Acceleration Measurements Research Articles

Quaternion attitude and disturbance observer mass estimation with low-cost sensors for industrial vehicles with trailers

Information about the vehicle mass and ground slope is important for many assistance functions in road and logistics vehicles. In intralogistics, safe speed limits for tractors depend on both slope and trailer mass. This work presents an estimation procedure for the attitude of a vehicle and the mass of a trailer attached to it. The estimator works in two steps. First, the attitude is estimated using an extended Kalman filter based on acceleration and angular rate measurements complemented by a single track vehicle model. In the second step, this information is used together with velocity and motor torque data to estimate the trailer mass and friction coefficient. A Kalman filter based disturbance observer for parameter estimation is compared to a recursive least squares identification. The mass estimation is extended by a structural break test to detect sudden mass changes when hitching and unhitching trailers. Multiple variants of the estimation scheme are implemented on an intralogistics vehicle. The performance of the proposed attitude and mass estimation solutions is demonstrated in comparison to state of the art reference algorithms in a large number of experiments. Compared to state of the art estimators, the proposed estimator yields a median 54 % reduction of pitch estimation RMSE and 40 % reduction of the mass estimation error. The structural break detection is able to detect all instances of hitching and unhitching of trailers with few false positives.

Performance of multiple neural networks in predicting lower limb joint moments using wearable sensors.

Joint moment measurements represent an objective biomechemical parameter in joint health assessment. Inverse dynamics based on 3D motion capture data is the current 'gold standard' to estimate joint moments. Recently, machine learning combined with data measured by wearable technologies such electromyography (EMG), inertial measurement units (IMU), and electrogoniometers (GON) has been used to enable fast, easy, and low-cost measurements of joint moments. This study investigates the ability of various deep neural networks to predict lower limb joint moments merely from IMU sensors. The performance of five different deep neural networks (InceptionTimePlus, eXplainable convolutional neural network (XCM), XCMplus, Recurrent neural network (RNNplus), and Time Series Transformer (TSTPlus)) were tested to predict hip, knee, ankle, and subtalar moments using acceleration and gyroscope measurements of four IMU sensors at the trunk, thigh, shank, and foot. Multiple locomotion modes were considered including level-ground walking, treadmill walking, stair ascent, stair descent, ramp ascent, and ramp descent. We show that XCM can accurately predict lower limb joint moments using data of only four IMUs with RMSE of 0.046 ± 0.013 Nm/kg compared to 0.064 ± 0.003 Nm/kg on average for the other architectures. We found that hip, knee, and ankle joint moments predictions had a comparable RMSE with an average of 0.069 Nm/kg, while subtalar joint moments had the lowest RMSE of 0.033 Nm/kg. The real-time feedback that can be derived from the proposed method can be highly valuable for sports scientists and physiotherapists to gain insights into biomechanics, technique, and form to develop personalized training and rehabilitation programs.

Associations between medium- and long-term exposure to air temperature and epigenetic age acceleration

Climate change poses a serious threat to human health worldwide, while aging populations increase. However, no study has ever investigated the effects of air temperature on epigenetic age acceleration. This study involved 1,725 and 1,877 participants from the population-based KORA F4 (2006–2008) and follow-up FF4 (2013–2014) studies, respectively, conducted in Augsburg, Germany. The difference between epigenetic age and chronological age was referred to as epigenetic age acceleration and reflected by Horvath’s epigenetic age acceleration (HorvathAA), Hannum’s epigenetic age acceleration (HannumAA), PhenoAge acceleration (PhenoAA), GrimAge acceleration (GrimAA), and Epigenetic Skin and Blood Age acceleration (SkinBloodAA). Daily air temperature was estimated using hybrid spatiotemporal regression-based models. To explore the medium- and long-term effects of air temperature modeled in time and space on epigenetic age acceleration, we applied generalized estimating equations (GEE) with distributed lag non-linear models, and GEE, respectively. We found that high temperature exposure based on the 8-week moving average air temperature (97.5th percentile of temperature compared to median temperature) was associated with increased HorvathAA, HannumAA, GrimAA, and SkinBloodAA: 1.83 (95% CI: 0.29–3.37), 11.71 (95% CI: 8.91–14.50), 2.26 (95% CI: 1.03–3.50), and 5.02 (95% CI: 3.42–6.63) years, respectively. Additionally, we found consistent results with high temperature exposure based on the 4-week moving average air temperature was associated with increased HannumAA, GrimAA, and SkinBloodAA: 9.18 (95% CI: 6.60–11.76), 1.78 (95% CI: 0.66–2.90), and 4.07 (95% CI: 2.56–5.57) years, respectively. For the spatial variation in annual average temperature, a 1 °C increase was associated with an increase in all five measures of epigenetic age acceleration (HorvathAA: 0.41 [95% CI: 0.24–0.57], HannumAA: 2.24 [95% CI: 1.95–2.53], PhenoAA: 0.32 [95% CI: 0.05–0.60], GrimAA: 0.24 [95%: 0.11–0.37], and SkinBloodAA: 1.17 [95% CI: 1.00–1.35] years). In conclusion, our results provide first evidence that medium- and long-term exposures to high air temperature affect increases in epigenetic age acceleration.

Identification of varying modal parameters of a tall building from the full-scale wind-induced responses

The dynamic behaviour of a tall building can change notably during the construction phases, under the varying degree of occupation, loading patterns, wind conditions, and aeroelastic phenomena. In this study, acceleration measurements taken over a period of one year and the corresponding wind conditions for a 150 m tall building in London, UK, are analysed to identify the reasons for variations in the structural modal parameters. Several environmental parameters are investigated, including the magnitude and the relative directions of the wind with respect to the building motion. The identified dominant effects are the response amplitude and the observation time. A finite element model of the tower is employed to support the experimental observations. The amplitude-dependent behaviour is studied for the natural frequencies and damping ratios of the first three vibration modes. The changes in the identified natural frequencies, with the monitoring time and response amplitudes, are attributed to the mass increase under the increasing occupancy and large-amplitude structural softening, respectively. In particular, it is shown that the natural frequencies experience an asymptotic drop of up to 5% with increasing time. The identified trends between the measured total damping and wind speed for different relative wind directions indicate that the amplitude of the lateral building motion, rather than aerodynamic wind characteristics, primarily influence the observed variation in the modal damping. Based only on the operational observations, an updated empirical model is proposed and compared against previous results from the literature. The established empirical model indicates the increase of the damping ratio values from around 0.6 % for the near-zero amplitude conditions to up to 2.1 % for the high amplitude conditions.

High-precision computational modeling of soil-rock mixtures considering realistic shape of rock blocks

Soil-rock mixtures (SRMs) are heterogeneous geological bodies with complex spatial structures and peculiar mechanical properties. This paper proposes a high-precision computational modeling method to realistically simulate the shape of rock blocks in the SRMs as well as the mesostructure of SRMs. The precise control theory of the rock blocks is first described. Then, acceleration measures such as bounding volume hierarchies and position culling are introduced to increase the computational efficiency while retaining the model accuracy. Practical examples and comparisons with the existing methods are provided to demonstrate the advantageous features of this method. In the end, the modeling method is successfully integrated into the DEM platform to create engineering models involved in SRMs, which lays the foundation for further DEM analysis to understand the effects of the mesostructure on the mechanical properties of SRMs.

Characterization of Mechanical and Cellular Effects of Rhythmic Vertical Vibrations on Adherent Cell Cultures.

This paper presents an innovative experimental setup that employs the principles of audio technology to subject adherent cells to rhythmic vertical vibrations. We employ a novel approach that combines three-axis acceleration measurements and particle tracking velocimetry to evaluate the setup's performance. This allows us to estimate crucial parameters such as root mean square acceleration, fluid flow patterns, and shear stress generated within the cell culture wells when subjected to various vibration types. The experimental conditions consisted of four vibrational modes: No Vibration, Continuous Vibration, Regular Pulse, and Variable Pulse. To evaluate the effects on cells, we utilized fluorescence microscopy and a customized feature extraction algorithm to analyze the F-actin filament structures. Our findings indicate a consistent trend across all vibrated cell cultures, revealing a reduction in size and altered orientation (2D angle) of the filaments. Furthermore, we observed cell accumulations in the G1 cell cycle phase in cells treated with Continuous Vibration and Regular Pulse. Our results demonstrate a negative correlation between the magnitude of mechanical stimuli and the size of F-actin filaments, as well as a positive correlation with the accumulations of cells in the G1 phase of the cell cycle. By unraveling these analyses, this study paves the way for future investigations and provides a compelling framework for comprehending the intricate cellular responses to rhythmic mechanical stimulation.

A Bayesian Approach to the Estimation of Road Profile and Bridge Damage from a Fleet Passing Vehicle Measurements

This paper presents a new way to determine road profile and detect bridge damage using accelerations from a fleet of passing vehicles. Using off-bridge data, a Bayesian approach updates estimates of the road profile and vehicle properties. The profile elevations and vehicle properties are shown to be insensitive to random noise in acceleration measurements. On-bridge data, with recently updated vehicle properties, are used to estimate bridge damage. Bearing damage and local crack damage in a bridge are simulated. For bearing damage, the results show that this method can quantify the damage level of a bearing and infer other bridge properties. For local crack damage, the levels and the location of the damage are inferred from the simulated measurements.

Three-dimensional structural displacement estimation by fusing monocular camera and accelerometer using adaptive multi-rate Kalman filter

Structural displacements play an important role in the health monitoring of civil structures; however, the accurate measurement of structural displacements remains a difficult task. Previous efforts have combined a monocular camera and an accelerometer to estimate structural displacement, but only in-plane displacements could be estimated in this way. In this study, the fusion of a monocular camera and an accelerometer was further extended for out-of-plane or three-dimensional displacement estimation. A computer vision algorithm and an adaptive multi-rate Kalman filter were integrated to efficiently estimate high-sampled displacements from low-sampled vision images and high-sampled acceleration measurements. All parameters associated with the computer vision algorithm were automatically calibrated without using any user-defined thresholds. Experimental validation was performed on two building structures and a 10-m-long bridge structure, and the proposed method accurately estimated the displacement for all three structures with a root mean square error of less than 1 mm.

Challenges and advances in the use of wearable sensors for lower extremity biomechanics

The use of wearable sensors for the collection of lower extremity biomechanical data is increasing in popularity, in part due to the ease of collecting data and the ability to capture movement outside of traditional biomechanics laboratories. Consequently, an increasing number of researchers are facing the challenges that come with utilizing the data captured by wearable sensors. These challenges include identifying/calculating meaningful measures from unfamiliar data types (measures of acceleration and angular velocity instead of positions and joint angles), defining sensor-to-segment alignments for calculating traditional biomechanics metrics, using reduced sensor sets and machine learning to predict unmeasured signals, making decisions about when and how to make algorithms freely available, and developing or replicating methods to perform basic processing tasks such as recognizing activities of interest or identifying gait events. In this perspective article, we present our own approaches to common challenges in lower extremity biomechanics research using wearable sensors and share our perspectives on approaching several of these challenges. We present these perspectives with examples that come mostly from gait research, but many of the concepts also apply to other contexts where researchers may use wearable sensors. Our goal is to introduce common challenges to new users of wearable sensors, and to promote dialogue amongst experienced users towards best practices.

Tissue Acceleration as a Novel Metric for Surgical Performance During Carotid Endarterectomy.

Gentle tissue handling to avoid excessive motion of affected fragile vessels during surgical dissection is essential for both surgeon proficiency and patient safety during carotid endarterectomy (CEA). However, a void remains in the quantification of these aspects during surgery. The video-based measurement of tissue acceleration is presented as a novel metric for the objective assessment of surgical performance. This study aimed to evaluate whether such metrics correlate with both surgeons' skill proficiency and adverse events during CEA. In a retrospective study including 117 patients who underwent CEA, acceleration of the carotid artery was measured during exposure through a video-based analysis. Tissue acceleration values and threshold violation error frequencies were analyzed and compared among the surgeon groups with different surgical experience (3 groups: novice , intermediate , and expert ). Multiple patient-related variables, surgeon groups, and video-based surgical performance parameters were compared between the patients with and without adverse events during CEA. Eleven patients (9.4%) experienced adverse events after CEA, and the rate of adverse events significantly correlated with the surgeon group. The mean maximum tissue acceleration and number of errors during surgical tasks significantly decreased from novice, to intermediate, to expert surgeons, and stepwise discriminant analysis showed that the combined use of surgical performance factors could accurately discriminate between surgeon groups. The multivariate logistic regression analysis revealed that the number of errors and vulnerable carotid plaques were associated with adverse events. Tissue acceleration profiles can be a novel metric for the objective assessment of surgical performance and the prediction of adverse events during surgery. Thus, this concept can be introduced into futuristic computer-aided surgeries for both surgical education and patient safety.

CREATION OF A UNIVERSAL MEASUREMENT COMPLEX FOR ROAD TESTS OF WHEELED VEHICLES

The results of studies related to the development of a universal measuring complex for road testing of wheeled vehicles (hereinafter referred to as the complex) are presented. The substantiation of the list of parameters measured by the complex and their metrological characteristics has been carried out, and the general requirements for the complex have been formulated; the basic principles of building special software included in the complex are given. The issues of calibration of the measuring systems of the complex are con-sidered in detail and the methods used for this, including features of the calibration method of the deceleration/ acceleration measurement system, are described. The description and technical characteristics of the developed complex are presented, as well as the results of its calibration and trial operation. On the basis of experimental studies, the expediency of using a two-stage method of calibrating path, speed and decelera-tion/acceleration measurement systems has been confirmed. As an example, the results of testing a vehicle for compliance with the requirements of UN Regulation No. 13, obtained using the complex, are shown, and the main software windows during these tests are described in detail.

Adaptive remaining useful life prediction framework with stochastic failure threshold for experimental bearings with different lifetimes under contaminated condition

Deterioration modelling and remaining useful life (RUL) prediction of roller bearings is critical to ensure a safe, reliable, and efficient operation of rotating machinery. RUL prediction models in model-based approaches are often based on constant failure threshold and time-domain features for bearings’ failure prognosis. Due to nonlinearity of the acceleration signals, noises, and measurement errors, the time-domain features used as condition indicators are unable to track bearings’ degradation successfully and they are mostly utilized for fault diagnosis, especially in the fault classification field using machine learning algorithms. This paper proposes an adaptive RUL prediction framework with a stochastic failure threshold which comprises of two main phases of feature extraction and RUL prediction using laboratory-acquired accelerated life test data obtained from contaminated bearings. The first phase is to decompose the empirical input signals into different frequency bands using some time–frequency transformation functions and extract several condition indicators for the second phase. The second phase is based on a stochastic Wiener process while the key parameters of the model are updated iteratively using a Bayesian approach, and RUL at different degradation datapoints is computed numerically. The experimental results showed the good performance of the developed framework. Some factors affecting RUL prediction such as the length of bearing samples, and degradation mechanism are highlighted in the result. The results of this paper can be further used for an effective maintenance optimization, determining an optimal maintenance alarm threshold, improving the reliability and safety of rotating machinery, and reducing the downtime cost.

Dynamic response analysis of a model truss bridge considering damage scenarios

Structural health monitoring plays a key role for maintenance and lifetime extension of bridges under complex loading conditions and damage scenarios. This paper applies vibration-based methods to monitor the structural health of a Warren-type truss bridge. First, acceleration measurements are used to obtain natural frequencies and modal shapes of the bridge by modal identification. Then, a high-fidelity numerical model in a finite element code is calibrated. Damage scenarios with various crack location and severity are considered for a member, and time-domain simulations are carried out to assess the dynamic responses of the bridge under random excitation. An analysis of the acceleration statistics shows that the damaged member displays a more significant increase in accelerations than the intact members, and locations near the supports of the damaged member experience more changes than other locations. This study contributes to a fundamental understanding of bridge dynamics and facilitates development of damage identification methods.

DESCRIPTIVE STATISTICAL CALIBRATION METHOD OF TRIAXIAL DIGITAL ACCELEROMETER ADXL345 AS EARTHQUAKES SENSOR

Seismic monitoring networks are the crucial elements in strong motion seismology for effective risk reduction. Low scale lateral variation of high intensity ground movement caused by earthquakes will be detected more effectively with densely located networks. However, the limitations of developing such project are rooted in expensive costs associated with the construction and installation in addition to bulky size of the conventional seismic observation system. Recently, micro-electromechanical system (MEMS) has being recognized in the applications of seismological and earthquake engineering due to the high precision obtained in these micron size semiconductor instruments and cheaper alternative for traditional seismic detector. ADXL345 is a type of digital triaxial MEMS accelerometer that is ideal for measurement of low-frequency vibrations and static accelerations of gravity, which makes it suitable for ground motion detection. Thus, this study aims at calibrating ADXL345 sensor that is required as sensing component in an affordable earthquake monitoring system with the Earthquake Benchmarking System (Penanda Aras Gempa Bumi, PAG) available in the inventory of Department of Mineral and Geoscience Malaysia, Sabah. Soil vibrations in EW (east-west or x-axis), NS (north-south or y-axis), and UD (up-down or z-axis) directions during random forces hit on the surface are recorded by both accelerometers. Acceleration magnitudes recorded by PAG and ADXL345 are extracted and data exploration is performed. Predominantly, ADXL345 measurements in horizontal and vertical ground movements are on a higher scale than the reference device. Subsequently, evaluation by using descriptive statistical analysis is chosen to produce numerical equations for data correction operations. İmplementation of the mathematical functions in ADXL345 for observing land movements in EW, NS, and UD directions resulted in decreasing the range values of output readings. Higher approximation of magnitudes of ground motion with the PAG system is achieved.

The heat is on: impacts of rising temperature on the activity of a common European mammal

Climate conditions severely impact the activity and, consequently, the fitness of wildlife species across the globe. Wildlife can respond to new climatic conditions, but the pace of human-induced change limits opportunities for adaptation or migration. Thus, how these changes affect behavior, movement patterns, and activity levels remains unclear. In this study, we investigate how extreme weather conditions affect the activity of European hares (Lepus europaeus) during their peak reproduction period. When hares must additionally invest energy in mating, prevailing against competitors, or lactating, we investigated their sensitivities to rising temperatures, wind speed, and humidity. To quantify their activity, we used the overall dynamic body acceleration (ODBA) calculated from tri-axial acceleration measurements of 33 GPS-collared hares. Our analysis revealed that temperature, humidity, and wind speed are important in explaining changes in activity, with a strong response for high temperatures above 25°C and the highest change in activity during temperature extremes of over 35°C during their inactive period. Further, we found a non-linear relationship between temperature and activity and an interaction of activity changes between day and night. Activity increased at higher temperatures during the inactive period (day) and decreased during the active period (night). This decrease was strongest during hot tropical nights. At a stage of life when mammals such as hares must substantially invest in reproduction, the sensitivity of females to extreme temperatures was particularly pronounced. Similarly, both sexes increased their activity at high humidity levels during the day and low wind speeds, irrespective of the time of day, while the effect of humidity was stronger for males. Our findings highlight the importance of understanding the complex relationships between extreme weather conditions and mammal behavior, critical for conservation and management. With ongoing climate change, extreme weather events such as heat waves and heavy rainfall are predicted to occur more often and last longer. These events will directly impact the fitness of hares and other wildlife species and hence the population dynamics of already declining populations across Europe.

Temperature Bias Drift Phase-Based Compensation for a MEMS Accelerometer with Stiffness-Tuning Double-Sided Parallel Plate Capacitors

This paper reports an approach of in-operation temperature bias drift compensation based on phase-based calibration for a stiffness-tunable MEMS accelerometer with double-sided parallel plate (DSPP) capacitors. The temperature drifts of the components of the accelerometer are characterized, and analytical models are built on the basis of the measured drift results. Results reveal that the temperature drift of the acceleration output bias is dominated by the sensitive mechanical stiffness. An out-of-bandwidth AC stimulus signal is introduced to excite the accelerometer, and the interference with the acceleration measurement is minimized. The demodulated phase of the excited response exhibits a monotonic relationship with the effective stiffness of the accelerometer. Through the proposed online compensation approach, the temperature drift of the effective stiffness can be detected by the demodulated phase and compensated in real time by adjusting the stiffness-tuning voltage of DSPP capacitors. The temperature drift coefficient (TDC) of the accelerometer is reduced from 0.54 to 0.29 mg/°C, and the Allan variance bias instability of about 2.8 μg is not adversely affected. Meanwhile, the pull-in resulting from the temperature drift of the effective stiffness can be prevented. TDC can be further reduced to 0.04 mg/°C through an additional offline calibration based on the demodulated carrier phase representing the temperature drift of the readout circuit.

Seismic-induced permanent displacement estimation combining acceleration and computer vision measurements

In this study, a seismic-induced permanent displacement estimation technique that combines acceleration and computer vision measurements was proposed. The proposed technique involves installing a vision camera and an accelerometer on a target building, with the vision camera capturing the change in the position of surrounding objects after a seismic event to estimate the permanent displacement of the target building. During the installation, the vision camera and accelerometer were vibrated in a controlled manner using a shaker on the target building. Then, in the absence of any artificial target or prior knowledge of the size of natural targets, an automatic scale factor map was generated, which converts a translation of an image (in pixels) into a physical displacement. After an earthquake, a displacement map was constructed to estimate the relative movement of the target building to the natural targets and then clustered using Gaussian mixture modeling. Finally, the undeformed portions of the natural targets were retrieved to estimate the permanent displacement of the target building with respect to the distribution of the natural target movement. Both indoor and outdoor experiments were conducted to validate the performance of the proposed technique. The results demonstrate the improved performance of the proposed technique compared with the previous technique.

BMAR

A requirement of cross-modal signal processing is accurate signal alignment. Though simple on a single device, accurate signal synchronization becomes challenging as soon as multiple devices are involved, such as during activity monitoring, health tracking, or motion capture---particularly outside controlled scenarios where data collection must be standalone, low-power, and support long runtimes. In this paper, we present BMAR, a novel synchronization method that operates purely based on recorded signals and is thus suitable for offline processing. BMAR needs no wireless communication between devices during runtime and does not require any specific user input, action, or behavior. BMAR operates on the data from devices worn by the same person that record barometric pressure and acceleration---inexpensive, low-power, and thus commonly included sensors in today's wearable devices. In its first stage, BMAR verifies that two recordings were acquired simultaneously and pre-aligns all data traces. In a second stage, BMAR refines the alignment using acceleration measurements while accounting for clock skew between devices. In our evaluation, three to five body-worn devices recorded signals from the wearer for up to ten hours during a series of activities. BMAR synchronized all signal recordings with a median error of 33.4 ms and reliably rejected non-overlapping signal traces. The worst-case activity was sleeping, where BMAR's second stage could not exploit motion for refinement and, thus, aligned traces with a median error of 3.06 s.

Leveraging machine learning to predict rail corrugation level from axle-box acceleration measurements on commercial vehicles

ABSTRACT Rail corrugation is a prominent degradative problem in the health monitoring of railway systems. Monitoring process is dependent on use of a diagnostic trolley, which is expensive and needs the track to be out-of-service. Alternatively, in-service rail vehicles with Axle-Box Acceleration measurement systems installed, have shown success in detecting rail corrugation levels based on physical models, albeit with limitations. Extending this approach, we build a Machine Learning model, represented by a tuned Random Forest regressor, trained on collected accelerometer signals along with other offline and/or static features. We also propose a method to engineer acceleration-based features which nullifies the aggregated acceleration vibrations inherited from the other rail due to dynamically coupled vibrations between the left and right rails. The resulting model is able to recreate the moving RMS irregularity profile at bandwidth 100–300 mm, especially in highly corrugated sections, with an R 2 score of 0.97–0.98. The results show that the suggested data-driven approach outperforms a state-of-the-art model-based benchmark.

Numerical simulation of subsonic and transonic water entry with compressibility effect considered

The purpose of this study is to analyze the subsonic and transonic water entry of the axisymmetric body with compressibility effect considered. The water-entry processes of axisymmetric body are numerically investigated, with Tait state equation and energy equation used to describe the compressibility of the liquid. Reasonable agreement has been obtained between the numerical results and the experimental measurements of trajectory, velocity and acceleration. The cavity evolution and dynamic characteristics of subsonic cases are investigated for vertical and oblique water-entry. The results show that the maximum drag coefficient of the oblique water entry is lower, and it is affected by a large moment which causes pitch angle and trajectory inclination angle to change significantly. In the oblique transonic conditions, the effect of compressibility of the liquid on the dynamic flow characteristics is analyzed. On this basis, the influence of entry angle on the hydrodynamic characteristics is investigated. It is concluded that the water-entry angle has a more significant influence on the trajectory characteristics.