions were COP-based parameters obtained using a sacrum-mounted sensor, especially in the eyes-closed condition, which could not be reliably used for clinical applications even with a 120-s test duration. Conclusions: Most standing balance parameters can be reliably measured using a single head- or sternum-mounted sensor within a 120-s test duration. For other sensor locations, the minimum test duration may be longer and may depend on the specific test conditions. [ABSTRACT FROM AUTHOR]

Vibrational analysis plays a pivotal role in predictive maintenance and condition-monitoring, providing the capability to identify emerging issues before they escalate into equipment failures, unplanned downtime, or safety hazards. Attaining maximum accuracy in vibrational measurements is crucial for the efficacy of these analyses. In this study, our objective is to enhance the measurement accuracy of the piezoelectric accelerometer, a fundamental transducer in vibrational analysis. We propose a novel formula that establishes a connection between the measurement accuracy of the sensor and the displacements of vibrational movements. This improvement is designed to elevate the precision of vibration analysis, reducing errors in fault detection and optimizing the outcomes of condition-based preventative maintenance initiatives. The overarching goal is to enhance the reliability of vibration analysis techniques, ensuring a more robust and efficient approach to machine condition monitoring and maintenance planning. [ABSTRACT FROM AUTHOR]

Target detection on the surface of water using wireless sensor networks (WSNs) should be able to collect information as much as possible. The previous studies are only able to collect speed and direction of the target. In addition to previous studies, we collect not only speed and direction but also real time location of the target. In this paper, we propose a target localization algorithm based on Time Different of Arrival (TDoA). The proposed algorithm takes into account grid topology of sensors because it is more tractable. The algorithm is validated using experiments to compute the mean average error (MAE); in these experiments, the estimated and real location coordinates were compared. The experimental results achieved the smallest MAE at 0.25 m. The experimental results indicated that the MAE is inversely proportional to the distance between the sensors. Moreover, the MAE of the x-axis is always greater than that of the y-axis is well explained using the principle of the Abbe error. [ABSTRACT FROM AUTHOR]

In this paper, the mechanical characteristics of a miniature optomechanical accelerometer, similar to those proposed for a wide range of applications, have been investigated. With the help of numerical modelling, characteristics such as eigenfrequencies, quality factor, displacement magnitude, normalized translations, normalized rotations versus eigenfrequencies, as well as spatial distributions of the azimuthal and axial displacements and stored energy density in a wide frequency range starting from the stationary case have been obtained. Dependencies of the main mechanical characteristics versus the minimal and maximal system dimensions have been plotted. Geometries of the optomechanical accelerometers with micron size parts providing the low and the high first eigenfrequencies are presented. It is shown that via the choice of the geometrical parameters, the minimal measured acceleration level can be raised substantially. [ABSTRACT FROM AUTHOR]

Cavity optomechanical sensors can offer exceptional sensitivity; however, interrogating the cavity motion with high accuracy and dynamic range has proven to be challenging. Here, we employ a dual optical frequency comb spectrometer to readout a microfabricated cavity optomechanical accelerometer, allowing for rapid simultaneous measurements of the cavity's displacement, finesse, and coupling at accelerations up to 24 g (236 m/s2). With this approach, we have achieved a displacement sensitivity of 2 fm Hz−1/2, a measurement rate of 100 kHz, and a dynamic range of 7.6 × 105, which is the highest we are aware of for a microfabricated cavity optomechanical sensor. In addition, comparisons of our optomechanical sensor coupled directly to a commercial reference accelerometer show agreement at the 0.5% level, a value that is limited by the reference's reported uncertainty. Furthermore, the methods described herein are not limited to accelerometry but rather can be readily applied to nearly any optomechanical sensor where the combination of high speed, dynamic range, and sensitivity is expected to be enabling. [ABSTRACT FROM AUTHOR]

A MS6.8 earthquake occurred on 5th September 2022 in Luding county, Sichuan, China, at 12: 52 Beijing Time (4:52 UTC). We complied a dataset of PGA, PGV, and site vS30 of 73 accelerometers and 791 Micro-Electro-Mechanical System (MEMS) sensors within 300 km of the epicenter. The inferred vS30 of 820 recording sites were validated. The study results show that: (1) The maximum horizontal PGA and PGV reaches 634.1 Gal and 71.1 cm/s respectively. (2) Over 80% of records are from soil sites. (3) The vS30 proxy model of Zhou J et al. is superior than that of Wald and Allen and performs well in the study area. The dataset was compiled in a flat file that consists the information of strong-motion instruments, the strong-motion records, and the vS30 of the recording sites. [ABSTRACT FROM AUTHOR]

The growing diversity of animal‐borne sensor types is revolutionizing our understanding of wildlife biology. For example, researcher‐developed sensors, such as audio and video loggers, are being increasingly attached to wildlife tracking collars to provide insights into a range of topics from species interactions to physiology. However, such devices are often prohibitively power‐intensive, relative to conventional wildlife collar sensors, and their retrieval without compromising long‐term data collection and animal welfare remains a challenge. We present an open‐source system (SensorDrop) for remotely detaching individual sensors from wildlife collars. SensorDrop facilitates the retrieval of power‐intensive sensors while leaving non‐resource‐intensive sensors intact on animals. SensorDrop systems can be made using commercially available components and are a fraction of the cost of other timed drop‐off devices that detach full wildlife tracking collars. From 2021 to 2022, eight SensorDrop units were successfully deployed on free‐ranging African wild dog packs in the Okavango Delta as part of audio‐accelerometer sensor bundles attached to wildlife collars. All SensorDrop units detached after 2–3 weeks and facilitated the collection of audio and accelerometer data while leaving wildlife GPS collars intact to continue collecting locational data (>1 year), critical for long‐term conservation population monitoring in the region. SensorDrop offers a low‐cost method to remotely detach and retrieve individual sensors from wildlife collars. By selectively detaching battery‐depleted sensors, SensorDrop maximizes the amount of data collected per wildlife collar deployment and mitigates ethical concerns on animal rehandling. SensorDrop adds to the growing body of open‐source animal‐borne technologies being utilized by wildlife researchers to innovate and expand upon data collection practices and supports the continued ethical use of novel technologies within wildlife studies. [ABSTRACT FROM AUTHOR]

Postural instability is a risk factor for falls in older adults. It is possible to detect postural stability using an integrated accelerometer (ACC) sensor in a smartphone. Therefore, a novel ACC-based smartphone application running on the Android operating system called BalanceLab was created and tested. This study aimed to determine the validity and reliability of a novel ACC-based Android smartphone application for assessing balance in older adults. Using BalanceLab, 20 older adults completed three balance assessments: the Modified Clinical Test of Sensory Interaction in Balance (MCTSIB), a single-leg stance test (SLST), and a limit of stability test (LOS). The validity of this mobile application was investigated using a three-dimensional (3D) motion analysis system and the Fullerton Advanced Balance (FAB) scale. The test-retest reliability of this mobile application was determined on two separate occasions within one day, at least two hours apart. The two static balance assessments (the MCTSIB and SLST) demonstrated moderate to excellent correlation with the 3D motion analysis system (r = 0.70–0.91) and the FAB scale (r = 0.67–0.80). However, the majority of the dynamic balance tests (the LOS tests) did not demonstrate any correlation with the 3D motion analysis system or the FAB scale. This novel ACC-based application demonstrated good to excellent test-retest reliability (ICC = 0.76–0.91). A static, but not dynamic, balance assessment tool that uses a novel ACC-based application for Android can be used to measure balance in older adults. This application has moderate to excellent validity and test-retest reliability. • A new accelerometer-based app was examined for its reliability and validity. • A balance app demonstrated high test-retest reliability. • An app showed a high level of validity in static but not in dynamic balance tests. • An older adult's static balance can be evaluated with a new balance app. [ABSTRACT FROM AUTHOR]

In mechatronic-related applications, estimating orientation from a magnetic, angular rate, and gravity (MARG) sensor array is a significant topic. Representing attitude orientation is a well-known topic in the aerospace industry, where it plays a critical role in airplanes and unmanned aerial vehicles (UAVs), but it has also gained relevance in other sectors. However, most of the sensors utilized are quite expensive, heavy, and large, making them unsuitable for modest applications. This paper examines the performance of several sensors in low-cost hardware and high-acceleration environments. A theorical method was adopted to estimate Euler angles by using accelerometer, gyroscope and magnetometer, and a robust and easy to implement method calibration was proposed to calibrate the MARG sensor without any external equipment. An experimental verification of the proposed calibration method was completed. The experimental results are then interpreted to provide an insight to advantages and disadvantages for using each sensor separately. [ABSTRACT FROM AUTHOR]

High–sensitivity shaken lattice interferometry (SLI) based sensors have the potential to provide deep space missions with the ability to precisely measure non–gravitational perturbing forces. This work considers the simulation of the OSIRIS-REx mission navigation in the vicinity of Bennu with the addition of measurements from onboard SLI–based accelerometers. The simulation is performed in the Jet Propulsion Laboratory's (JPL) Mission Analysis, Operations and Navigation Toolkit (MONTE) and incorporates OSIRIS-REx reconstructed trajectory and attitude data from the Navigation and Ancillary Information Facility (NAIF) database. The use of the reconstructed data from NAIF provides realistic true dynamical errors and JPL's MONTE software allows for a high–fidelity simulation of an integrated trajectory for the filter. The navigation performance and reduction of tracking and complex modelling enabled by the onboard SLI–based sensor are presented for two orbital phases of the OSIRIS–REx mission. Overall, the results show that the addition of SLI–based accelerometer measurements improves navigation performance, when compared to a radiometric tracking only configuration. In addition, results demonstrate that highly–precise accelerometer measurements can effectively replace at least one day of DSN passes over a three–day period, thereby reducing tracking requirements. Furthermore, it is shown that lower–fidelity surface force modeling and parameter estimation is required when using onboard SLI–based accelerometers. [ABSTRACT FROM AUTHOR]

Copyright of Diversitas Journal is the property of Diversitas Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transportation Science & Technolgy is the property of Transportation Science & Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Running power is a popular measure to gauge objective intensity. It has recently been shown, though, that foot-worn sensors alone cannot reflect variations in the exerted energy that stems from changes in the running economy. In order to support long-term improvement in running, these changes need to be taken into account. We propose leveraging the presence of two additional sensors worn by the most ambitious recreational runners for improved measurement: a watch and a heart rate chest strap. Using these accelerometers, which are already present and distributed over the athlete's body, carries more information about metabolic demand than a single foot-worn sensor. In this work, we demonstrate the mutual information between acceleration data and the metabolic demand of running by leveraging the information bottleneck of a constrained convolutional neural network. We perform lab measurements on 29 ambitious recreational runners (age = 28 ± 7 years, weekly running distance = 50 ± 25 km, V ˙ O 2 max = 60.3 ± 7.4 mL · min−1·kg−1). We show that information about the metabolic demand of running is contained in kinetic data. Additionally, we prove that the combination of three sensors (foot, torso, and lower arm) carries significantly more information than a single foot-worn sensor. We advocate for the development of running power systems that incorporate the sensors in watches and chest straps to improve the validity of running power and, thereby, long-term training planning. [ABSTRACT FROM AUTHOR]

Changes in gait are a well-documented symptom for several conditions, such as degenerative diseases, stroke, foot conditions, neurological conditions (i.e., Parkinson's) [1]. Gait speed is thus a well-known indicator for assessing an individual's functional mobility [2]. Machine learning (ML) shows great potential in estimating gait speed due to its ability to reveal hidden patterns in large datasets and has already shown promising results over standard strap-down integration methods for foot-mounted inertial sensors (IMU) [3]. For the present investigation, we evaluated various supervised ML models to estimate gait speed based on IMU data collected on the most dominant lower limb. The Gait Events Data Set by Miraldo et al. [4] was used for the analysis. This dataset consists of metadata and inertial measurements (tri-axial accelerometer and gyroscope @6.75ms/sample) from sensors fixed to the flat part of the tibia on the dominant side, collected from 22 healthy subjects. Each subject walked barefoot six times at three randomly ordered, self-paced speeds (comfortable, slow, fast) on a 40-m long 2-m wide walkway, without curves, with a flat and rigid surface. The trials lasted 30-to-60 seconds. Overall, the dataset contains 9,661 gait strides. The IMU data for each trial was segmented into strides by the method proposed in [5]. Several features (Table 1) were extracted from each stride [6] and then averaged over the length of the trial to be used as input to several multiple linear regression and tree-based models along with participant anthropometrics. The number of trials were split 80%/20% into training and test sets with data from the same subject appearing only in either one or the other. A grid-search was performed on the training set using leave-one-subject-out cross-validation (LOSO-CV) to find the best performing model and mean absolute percentage error (MAPE) reported as the validation metric. The selected model was compared to a dummy regression model used as a baseline. Tables 2-3 show the results for the different models. Ridge regression showed the best results with MAE equal to 0.12 m/s and MAPE to 9.13%, almost three times lower compared to baseline. Fig. 1 shows the difference between ground truth and predicted speed across different subjects, the related Bland-Altman plot, and a sample of the obtained error for one trial. Results obtained are comparable to results in [5,6]. [Display omitted] This investigation shows that Ridge regression using features extracted from detected gait events in IMU data and participant anthropometrics is an appropriate approach to estimate gait speed, outperforming several other linear and tree-based regression models. The final model achieved promising results with a considerably lower error than the baseline, although further investigation is needed to confirm efficacy at atypical gait speeds, i.e., with populations with movement disorders. Funding: This work was supported in part by Enterprise Ireland (EI) and Abbvie, Inc. under agreement IP 2017 0625; and in part by the Science Foundation Ireland which are Co-Funded through the European Regional Development Fund under Grant 12/RC/2289-P2-INSIGHT. [ABSTRACT FROM AUTHOR]

The zero-velocity update (ZUPT) method is an effective technique, which has been widely used in low-cost foot-mounted inertial navigation systems. It can mitigate the accumulated error by using the fact that, when the foot is stationary, the velocity should be zero. However, it is hard to detect the zero-velocity phases (ZVPs) correctly in various motion modes. To deal with this problem, a novel adaptive zero velocity detection algorithm based on an improved general likelihood ratio test (IGLRT) detector is proposed. The prior knowledge of the proposed IGLRT detector is the minima output value of inertial sensors, which is determined by an improved peak-valley detection method, while the prior knowledge of the traditional general likelihood ratio test (GLRT) method is based on the hypothesis that, when the foot is stationary, the magnitude of angular rates is zero, and the magnitude of accelerometers is equal to the magnitude of gravitation. Also, an adaptive method is proposed to enhance the robustness of the IGLRT, where the thresholds of IGLRT can be adjusted adaptively according to the amplitude of accelerometers. Field tests show that the proposed algorithm performs better than conventional and state-of-the-art approaches in mixed motion modes. [ABSTRACT FROM AUTHOR]

A popular approach to track the position of a user is to use an inertial measurement unit (IMU), which allows tracking a user with a pedestrian dead reckoning (PDR) system by estimating the length and heading of each step a user takes. Furthermore, a user’s steps together with their length can be used to detect anomalies in the gait due to, e.g., a gait disorder. To determine the length and/or heading of a step, we first need an algorithm that estimates the boundaries of a step, i.e., the start and end, in the IMU signals. In this article, we propose a deep-learning-based step detection algorithm that detects the start and end of a step using only data from a handheld accelerometer. In this algorithm, also, the relationship between the start and end of a step is used to improve performance. To evaluate the performance of the detector, in contrast to most previous works that only look at the total number of detected steps, we determine if the start and end of each step are detected at the correct instant. This resulted in a step detector with an f-score of 99.2% and 99.0% for, respectively, detecting the start and end of a step. After detecting the start and end of the steps, we use a long short-term memory (LSTM)/convolutional neural network (CNN)-based step length estimator, which resulted in a mean absolute error (mae) on the step length of 3.21 cm. Finally, we also determine the combined performance of the step detector and step length estimator, i.e., where the proposed step detector is used to extract the accelerometer fragments of a step instead of assuming that the boundaries are known. This resulted in an mae of 6.56 cm using the deep-learning-based step length estimator. [ABSTRACT FROM AUTHOR]

Pitching biomechanical research is highly focused on injury prevention with little attention to how biomechanical data can facilitate skill development. The overall purpose of this study was to explore how sensor-derived segment kinematics and timing relate to command and ball velocity during baseball pitching. We used a cross-sectional design to analyze a series of pitches thrown from 10 collegiate baseball pitchers. We collected biomechanical data from six inertial sensors, subjective command from the pitchers, and ball velocity from a radar device. Stepwise regression analyses were used to explore biomechanical variables associated with command for all pitches and ball velocity for fastballs only. We found that only peak forearm linear acceleration was significantly associated with command, whereas several segment kinematic measures were significantly associated with ball velocity. Our results suggest that different biomechanical variables are linked to specific pithing skills. Our findings suggest that end-effector (forearm) movement is more important for pitch command, whereas proximal-to-distal (pelvis, trunk, upper arm, forearm) segmental movement is important for ball velocity. [ABSTRACT FROM AUTHOR]

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

A simplified gravitational reference sensor (S-GRS) is an ultra-precise inertial sensor for future Earth geodesy missions. These sensors measure or compensate for all non-gravitational accelerations of the host spacecraft to remove them in the data analysis and recover spacecraft motion due to Earth's gravity field. Low–low satellite-to-satellite tracking missions like GRACE-FO that use laser ranging interferometers (LRI) are limited by the acceleration noise performance of their electrostatic accelerometers and temporal aliasing associated with Earth's gravity field. The current accelerometers, used in the GRACE missions, have a limited sensitivity of ∼ 10 - 10 m/s 2 /Hz 1 / 2 around 1 mHz. The S-GRS is estimated to be at least 40 times more sensitive than the GRACE accelerometers and over 500 times more sensitive if operated on a drag-compensated platform. This improvement is enabled by increasing the mass of the sensor's test mass, increasing the gap between the test mass and its electrode housing, removing the grounding wire used in GRACE, and replacing it with a UV LED-based charge management system. This allows future missions to take advantage of the sensitivity of the GRACE-FO LRI in the gravity recovery analysis. The S-GRS concept is a simplified version of the flight-proven LISA Pathfinder (LPF) GRS. Performance estimates are based on models vetted during the LPF flight and the expected spacecraft environment based on GRACE-FO data. The relatively low volume ( ∼ 10 4 cm 3 ), mass (∼ 13 kg), and power (∼ 20 W) enable the use of S-GRS on microsatellites, reducing launch costs and allowing more satellite pairs to improve the temporal resolution of gravity field maps. The S-GRS design and analysis, as well as its gravity recovery performance in two candidate mission architectures, are discussed in this article. [ABSTRACT FROM AUTHOR]

Early drone anomaly inspection is vital to ensure the drone's safety and effectiveness. This process is often overlooked, especially by amateur drone pilots; however, some faulty conditions are difficult to notice by the naked eye or discover, even though the drone inspection process has been conducted; therefore, a real-time early drone inspection approach based on vibration data is proposed in this study. Firstly, the reliability of several microelectromechanical systems (MEMS) sensors, namely the ADXL335 accelerometer, ADXL 345 accelerometer, ADXL377 accelerometer, and SW420 vibration sensor in detecting faulty conditions, were tested and compared. The experimental results demonstrated that the vibration parameter measured using ADXL335 and ADXL345 accelerometers are the best choice as most of the faulty conditions can be detected, in contrast to other MEMS sensors. The output produced from the anomaly inspection algorithm is then converted to the "Healthy" or "Faulty" state, which is displayed in a mobile application for easy monitoring. [ABSTRACT FROM AUTHOR]

Cattle are less active than humans. Hence, it was hypothesized in this study that transmitting acceleration signals at a 1 min sampling interval to reduce storage load has the potential to improve the performance of motion sensors without affecting the precision of behavior classification. The behavior classification performance in terms of precision, sensitivity, and the F1-score of the 1 min serial datasets segmented in 3, 4, and 5 min window sizes based on nine algorithms were determined. The collar-fitted triaxial accelerometer sensor was attached on the right side of the neck of the two fattening Korean steers (age: 20 months) and the steers were observed for 6 h on day one, 10 h on day two, and 7 h on day three. The acceleration signals and visual observations were time synchronized and analyzed based on the objectives. The resting behavior was most correctly classified using the combination of a 4 min window size and the long short-term memory (LSTM) algorithm which resulted in 89% high precision, 81% high sensitivity, and 85% high F1-score. High classification performance (79% precision, 88% sensitivity, and 83% F1-score) was also obtained in classifying the eating behavior using the same classification method (4 min window size and an LSTM algorithm). The most poorly classified behavior was the active behavior. This study showed that the collar-fitted triaxial sensor measuring 1 min serial signals could be used as a tool for detecting the resting and eating behaviors of cattle in high precision by segmenting the acceleration signals in a 4 min window size and by using the LSTM classification algorithm. [ABSTRACT FROM AUTHOR]

Wireless sensor networks usually suffer from the issue of time synchronization discrepancy due to environmental effects or clock management collapse. This will result in time delays between the dynamic responses collected by wireless sensors. If non-synchronized dynamic response data are directly used for structural modal identification, it leads to the misestimation of modal parameters. To overcome the non-synchronization issue, this study proposes a time synchronization approach to detect and correct asynchronous dynamic responses based on frequency domain decomposition (FDD) with frequency-squeezing processing (FSP). By imposing the expected relationship between modal phase angles extracted from the first-order singular value spectrum, the time lags between different sensors can be estimated, and synchronization can be achieved. The effectiveness of the proposed approach is fully demonstrated by numerical and experimental studies, as well as field measurement of a large-span spatial structure. The results verify that the proposed approach is effective for the time synchronization of wireless accelerometer sensors. [ABSTRACT FROM AUTHOR]

Like gravitational wave detection, inter-spacecraft geodesy is a measurement of gravitational tidal accelerations deforming a constellation of two or more orbiting reference test masses (TM). The LISA TM system requires TM in free fall with residual stray accelerations approaching the fm/s 2 /Hz 1 / 2 level in the mHz band, as demonstrated in the LISA Pathfinder "Einstein's geodesic explorer" mission. Current geodesy missions are limited by accelerometers with 100 pm/s 2 /Hz 1 / 2 level, due to intrinsic design limitations, as well as the challenging low Earth orbit environment and operating conditions. A reduction in the TM acceleration noise could lead to an important improvement in the scientific return of future geodesy missions focusing on mass change, especially in a scenario with multiple pairs of geodesy satellites. We present here a preliminary assessment of how the LISA TM system, known as the "gravitational reference sensor" (GRS), could be adapted for use in future geodesy missions aiming at residual TM accelerations noise at the pm/s 2 /Hz 1 / 2 level, addressing the major design issues and performance limitations. We find that such a performance is possible in a geodesy GRS that is simpler and smaller than that used for LISA, with a lighter, sub-kg TM and gaps reduced from 4 mm to less than 1 mm. Acceleration noise performance limitations will likely be closely tied to the required levels of applied actuation forces on the TM. [ABSTRACT FROM AUTHOR]

Vertical Gyro is a gyroscopic device for attitude (pitch, roll angles) determination. This article deals with the influence of the object oscillations on the Vertical Gyro error and its reduction. The reason of the Vertical Gyro error at oscillations is acceleration appeared at the place of the Vertical Gyro installation on the object. This acceleration disturbs gyro correction by accelerometers as level sensors. There are following popular types of the Vertical gyro correction: proportional, proportional-integral, constant, mixed, and strapdown Vertical Gyros use also the Kalman filter and its various modifications. The choice of one or another type depends on tasks to be solved and possibility to use complex correction types. This article investigates the Vertical Gyro with constant correction which has a number of advantages specifically when working at object acceleration. A technique for reducing the Vertical Gyro error is proposed which uses automatic switch off the correction by analysis of the level sensor signal. There is proposed a band-pass filter for this signal, technique for choice of parameters of the proposed switching-off algorithm that provides minimum of the Vertical Gyro error at oscillations. Example of calculation of these parameters is presented. To check correctness of the calculations and to confirm the efficiency of the technique, data obtained during bench tests of the Inertial Labs MRU-B2 (Vertical Gyro built on micromechanical sensors) were used. A semi-physical simulation of the Vertical Gyro operation with the proposed method of reducing the error during oscillation was performed. This simulation results confirmed the effectiveness of the proposed method and the correctness of the analytical calculations. Due to the proposed switching off the correction during the oscillation, it was possible to reduce the initial error by approximately 80%. In further work, it is planned to investigate the operation of the Vertical Gyro with proportional-integral correction (which is also widely used) at oscillations, with correction automatic switch off. [ABSTRACT FROM AUTHOR]

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

Copyright of Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Because falls are the second leading cause of injury deaths, especially in the elderly according to WHO statistics, there have been a lot of studies on developing a fall detection and warning system. Many approaches based on wearable sensors, cameras, Infrared sensors, radar, etc., have been proposed to detect falls efficiently. However, it still faces many challenges due to noise and no clear definition of fall activities. This paper proposes a new way to extract 44 features based on the time domain, frequency domain, and Hjorth parameters to deal with this. The effect of the proposed feature set has been evaluated on several classification algorithms, such as SVM, k-NN, ANN, J48, and RF. Our method achieves a relative high performance (F1-Score metric) in detecting fall and non-fall activities, i.e., 95.23% (falls), 99.11% (non-falls), and 96.16% (falls), 99.90% (non-falls) for the MobileAct 2.0 and UP-Fall datasets, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a power efficient, low delay and rate adaptive dual chamber pacemaker (PLRDPM) using heart rate and accelerometer sensor. In recent years, number of modifications have been done in the pacemaker design. However, design of an implantable device on an open source is still challenging. Through this paper, we are proposing a "proof of concept" for the design of PLRDPM on FPGA for improving the vital parameters: delay and power consumption. The proposed PLRDPM comprises of accelerometer and heart rate sensors to measure physical activity's effect on heart rate of the bradycardia patients. A rate adaptive pacing algorithm has been designed using two sensor's data, to reduce the delay and power consumption. However, delay in the responses of various components in the circuitry produces an accumulative delay effect in any practical circuit. The delay and the power consumption for the proposed PLRDPM are found to be 2.82 ns and 9 mW, respectively. [ABSTRACT FROM AUTHOR]

This study proposes the use of a high-speed camera as a holographic visual sensor to obtain the dense full-field dynamic parameters of the main beam of a bridge by the field of view through uniaxial rotation photography. Based on the basic principle that the frequency and mode of a structure are inherent characteristics, the mode coordinates obtained from each field of view are unified, normalized, and matched according to the same name pixels to obtain the dense full- field dynamic parameters of the entire bridge. The frequency and first three order modes of a self-anchored suspension test bridge are collected by the method proposed in this study. The frequency comparison between the accelerometers and dial gauges is within 3%, and the mode shapes are more holographic and more realistic than those obtained by limited measuring points. In addition, the difference in the curvature mode under various damage conditions obtained by limited measurement points is compared with that obtained by the method proposed in this study. Results shows that the dense full-field modal curvature difference can reflect the change in the damage location even in a low order, which means the sensitivity of the change of damage location in low-order modal. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a regularization of long short-term memory (LSTM) based fall detection system using TensorFlow that can detect falls that can occur in the elderly. Fall detection uses data from a 3-axis acceleration sensor attached to the body of an elderly person and learns about a total of 7 behavior patterns, each of which is a pattern that occurs in daily life, and the remaining 3 are patterns for falls. During training, a normalization process is performed to effectively reduce the loss function, and the normalization performs a maximum-minimum normalization for data and a L2 regularization for the loss function. The optimal regularization conditions of LSTM using several falling parameters obtained from the 3-axis accelerometer is explained. When normalization and regularization rate λ for sum vector magnitude (SVM) are 127 and 0.00015, respectively, the best sensitivity, specificity, and accuracy are 98.4, 94.8, and 96.9%, respectively. [ABSTRACT FROM AUTHOR]

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

The use of inertial sensors in fast bowling analysis may offer a cheaper and portable alternative to current methodologies. However, no previous studies have assessed the validity and reliability of such methods. Therefore, this study aimed to assess the validity and reliability of collecting tibial accelerations and spinal kinematics using inertial sensors during in vivo fast bowling. Thirty-five elite male fast bowlers volunteered for this study. An accelerometer attached to the skin over the tibia was used to determine impacts and inertial sensors over the S1, L1 and T1 spinous processes used to derive the relative kinematics. These measurements were compared to optoelectronic and force plate data for validity analysis. Most acceleration and kinematics variables measured report significant correlations > 0.8 with the corresponding gold standard measurement, with intraclass correlation coefficients greater than 0.7. Low standard error of measurement and consequently small minimum detectable change (MDC) values were also observed. This study demonstrates that inertial sensors are as valid and reliable as current methods of fast bowling analysis and may provide some advantages over traditional methods. The novel metrics and methods described in this study may aid coaches and practitioners in the design and monitoring of fast bowling technique. Graphical abstract illustrating the synopsis of the findings from this paper. [ABSTRACT FROM AUTHOR]

Wearable sensor systems are a emerging tools for the evaluation of the sport's activity and can be used to quantify the external workload of the athlete. The main goal of this paper was to evaluate the validity and reliability of the "Armbeep inertial measurement unit" (IMU) sensor both in a closed tennis exercise and in open matchplay. Twentyfour junior tennis players performed a baseline drill and played matches, during which they wore a combined accelerometer and gyroscope sensor. Video footage was concomitantly recorded using a digital video camera. The agreement between the measurements was assessed with the intraclass correlation coefficient (ICC) and the standard error of measurement (SEM). A simple linear regression was used to predict the number of shots registered from the video and from the Armbeep IMU sensor's data. The number of total forehand and backhand shots during the drill repetitions showed an excellent test and re-test reproducibility (ICC≥0.90). There was a significant relationship between the Armbeep IMU sensor's number of contacts and the total number of shots (R2 = 0.938) which indicated the excellent reliability of the tested Armbeep IMU sensor for those parameters. Considering the accuracy of the total tennis shots and the small magnitude of error for wrist speed and acceleration, the Armbeep IMU sensor appears to be an appropriate on-court tool that can be used to monitor the hitting load during tennis practice and matches. [ABSTRACT FROM AUTHOR]

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

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

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