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

• Cantilever, diaphragm, hinge and other structures geophones and accelerometers are reviewed. • Sensing mechanism, resonant and working frequency range, sensitivity, temperature compensation technique are reviewed. • Recommendations for seismic surveying in the context of carbon storage monitoring are discussed. Carbon capture and storage (CCS) technology has gained widespread attention as a mitigation strategy in the battle against climate change. This technology involves capturing and storing carbon dioxide (CO 2) gas deep underground. Conventionally, seismic surveys employing electrical geophones were utilized to monitor the storage integrity. However, the advantages of fibre optic sensors (FOSs) have been seen as having great potential to replace conventional electrical geophones. Therefore, this paper reviews various state-of-the-art FOSs geophones and accelerometers categorizing them into cantilever structures, diaphragm structures, hinge structures and other structures. The review paper focuses only on point sensing and quasi-distributed FOSs and does not cover fully distributed FOSs. Finally, key findings and future prospects related to FOSs geophones and accelerometers were discussed within the context of seismic surveying for carbon storage monitoring. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the deterministic errors caused by non-orthogonal installation, calibration factor, zero bias and other factors in production and in the use of accelerometers need to be calibrated by high-precision instruments, support vector machine regression is used to process the original data output by the accelerometer, and the processed data of each axis are used to establish a parameter calibration model without reference datum through the relationship between the output value of each axis of accelerometer, gravity acceleration and coaxial reversal in the paper. Then, the adaptive mutation rate is used to dynamically adjust the number of reverse learning particles, and the particles of particle swarm optimization algorithm are selected and adjusted according to the reverse learning, which solves the problems that particle swarm optimization algorithm tends to fall into localoptimum and the convergence speed is slow, through which a fast, accurate and simple calibration can be realized, and the performance of particle swarm optimization algorithm is improved. The calibration experiment shows that the improved particle swarm optimization algorithm has higher accuracy and faster convergence speed than the particle swarm optimization algorithm, and the calibration parameter accuracy is higher than that of the least square method, which does not need the datum of each axis. The calibration model proposed in this paper can realize a benchmark-free calibration outside the laboratory. At the same time, the improved particle swarm optimization algorithm can obtain calibration parameters with higher accuracy and faster speed in the rapid calibration, which provides the idea of a new model for accelerometer calibration and expands the application environment of accelerometer. [Display omitted] • The relationship between the gravity acceleration and the output values of each axis of the accelerometer is used. • According to the established model, SVR is used to preprocess the input data of the model. • Reverse learning and mutation rate are proposed to modify the particles of PSO. [ABSTRACT FROM AUTHOR]

Cutting forces are valuable for monitoring machining processes, as they allow the evaluation of tool wear, process stability, and workpiece quality. This paper presents a new non-intrusive method for monitoring cutting forces using a Kalman filter (KF) and Rauch-Tung-Striebel (RTS) smoothing. The method incorporates structural acceleration measurements as well as feed drive motor currents, thereby achieving wide estimation bandwidth and steady-state (DC) gain preservation. The KF + RTS smoothing are parameterized using spindle speed-sweeping cutting tests, which significantly reduces the experimental effort for model identification. The approach was validated in different machining conditions, which demonstrated good agreement with dynamometer measurements. [ABSTRACT FROM AUTHOR]

Existing controller designs for pendulous accelerometers focus on maintaining angular balance, emphasizing low-frequency measurement performance while neglecting high-frequency dynamic control accuracy. However, in high dynamic conditions, dynamic control errors directly impact accelerometer measurement performance. Through mechanical analysis, residual torque on the pendulum is identified as a major contributor to dynamic control errors in pendulous accelerometers. To enhance the dynamic performance, a torque-balance Linear Quadratic Regulator (LQR) control method is proposed, aiming to suppress dynamic control errors by implementing torque balance control. The effectiveness and stability of this control method is demonstrated through simulations and experiments. Furthermore, this study compares the advantages of torque-balance LQR closed-loop systems in dynamic compensation. The results illustrate that the proposed method significantly enhances accelerometer dynamic control performance and compensated dynamic accuracy, thereby increasing reliability and accuracy in high dynamic conditions. [Display omitted] • This paper explains and emphasizes the limitations of angle-balance based traditional PID control of pendulous accelerometers in high dynamic measurements. • Torque balance is introduced as the primary control objective for pendulous accelerometers, enhancing control theories and dynamic performance. • The effectiveness and stability of the method are demonstrated theoretically and experimentally. • Discusses and demonstrates significant improvements in compensated dynamic accuracy and practical applicability through torque balance control. [ABSTRACT FROM AUTHOR]

This paper presents an efficient in-field calibration method tailored for low-cost triaxial MEMS gyroscopes often used in healthcare applications. Traditional calibration techniques are challenging to implement in clinical settings due to the unavailability of high-precision equipment. Unlike the auto-calibration approaches used for triaxial MEMS accelerometers, which rely on local gravity, gyroscopes lack a reliable reference since the Earth's self-rotation speed is insufficient for accurate calibration. To address this limitation, we propose a novel method that uses manual rotation of the MEMS gyroscope to a specific angle (360°) as the calibration reference. This approach iteratively estimates the sensor's attitude without requiring any external equipment. Numerical simulations and empirical tests validate that the calibration error is low and that parameter estimation is unbiased. The method can be implemented in real-time on a low-energy microcontroller and completed in under 30 seconds. Comparative results demonstrate that the proposed technique outperforms existing state-of-the-art methods, achieving scale factor and bias errors of less than 2. 5 × 1 0 − 2 for LSM9DS1 and less than 1 × 1 0 − 2 for ICM20948. [ABSTRACT FROM AUTHOR]

Background: Gait event detection (GED) is an important aspect in identifying and interpret a user's gait to assess gait abnormalities and design intelligent assistive devices.Research Question: There is a need to develop robust GED models that can accurately detect various gait instances in different scenarios and environments.Methods: This paper presents a novel method of detecting heel strikes (HS) and toe offs (TO) during the user's gait cycle using a modified Long Short-Term Memory (LSTM) networks approach. The method was tested on a database from Movement Analysis in Real-world Environments using Accelerometers (MAREA) (n = 20 healthy subjects) that consisted of walking and running in indoor and outdoor environments with accelerometers positioned on waist, wrist and both ankles. Modifications include oversampling, composite accelerations and optimizing the LSTM network architecture were made.Results: Performance of our modified model was found to be better than six state-of-the-art GED algorithms, with a median F1 score of 0.98 for Heel Strikes and 0.98 for Toe Offs in the scenario of steady walking in an indoor environment, and a median F1 score of 0.94 for Heel Strikes and 0.68 for Toe-offs in the scenario of walking and running in an outdoor environment.Significance: This paper highlights the potential of the single proposed model to be an alternative to the six GED models in gait detection under various conditions. [ABSTRACT FROM AUTHOR]

• The paper explores the fusion of traditional password-based and hand-writing-based authentication. • A lightweight equipment is proposed, that exploits a sensor-equipped pen to be used on normal paper. • The experiments compare Machine Learning-based verification with the use of plain DTW without training. • The experiments compare different choices to compute the DTW similarity score from a multiple template gallery. • The experiments compare verification accuracy with a different number of templates in the user gallery. The paper presents experiments with the home-made, low-cost prototype of a sensor-equipped pen for handwriting-based biometric authentication. The pen allows to capture the dynamics of user writing on normal paper, while producing a kind of password (passphrase) chosen in advance. The use of a word of any length instead of the user's signature makes the approach more robust to spoofing, since there is no repetitive pattern to steal. Moreover, if the template gets violated, this is much less harmful than signature catch. The entailed sensors are a pair of accelerometer and gyroscope and a pressure sensor. The aim is a natural yet precise interaction, that allows recognizing the user by the signals recorded while producing a specific word chosen during enrollment and possibly changed later. The pen can be exploited in a number of applications requiring user recognition, yet relieving from the need to learn complex procedures, and to undergo critical capture operations. The approach fuses the use of a kind of password, though not necessarily complex as those requested by traditional approaches, and biometric recognition. The novelty with respect to most proposals in literature is the combination of three elements at once: the matching of any handwritten text instead of user signature, the on-line capture of seven sensor signals to recognize handwriting dynamics (three from accelerometer, three from gyroscope and one from pressure sensor), and the use of normal paper instead of a digitizing tablet. Presented experiments test two different recognition techniques, implemented by two modules that can be alternatively plugged into the system. An SVM-based verification module entails to extract the most relevant features from writing dynamics, and to acquire a sufficient amount of enrolling data (30 samples per user) to train an SVM for each user. A pure Dynamic Time Warping (DTW) verification module does not require such training, and is tested using either a gallery with the same number of templates per user as those used for SVM training, or with a gallery containing a much lower number of templates per user (namely 5). Obtained results encourage further investigation of lightweight strategies for written password dynamics recognition. [ABSTRACT FROM AUTHOR]

• A dynamic characterization method is proposed for accelerometer dynamic calibration. • An iteration estimation procedure of the model coefficients and the order is performed by means of the equivalent matrix of the excitation and response signals, which overcomes the coupling effect of model order on the parameter precision. • The dynamic characteristics of accelerometer could be improved by means of zero-pole offset of the dynamic model. • The requirement of the excitation signal generated by the Hopkinson bar is quantified by the dynamics analysis of stress propagation. • The impact resistance of accelerometer could be improved by vacuum encapsulating technology. The dynamic characterization of accelerometers is the prerequisite and insurance for highly accurate measurement of dynamic acceleration signals. The main problem of dynamic characterization is the lack of effective methods to deal with model coefficients and the order coupling effect, which could severely influence the identification accuracy. This paper presents a novel approach to solve the identification coupling problem through synchronous iteration and compensation. In order to guarantee the precision of the dynamic model, the Hopkinson bar dynamic calibration system with a traceable excitation acceleration signal is proposed. The feasibility of dynamic modelling is proved by the data comparison of experiment result and model regression result in the time domain. Based on the dynamic model, a dynamic compensation model is established, which improves the dynamic performance by frequency coverage extension. Finally, the results of the dynamic modelling and dynamic compensation are demonstrated by means of the calibration experiment in the paper. [ABSTRACT FROM AUTHOR]

Hand deformities often become a major obstacle in conducting everyday tasks for many people around the globe. Rehabilitation procedures are widely used for strengthening the hand muscles, which in turn leads to the restoration of functionality of the affected hand. This paper conducts a survey of various wearable technologies that can be used to accurately quantify the rehabilitation progress in terms of fingers' hand joint angles. Based on the data acquisition methods, these technologies can be mainly divided into six categories: 1) Flex sensor based; 2) Accelerometer based; 3) Vision based; 4) Hall-effect based; 5) Stretch sensor based; and 6) Magnetic sensor based. The main focus of some of the discussed technologies has been on various other domains, like gaming gloves, tele-manipulation etc., and thus their usage for rehabilitation of hand joints could be quite interesting. This paper analyzes the strengths and weaknesses of these wearable technologies along with some examples of their implementations. Based on our survey results, we propose a wearable glove for accurately measuring hand joint angles with enhanced features for better diagnosis and rehabilitation. [ABSTRACT FROM AUTHOR]

Abstract During the last two decades, accelerometers on board of the CHAMP, GRACE, GOCE and Swarm satellites have provided high-resolution thermosphere density data to improve our knowledge on atmospheric dynamics and coupling processes in the thermosphere-ionosphere region. Most users of the data have focused on relative density variations. Scale differences between datasets and models have been largely neglected or removed using ad hoc scale factors. The origin of these scale differences arises from errors in the aerodynamic modelling, specifically in the modelling of the satellite outer surface geometry and of the gas-surface interactions. Therefore, the first step to remove the scale differences is to enhance the geometry modelling. This work forms the foundation for the future improvement of characterization of satellite aerodynamics and gas-surface interactions models at TU Delft, as well as for extending the use of sideways and angular accelerations in the aerodynamic analysis of accelerations and derivation of thermosphere datasets. Although work to improve geometry and aerodynamic force models by other authors has focused on CHAMP and GRACE, this paper includes the GOCE and Swarm satellites as well. In addition, it uses a density determination algorithm that is valid for arbitrary attitude orientations, enabling a validation making use of attitude manoeuvres. The results show an improvement in the consistency of density data between these four missions, and of data obtained before, during and after attitude manoeuvres of CHAMP and Swarm. The new models result in larger densities, compared to the previously used panel method. The largest average rescaling of density, by switching to the new geometry models is reached for Swarm at 32%, the smallest for GRACE at 5%. For CHAMP and GOCE, mean differences of 11% and 9% are obtained respectively. In this paper, an overview of the improvements and comparisons of data sets is provided together with an introduction to the next research phase on the gas-surface interactions. [ABSTRACT FROM AUTHOR]

Abstract The developments in structural health monitoring techniques have led to the invention of various sensors that can be effective damage indicator. Due to environmental or electromagnetic effects and need for constant energy source, the traditional sensors are unable to provide accurate and continuous measurements. In light of these events, new and improved sensors have been developed, along with wireless technology, to assist the monitoring process. With the need of detecting more than one damage parameters, multiplexed sensors have been the main interest of researchers. This paper deals with the different sensors used for determination of strain, acceleration and corrosion. A brief comparative study has been performed and presented in the following review paper. Multiplexed Fiber optics sensor have proved quite effective for SHM and proved to be a good competitor with other sensors. Optimum Sensor Placement technique developed for low level damage diagnosis is Iterated Improved Reduced System (IIRS) Method. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • The paper presents the applied method for indirect surface finishing measurement in aerospace manufacturing industry. • This paper introduces a method to quantify and correlate the deburring stage with the features extracted from sensors data. • This study covers signal processing, feature extraction and analytical method for surface finish quality estimation in deburring processes. • Wavelet decomposition and Welch spectrum estimate are used as a signal processing and feature extraction method. • The features are used as the basis for classification analysis by adaptive neuro-fuzzy inference system (ANFIS). Abstract Manufacturing of aerospace components consists of combination of different types of machining, finishing, and measuring processes. One of the finishing processes is deburring, i.e. a finishing process to remove burrs from work coupons after a boring hole process. Deburring is conducted to achieve required surface finish quality prior to further processes in assembly line. This paper introduces sensor data analysis as a tool to quantify and correlate the deburring stage with the features extracted from sensors data. This study covers signal processing, feature extraction and analytical method to determine its relevancy to the surface finish quality from deburring process. Wavelet decomposition and Welch's spectrum estimate is used as a signal processing and feature extraction method. Consequently, the features are used as the basis for analysis by adaptive neuro-fuzzy inference system (ANFIS). The ANFIS yields the output corresponding to the predicted surface finish quality in terms of boss hole chamfer length and the stage classification of deburring process. The results show a decreasing trend in measured vibration signal, which is qualitatively well correlated to the deburring stage and the development of chamfer length during deburring process. [ABSTRACT FROM AUTHOR]

The fluid has great influence on the motion and deformation of the structure under vibration condition, which makes the structure produce a strongly nonlinear vibration. The spent fuel storage rack, which submerged in liquid, is an important equipment in nuclear power plant. Thus, it is extremely important to study fluid-structure interaction problem of the spent fuel storage rack among the equipment in nuclear power plant. Moreover, the fluid-structure interaction between the water around the spent fuel storage rack and the structure may compromise the integrity of the storage rack. Therefore, it is highly necessary to research the characteristic of the fluid-structure interaction among the racks. In this paper, the free vibration experiments, based on the 3:10 model of the spent fuel storage rack in CAP1400 (a new reactor type designed by China), were conducted to study the fluid-structure interaction effect of adjacent racks. The parameters of fluid-structure interaction among the racks was measured under different clearances through free vibration experiment. Ultimately, the formula for calculating the added mass of the rack under different gap sizes was put forward in this paper. This study can provide fluid-structure interaction parameters for structural dynamic analysis of the spent fuel storage rack, and offer the assistance for the design and manufacture of spent fuel pools and high-density spent fuel storage racks, ultimately. [ABSTRACT FROM AUTHOR]

This paper presents a health condition monitoring solution that detects an elderly accidental fall occurrence. The fall detection algorithm implements both accelerometer-based and sound-based detections for the possible occurrence of a valid fall. The accelerometer-based fall detection is instrumental in the detection of a valid fall occurrence. However, it has been shown that by using accelerometer alone is insufficient to accurately detect a fall, as the accelerometer misinterprets some daily motion activities and classified them as valid falls. The sound sensor can be used to detect the sound pressure generated from a resultant fall, but sound pressure cannot by itself be used as a reliable indicator of a fall. Thus, a fuzzy logic-based fall detection algorithm is developed to process the output signals from the accelerometer and sound sensor, where a valid fall activity detected by the accelerometer, coupled with a detected sound pressure from the resultant fall can infer an occurrence of a valid fall. This paper demonstrates the fuzzy logic algorithm to improve the accuracy of detecting a valid fall as compared to the accelerometer only fall detection algorithm and it can be demonstrated that the algorithm is capable of minimizing false fall detections per day from high of 1.37 to low of 0.06 . [ABSTRACT FROM AUTHOR]

Background and objectives Computerised posturography can be used to assess standing balance, and can predict poor functional outcomes in many clinical populations. A key limitation is the disparate signal filtering and analysis techniques, with many methods requiring custom computer programs. This paper discusses the creation of a freely available web-based software program, SeeSway ( www.rehabtools.org/seesway ), which was designed to provide powerful tools for pre-processing, analysing and visualising standing balance data in an easy to use and platform independent website. Methods SeeSway links an interactive web platform with file upload capability to software systems including LabVIEW, Matlab, Python and R to perform the data filtering, analysis and visualisation of standing balance data. Input data can consist of any signal that comprises an anterior-posterior and medial-lateral coordinate trace such as center of pressure or mass displacement. This allows it to be used with systems including criterion reference commercial force platforms and three dimensional motion analysis, smartphones, accelerometers and low-cost technology such as Nintendo Wii Balance Board and Microsoft Kinect. Filtering options include Butterworth, weighted and unweighted moving average, and discrete wavelet transforms. Analysis methods include standard techniques such as path length, amplitude, and root mean square in addition to less common but potentially promising methods such as sample entropy, detrended fluctuation analysis and multiresolution wavelet analysis. These data are visualised using scalograms, which chart the change in frequency content over time, scatterplots and standard line charts. This provides the user with a detailed understanding of their results, and how their different pre-processing and analysis method selections affect their findings. Results An example of the data analysis techniques is provided in the paper, with graphical representation of how advanced analysis methods can better discriminate between someone with neurological impairment and a healthy control. Conclusions The goal of SeeSway is to provide a simple yet powerful educational and research tool to explore how standing balance is affected in aging and clinical populations. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the accuracy of three measurement systems estimating gait speed during a loaded military march over graded terrain. Systems developed by the Swiss and Netherlands Armed Forces and a commercial wrist-based device were evaluated in comparison to a Global Positioning System. The first part of the paper focuses on the development of the Dutch system, where speed is estimated from a chest worn accelerometer and body measurements. For this validation study 36 subjects were walking or running 13 laps of 200 m at different speeds. Results showed that walking and running speed can be estimated with a R2adj of 0.968 and 0.740, respectively. In the second part of this paper, data from 64 soldiers performing a 35 km march were used to evaluate the accuracy of three measurement systems in estimating speed. Data showed that estimating gait speed with a single accelerometer can be accurate for military activity, even without prior individual calibration measurements. However, predictions should be corrected for confounders such as body size and shoe type to be accurate. Both, downhill and uphill walking led to changes in gait characteristics and to an overestimation of speed by up to 10%. Correcting for slope or gradient using altimetry in future algorithms/experiments could improve the estimation of gait speed. [ABSTRACT FROM AUTHOR]

This paper evaluates a new and adaptive real-time cadence detection algorithm (CDA) for unconstrained sensor placement during walking and running. Conventional correlation procedures, dependent on sensor position and orientation, may alternately detect either steps or strides and consequently suffer from false negatives or positives. To overcome this limitation, the CDA validates correlation peaks as strides using the Sylvester's criterion (SC). This paper compares the CDA with conventional correlation methods. 22 volunteers completed 7 different circuits (approx. 140 m) at three gaits-speeds: walking (1.5 m s − 1 ), running (3.4 m s − 1 ), and sprinting (5.2 and 5.7 m s − 1 ), disturbed by various gait-related activities. The algorithm was simultaneously evaluated for 10 different sensor positions. Reference strides were obtained from a foot sensor using a dedicated offline algorithm. The described algorithm resulted in consistent numbers of true positives (85.6–100.0%) and false positives (0.0–2.9%) and showed to be consistently accurate for cadence feedback across all circuits, subjects and sensors (mean ± SD: 98.9 ± 0.2%), compared to conventional cross-correlation (87.3 ± 13.5%), biased (73.0 ± 16.2) and unbiased (82.2 ± 20.6) autocorrelation procedures. This study shows that the SC significantly improves cadence detection, resulting in robust results for various gaits, subjects and sensor positions. [ABSTRACT FROM AUTHOR]

In order to increase accuracy of inertial navigation system, it is necessary to calibrate inertial measurement unit (IMU). In some applications, acceleration applied to accelerometer is larger than its linear operation area, thus input-output relationship deviates from linear form. Main purpose of this paper is to estimate coefficients of nonlinear terms in input-output relationship of accelerometer to increase its measurement accuracy. A centrifuge device is used to generate high acceleration levels as calibration reference. In this paper, linear and nonlinear coefficients of a low cost MEMS accelerometer (x axis) are estimated using continuous calibration method (using earth gravity vector (0–1 g )) and centrifuge test (accelerations between 0 and 16 g generated by the centrifuge). In addition, adequacy of the nonlinear model is evaluated. The well-known F test is used to compare linear and nonlinear models. Also, errors of linear and nonlinear models are compared using Mean Square Error (MSE). Experimental results showed that nonlinear model has improved measurement accuracy about 11.7%. [ABSTRACT FROM AUTHOR]

Low frequency accelerometer calibration is generally a time consuming process and requires special considerations as well as excitation techniques due to the low frequency and low acceleration levels encountered. A limited work has been reported on this aspect so far. The paper discusses the measurement considerations associated with the implementation of optical encoder technology in low frequency accelerometer calibration. The method may be considered as primary one as reference is displacement rather than acceleration. The paper provides comparison of the optical encoder reference technique to manufacturer’s accredited primary calibration of an artifact as per ISO 16063-11. The work also discusses the measurement uncertainty associated with low frequency accelerometer calibration using an optical encoder sensor. [ABSTRACT FROM AUTHOR]

This paper draws on Elias's sociology of knowledge to provide a critical assessment of illness narratives. Focusing on a cohort of chronic obstructive pulmonary disease (COPD) patients (n = 26), the paper employs a comparative analysis of mixed method data derived from qualitative interviews, quantitative questionnaires, and physiological and accelerometer testing. The article firstly compares four narratives conveyed in interviews with the broader paradigmatic approach to illness narratives and existing COPD-specific studies. It then explores the relationship between these ‘stories’ and COPD patients' biographical contingencies (e.g. age, wealth, context of diagnosis) and embodied condition (e.g. co-morbidities, lung function), demonstrating how illness narratives are shaped by both broader social structural factors and embodied experience. Invoking Elias we further find that different narrative subthemes are varyingly affected by patients' emotional engagement and ontological security and thus that people are differently enabled or constrained to present illness narratives that are consistent with their broader social and physical condition. Consequently, while narratives, social structure and embodied experience are interdependent, our reading of ‘truth’ must be sensitive to the social positioning of the ‘teller’ and the specific content being relayed. The paper therefore presents a more systematic, comparative, bio-psycho-social analysis than has hitherto been produced. [ABSTRACT FROM AUTHOR]

GRACE and GRACE-FO require the reduction of non-gravitational accelerations, through an onboard accelerometer, to accurately estimate variations in the Earth's gravitational field. Accuracy in the accelerometer measurements is vital to this mass change recovery and significant errors will reduce the quality of the recovered science data. This paper describes our current characterization of error sources in the GRACE-FO accelerometers and the error mitigation strategies used in the generation of the JPL/CSR GRACE-FO Version 04 accelerometer data for public release. Our current error characterization model describes flicker noise on readings of one accelerometer plate pair on GRACE-D, impulse response limitations on both accelerometers, nonphysical once per revolution variations in the roll angular acceleration measurements on both accelerometers, significant correlated errors on all axes on GRACE-D, nominal thermal bias dependencies on both accelerometers, and expected nominal inter-measurement aliasing (cross-talk - expected as heritage from GRACE). Attitude reconstruction strategies incorporating star camera, inertial measurement unit, and magnetorquer measurements visibly improve angular acceleration recovery independent of the accelerometer. Error mitigation strategies for the linear acceleration measurements, associated with the calibrated accelerometer product provided in the version 04 release, dramatically improve estimates of the Earth's gravity field. While many of the error characterizations described here are not necessary for constructing the accelerometer transplant from GRACE-C to GRACE-D, as is currently done in the Version 04 release, the error characterizations described here lay the groundwork for future use of calibrated/corrected GRACE-D accelerometer data. [ABSTRACT FROM AUTHOR]

The paper presents the analysis of the influence of the method of accelerometers attachment to the ground on the recorded level of vibration acceleration. Measurements of vibrations were conducted using two types of accelerometers with various weights and measuring accuracy. Accelerometers were mounted to the ground in four different methods using various mounting bases anchored in the ground with one, three and four rods. The vibrations were generated using Light Falling Weight Deflectometer (LFWD) and recorded in longitudinal, transversal and vertical directions at the distance of 5 and 10 m from the source of vibrations. Measurements of vibration accelerations were carried out using 32-channel and 24-bit mobile data acquisition system - SIEMENS LMS SCADAS Recorder, as well as a set of 18 low-frequency, seismic piezoelectric accelerometers type 8340, manufactured by Brüel&Kjær, and two triaxial, high sensitivity accelerometers type TLD356B18 manufactured by PCB Piezotronics. The comparative analysis of Peak Particle Accelerations (PPA) has demonstrated that the method of attaching of accelerometers to the ground is crucial for the credibility of measurements. The best way of attaching transducers to the ground was shown in the paper. The resulting values of vibration acceleration may constitute a basis for a reliable analysis of vibration in the subsoil, which is particularly important in terms of the credibility of assessment of the impact of physical pollution in a form of vibrations propagated in the environment on the newly designed buildings and their future users. [ABSTRACT FROM AUTHOR]

In this paper we highlight a clustering algorithm for the purpose of identifying sleep and wake periods directly from actigraphy signals. The paper makes use of statistical Principal Component Analysis to identify periods of rest and activity. The aim of the proposed methodology is to develop a quick and efficient method to determine the sleep duration of an individual. In addition, a robust method that can identify sleep periods in the accelerometer data when duration, time of day varies by individual. A selected group of 10 individual’s sensor data consisting of actigraphy from an accelerometer (3-axis), near body temperature, and lux sensors from a single GENEActiv watch worn on the non-dominant hand. The actigraphy of each individual was collected 24 hours a day for a period spanning 80 days. We highlight that a simple data preprocessing stage followed with a 2 phase clustering method provides results that align with previously validated methodologies. [ABSTRACT FROM AUTHOR]

Obesity is a global health issue that affects 2.1 billion people worldwide and has an economic impact of approximately $2 trillion. It is a disease that can make the aging process worse by impairing physical function, which can lead to people becoming more frail and immobile. Nevertheless, it is envisioned that technology can be used to aid in motivating behavioural changes to combat this preventable condition. The ubiquitous presence of wearable and mobile devices has enabled a continual stream of quantifiable data (e.g. physiological signals) to be collected about ourselves. This data can then be used to monitor physical activity to aid in self-reflection and motivation to alter behaviour. However, such information is susceptible to noise interference, which makes processing and extracting knowledge from such data challenging. This paper posits our approach that collects and processes physiological data that has been collected from tri-axial accelerometers and a heart-rate monitor, to detect physical activity. Furthermore, an end-user use case application has also been proposed that integrates these findings into a smartwatch visualization. This provides a method of visualising the results to the user so that they are able to gain an overview of their activity. The goal of the paper has been to evaluate the performance of supervised machine learning in distinguishing physical activity. This has been achieved by (i) focusing on wearable sensors to collect data and using our methodology to process this raw lifelogging data so that features can be extracted/selected. (ii) Undertaking an evaluation between ten supervised learning classifiers to determine their accuracy in detecting human activity. To demonstrate the effectiveness of our method, this evaluation has been performed across a baseline method and two other methods. (iii) Undertaking an evaluation of the processing time of the approach and the smartwatch battery and network cost analysis between transferring data from the smartwatch to the phone. The results of the classifier evaluations indicate that our approach shows an improvement on existing studies, with accuracies of up to 99% and sensitivities of 100%. [ABSTRACT FROM AUTHOR]

Pipe leak location estimates are commonly conducted using Vibro-Acoustic Emission (VAE) based methods, usually using accelerometers or hydrophones. Successful estimation of a leak's location is dependent on a number of factors, including the speed of sound, resonance, backfill, reflections from other sources, leak shape and size. However, despite some investigation into some of the aforementioned factors, the influence of backfill type on a leak's VAE signal has still not been experimentally quantified. A limited number of studies have attempted to quantify the effects of backfill. However, all of these studies couple other variables which could be equally responsible for their observed changes in leak signal. There have been no controlled studies where one variable can be directly compared to one another (i.e. all variables remain constant, only changing backfill type). The aim of this paper is to better characterise the influence of backfill on a leak's VAE signal by individually isolating all variables. For the first time, this paper demonstrates the influence of backfill on leak VAE signal by keeping all other variables consistent. It was found that the backfill type had a strong influence on the frequency and amplitude of leak signals, which is likely to have a significant impact on the accuracy of leak location estimates. [ABSTRACT FROM AUTHOR]

There exist numerous navigation solutions already implemented into various navigation systems. Depending on the vehicle in which the navigation system is used, it can be distinguished in most cases among; navigation, tactical, and commercial grade categories of such systems. The core of these systems is formed by inertial sensors, i.e. accelerometers and angular rate sensors/gyros. Navigation and tactical grade systems commonly rely on fiber optic/ring laser gyros and servo/quartz accelerometers with high resolution, sensitivity, and stability. In the case of cost-effective navigation systems, for example piloted light and ultralight aircraft, usually use commercial grade sensors, where the situation differs. The sensor outputs are less stable and sensitive, and suffer from manufacturing limits leading to temperature dependency, bias instability, and misalignment which introduces non-negligible disturbances. These conditions commonly limit the applicability of the navigation solution since its stand-alone operation using free integration of accelerations and angular rates is not stable. This paper addresses a cost-effective solution with commercial grade inertial sensors, and studies the performance of different approaches to obtain navigation solution with robustness to GNSS outages. A main goal of this paper is thus comparison of a nonlinear observer and two extended Kalman filter solutions with respect to the accuracy of estimated quantities and their sensitivity to GNSS outages. The performance analyses are carried out on real flight data and evaluated during phases of the flight when the solutions are challenged by different environmental disturbances. [ABSTRACT FROM AUTHOR]

The paper deals with the results of an experimental campaign carried out on a post tensioned concrete beam with the aim of investigating the possibility to detect early warning signs of deterioration based on static and/or dynamic tests. The beam was tested in several configurations aimed to reproduce 5 different phases of the ‘life’ of the beam: in the original undamaged state, under increasing loss of tension in the post tensioning cables, during and after the formation of cracks at mid span, after a strengthening intervention carried out by means of a second tension cable, during and after the formation of further cracks on the strengthened beam. Responses of the beam were measured by an extensive set of instruments consisting of accelerometers, inclinometers, displacement transducers, strain gauges and optical fibers. In this paper data from accelerometers and displacement transducers have been exploited. The paper presents the test program and the dynamic characterization of the beam in the different damage scenarios in terms of the first modal frequency, identified from dynamic tests and of the bending stiffness monitored during static tests. [ABSTRACT FROM AUTHOR]

Monitoring and analyzing floor vibrations to determine human activity has major applications in fields such as health care and security. For example, structural vibrations could be used to determine if an elderly person living independently falls, or if a room is occupied or empty. Monitoring human activity using floor vibration promises to have advantages over other methods. For example, it does not have the privacy concerns of other methods such as vision-based techniques, or the compliance challenges of wearable sensors. The analysis of the signals becomes a classification problem determining the type of human activity. Unfortunately only a few research groups are performing research of this subject even though there is a significant number of techniques that could be applied to this field. To date, no systematic study about the challenges and advantages of using different types of algorithms for this problem has been performed. This paper proposes a benchmark problem to: (i) encourage researchers to design new algorithms for monitoring human activity using floor vibrations, (ii) provide a dataset to test new algorithms, and (iii) allow the comparison of proposed methods based on a set of standard metrics. The benchmark consists of seven different cases of increasing difficulty. Each case has a specific number of sensors, calibration signals, and type of floor excitation forces to be considered. The paper also proposes specific metrics that enable the direct comparison of different techniques. Research groups interested in monitoring human activity using floor vibrations are encouraged to use the experimental data and evaluation metrics published in this paper to develop their own methodologies. This will enable the community of researchers to easily compare and contrasts techniques and better understand what type of methods will be appropriate in different applications. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new procedure for the extended calibration of charge-mode accelerometers. This procedure covers two main stages. The first one follows the general guidelines of international guides that relate to calibrating accelerometers in the frequency domain and are implemented using a weighted-least-squares method. These guidelines are modified herein for the advanced modelling of the charge-mode accelerometer. The second stage provides a new solution which is an extension of the standard calibration using the upper bound of the dynamic error (UBDE) according to the integer-square criterion and a fixed-point algorithm that enables this error to be determined. Additionally, the special functions, representing the values of the dynamic error for the predetermined ranges of the parameters associated with the mathematical model of the charge-mode accelerometer, are determined. Such functions ensure an easy and rapid execution of the second stage of calibration without conducting complicated procedures to determine the value of the UBDE. The extended procedure proposed here enables the comparison of accelerometers with similar technical parameters, but produced by different manufacturers of measuring sensors that frequently compete with each other. In this way, we can select the accelerometer that will produce the lower value of UBDE. [ABSTRACT FROM AUTHOR]

Nowadays, the amount of elder people living alone is increasing, with all the risks that it involves, maybe the most dangerous threat that they face is fall off with nobody around to help them. A fall at an advanced age usually leads to consequences like bones and hip fracture, which in addition to the low mobility that these people present, make to stand up impossible for them. This situation can get worse if after the fall a person loses consciousness, making it impossible to contact to a third party for help by means of a mobile phone or something similar. Different solutions have been developed in order to accomplish this problem, but some of them are not realistic enough, for example some video solutions invade our privacy, and those which are based on mobile phones expect the user to go everywhere with it. In this paper, it is proposed a low cost solution based on an 9-axis IMU (Inertial Measure Unit), which counts with an accelerometer, gyroscope and magnetometer that will give us the needed information to build a fall detector supported by machine learning. This system will include a gateway, which will be responsible of the data collection and the most complex computations. [ABSTRACT FROM AUTHOR]

Quartz Flexible Accelerometer (QFA) system has gained extensive utilization in related navigation system due to the high precision, exceptional stability, and rapid response. The operational amplifier as the pivotal component of the analog servo circuit intricately relative to the performance of the QFA system. In this paper, the electrical mechanism of the QFA readout system and the influence of operational amplifier noise on the noise of QFA readout system is studied, a low-noise Class AB amplifier for the servo circuit of QFA readout system designed and fabricated by 180 nm BCD process, and the QFA readout system with the designed amplifier is simulated. The experimental results show that the low-noise amplifier achieved equivalent input current and voltage noise of 5.75pA/sqrt (Hz) and 12.7nV/sqrt (Hz) separately. Simulation results shows the designed QFA servo circuit has achieved resolution of 35 μ g and noise of 2.1 μ g/sqrt (Hz) @1Hz. [ABSTRACT FROM AUTHOR]

• - A new accelerometer is designed for highest possible sensitivity using a novel surface micromachining approach. • - A modified topology optimization methodology is developed and implemented to maximize sensitivity of the desired MEMS accelerometer for landslide monitoring. • - Despite of traditional topology optimization approaches, a range of variable inertial forces considered in designing this new MEMS accelerometer. • - In addition, as another novel modification in topology optimization algorithm, the performance and manufacturing constraints are inherently applied into the optimization process. • - In order to control cross-sensitivity, as another unique approach the sensitive vibration mode was recognized and was kept in the sensing direction by preventing the mode switch. • - The design of this novel MEMS accelerometer is fully fabricated and tested for performance to confirm the efficiency of the design. MEMS accelerometers capable of detecting weak motions are required in measuring landslide movements. The sensor's sensitivity and its independent readings of various acceleration modes are crucial factors in the design of these sensors. In addition, it is highly desired to fabricate these sensors with low-cost and flexible manufacturing technologies such as surface micromachining. This paper presents an advanced topology optimization approach to design the suspended structure for the maximum level of sensitivity and for a low cross-sensitivity in the reading of principal and lateral modes, while it is manufacturable with a low-cost surface micromachining process. The effect of inertial forces as a variable loading condition and mode switch prevention is applied using a meta -heuristic topology optimization method with simulated annealing. Considering the unique results of this topology optimization, PolyMUMPs surface micromachining is employed to manufacture the developed sensor. The fabricated sensor is subjected to measurement and subsequent comparison with analogous designs from the literature. The outcomes reveal a sensitivity of 0. 3 pF/g and cross-axis sensitivity of 0. 048%, signifying a noteworthy advancement for surface micromachined accelerometers. Notably, this design, coupled with the devised topology optimization methodology, holds the potential for adoption by sensor manufacturers for diverse commercial applications. [ABSTRACT FROM AUTHOR]

This paper presents a mixed H 2 / H ∞ performance control algorithm for designing closed-loop pendulous accelerometer controllers. The algorithm aims to enhance the dynamic performance of accelerometers while ensuring robustness and disturbance resistance. By integrating H 2 control and H ∞ constraints, the algorithm effectively enhances dynamic performance while countering external disturbances and convex parameter uncertainties. The efficacy and reliability of the algorithm are validated through theoretical analysis and experimentation. A comparative experiments with the standard LQR control algorithm further demonstrate the superiority of the proposed algorithm in terms of robustness and dynamic performance. • Analysis of accelerometer systematic errors under varied conditions, with a torque integral-based controller design strategy. • Clear quantification of accelerometer failure constraints under parameter uncertainty. • Proposed Novel mixed H 2 / H ∞ algorithm enhances accelerometer dynamic performance and robustness. • Experimental evidence demonstrates the superiority of mixed H 2 / H ∞ control over standard LQR. [ABSTRACT FROM AUTHOR]

• The article designs FBG acceleration sensors for cable force monitoring of FAST. • The sensor has a high sensitivity factor for low-frequency signal measurements. • The sensor has a good temperature compensation performance. • The sensor's limiting structure ensures its survival during transportation. • Many tests demonstrate practical usefulness of the FBG accelerometer. The Five-hundred-meter Aperture Spherical radio Telescope (FAST) currently holds the distinction of being the largest single-aperture telescope worldwide. To ensure its secure functioning, it is crucial to monitor the cable force on its active reflecting surface cable network structure. In this paper, a fibre Bragg grating (FBG) accelerometer is developed to acquire low-frequency vibration signals of the cables. The sensor's cantilever beam structure enables high sensitivity measurement of low-frequency signals, while the symmetrical double fibre grating structure effectively eliminates the impact of temperature. The sensor's specific limiting structure ensures its survival during transportation and installation. A series of laboratory tests have been performed, and they demonstrate the excellent measurement performance, temperature insensitivity and practical usefulness of the accelerometer. Ninety FBG accelerometers are successfully installed on the site of the FAST project, where they are precisely measuring and documenting the magnitude of force exerted on the edge cables. [ABSTRACT FROM AUTHOR]

In this paper, modal parameters of a layered soil system comprising of a soft clay layer overlying a dense sand layer are identified from accelerometer recordings in a centrifuge test. For the first time, the subspace state space system identification (4SID) method was employed to identify the natural frequencies, damping ratios, and complex valued mode shapes while considering the non-proportional damping in a soil system. A brief review of system identification concepts needed for application of the 4SID techniques to structural modal identification is provided in the paper. The identified natural frequencies were validated against those estimated by transfer function spectra. The computed normal mode shapes were compared with closed-form solutions obtained from the one-dimensional shear wave propagation equation. The identified modal parameters were then employed to synthesize state space prediction models which were subsequently used to simulate the soil response to three successive base motions. The identified models captured acceleration time-histories and corresponding Fourier spectra reasonably well in the small and moderate shaking events. In the stronger third shaking event, the model performed well at greater soil depths, but was less accurate near the surface where nonlinearities dominated. [ABSTRACT FROM AUTHOR]

The paper presents an application of the continuous wavelet transform (CWT) to analyse the energy density of signals which maximize the integral-square error (ISE) at the output of two different accelerometers. These accelerometers were chosen as being examples of a large class of measuring instrument intended for the measurement of nondetermined input signals. Input signals constrained in magnitude only and simultaneously in magnitude and rate of change are considered in this paper. Scalogram analysis which provides a graphical representation of the signal energy density over the time-scale plane is discussed in detail in the second section of this paper. In the third, the results for the two accelerometers modelling are presented, while the fourth section presents methods for determining signals maximizing the ISE by using the genetic algorithm (GA). The final sections is devoted to a discussion of the results and analysis of the energy density based on the scalogram and corresponding conclusions with respect to properly determined signals maximizing ISE. For modelling the sensors MathCad15 was applied, while the maximizing signals and CWT analysis were executed using MATLAB2011. The methods presented in this paper constitute a novel approach for the estimation of the correctness of the signals maximizing the ISE by means of energy density analysis. [ABSTRACT FROM AUTHOR]

The paper considers the impact of uncertainties associated with estimated parameters of the accelerometer model on the maximum values of absolute dynamic error. Assessment of such impact constitutes a novelty in determination of the maximum values of absolute dynamic error. The parameter estimation method of the accelerometer mathematical model in the frequency domain and the procedure for calculation of the associated uncertainties under the current regulations, are discussed in detail in the second section of this paper. The procedures for determining the maximum values of absolute dynamic error generated at the output of an accelerometer, when the input signals are constrained in magnitude and both in magnitude and rate of change, are discussed in the third section. This dynamic error has been determined in relation to the standard represented by the 15-th order Butterworth filter. The possibilities of application of both the continuous and the discrete procedures of error calculation have also been presented. For the execution of numerical integration, the Romberg method has been applied. Based on theoretical consideration, the results of both the parameter estimation of the Endevco87 accelerometer and calculation of the corresponding uncertainties are presented in the fourth section. The fifth section is devoted to the presentation of the most important assumptions, which allow the determination of the maximum values of absolute dynamic error. An assessment of the impact of the uncertainties associated with the estimated parameters on the value of absolute error is discussed in the last section of this paper. The numerical calculations were executed in the MathCad14 program. [ABSTRACT FROM AUTHOR]

In order to predict the noise performance of a digital capacitive MEMS accelerometer and optimize the parameters of circuits, an improved quantization noise model is presented in this paper. Considering the distortion produced by the nonlinear characteristic of a quantizer, a system model of the Sigma-Delta (ΣΔ) accelerometer is established on the basis of describing function method model of a 1-bit quantizer. On the basis of this model, the formula of quantization noise before noise shaping, the transfer function of quantization noise and its expression in the signal band are presented. The model of quantization noise proposed in this paper includes the influence of sensing element parameters and the high non-linearity of a 1-bit quantizer. DC and AC simulation results show that the model can forecast the quantization noise in the signal band more accurately, compared with the models based on linear and quasi-linear model of a quantizer. The influences of electronic noise and sampling frequency on quantization noise are also analyzed. The results show that in ΣΔ MEMS accelerometer, electronic noise will lead to a reduction of the quantization gain and impact the noise shaping ability seriously. Increasing sampling frequency cannot effectively reduce the output quantization noise, but it will decline with sampling frequency at the slope of −3 dB/oct. [ABSTRACT FROM AUTHOR]

A redundant inertial measurement unit (IMU) is an inertial sensing device composed by more than three accelerometers and three gyroscopes. This paper analyzes the performance of redundant IMUs and their various sensors configurations. The inertial instruments can achieve high reliability for long periods of time only by redundancy. By suitable geometric configurations it is possible to extract the maximum amount of reliability and accuracy from a given number of redundant single-degree-of-freedom gyros or accelerometers. This paper gives general derivation of the optimum matrix which can be applied to the outputs of any combination of 3 or more sensors to obtain 3 orthogonal vector components based on their geometric configuration and error characteristics. Certain combinations of 4 or more instruments have the capability of detecting an instrument malfunction, those of 5 additional capabilities of isolating that malfunction to a particular sensor. [ABSTRACT FROM AUTHOR]

• A novel application of transfer learning for tool wear detection in turning processes using one-dimensional (1D) convolutional neural network (CNN). • Transfer learning of a source model for tool wear detection to a new CNC turning machine using new parameters. • Applications of low-cost MEMS and IEPE accelerometers in tool wear detection. • Evaluation of the performance of low-cost MEMS accelerometers in transfer learning. • Transfer learning using significantly smaller amount of vibration data for the target model in tool wear detection. This paper presents a novel application of transfer learning for tool wear detection in turning processes using one-dimensional (1D) convolutional neural network (CNN). The work also investigates the applications of low-cost MEMS as well as IEPE accelerometers in tool wear detection. Tool wear detection is performed using a classification method in which two classes of tool wear sizes are defined: class one and class two which correspond to tool wear sizes smaller or equal to 0.1 mm and bigger than 0.1 mm respectively. The advantage of transfer learning is that it utilizes knowledge from previously learned tasks and applies them to the related ones. In the case of CNC machines, tool wear mechanism in the turning process and the working principles are similar thus, transfer learning can be used to generalize the specific cutting condition to a broader use case. The transfer learning model in this paper uses a pretrained tool wear prediction model which was composed of 1D CNN layers with full connection layers and sufficient amount of data on a source CNC machine. The CNN layers of the pretrained model are frozen at first and then the full connection layers are trained with the new data from the target CNC machine with different cutting insert types. In this study, the application of transfer learning in tool wear size classification showed that the pretrained tool wear detection models can be transferred to other similar processes while maintaining a high accuracy. The accuracy of the transfer learning model was evaluated by comparing it with the results of a model developed from scratch using only the target machine data. It is demonstrated that the transfer learning maintained an accuracy of higher than 80%. In addition to this, the transfer learning model significantly increased the tool wear classification accuracy using the single-axis low-cost MEMS accelerometer from 58% to 85%. Moreover, the tool wear classification model using transfer learning significantly reduced the amount of data required for model development. To evaluate this, the accuracy of the transfer learning model was tested by further reducing the amount of the training data by maximum 80% and the model still showed an accuracy higher or equal to 80%. [ABSTRACT FROM AUTHOR]

This paper presents a new accelerometer based method for estimating the posture of a subject standing on a dynamic perturbation platform. The induced perturbation is used to study the control mechanisms as well as the balance requirements that regulate the upright standing. These perturbations are translated into different intensity levels of speed and acceleration along longitudinal and lateral directions of motion. In our method, the human posture is modeled by a tridimensional, three-segment inverted pendulum which simultaneously takes into account both the anterior–posterior and medio-lateral strategies of hip and ankle. Four tri-axial accelerometers are used her, one accelerometer is placed on the platform, and the other three are attached to a human subject. Based on the results, the joint angle estimated compare closely to measurements from magnetic encoders placed on an articulated arm joint. The results were also comparable to those found when using a high-end optical motion capture system coupled with advanced biomechanical simulation software. This paper presents the comparisons of our accelerometer-based method with encoder and optical marker based method of the estimated joint angles under different dynamics perturbations. [ABSTRACT FROM AUTHOR]

The paper presents the construction and application of multiple classifiers to increase the accuracy of the fault detection module in the diagnostic task. The structure of the ensemble of classifiers is presented and the applied voting mechanisms explained. Methods of storing knowledge in the intelligent diagnostic systems are introduced and their taxonomy provided. Next, the selected algorithms implemented in the fault detection operation are briefly described. Problems with the practical implementation of the proposed solution are considered. The scheme is used to detect faults in the analog part of the MEMS accelerometer. The paper is concluded with the possible prospects for the proposed scheme. [ABSTRACT FROM AUTHOR]

This paper addresses a practical problem: “Under what fractional coverage of buoyancy modules, would the vortex induced vibration (VIV) excitation on buoyant segments dominate the response?” The source of data is a recent model test on a 38 m long flexible cylinder, densely instrumented with fiber optic strain gauges and accelerometers. A pipe model with five staggered buoyancy configurations was tested. The paper emphasis is on exploring the winner of excitation competition between bare and buoyant segments and offering useful insights on the design of a pipe with staggered buoyancy modules in uniform flow. Four particular topics are covered: (i) the identification of VIV excitation regions, (ii) the experimental determination of the winner of excitation competition between bare and buoyant segments, (iii) the prediction of the winner of excitation competition, and (iv) the factors affecting fatigue damage rate for pipes with staggered buoyancy modules. Potential areas for promising additional research and a general guidance in designing staggered buoyancy pipes are proposed. [ABSTRACT FROM AUTHOR]

• We will monitor compliance with the intervention and adverse events. • Data will be analyzed following intention-to-treat principles. • A cost-effectiveness analysis will be performed. This a priori statistical analysis plan describes the methods of analysis for the Trial Of Prevention Strategies for low back pain (TOPS). TOPS aimed to investigate the effectiveness and cost-effectiveness of exercise and education classes compared with a minimal intervention control in preventing recurrence of low back pain (LBP) in people who have recently recovered from an episode of LBP. TOPS is a superiority, pragmatic, parallel-group randomized controlled trial with allocation concealment, blinded outcome assessors, and intention-to-treat analysis. Participants were randomized to a physical therapist-led exercise and education program for 12 weeks or minimal intervention. The primary outcome was days to recurrence of an episode of LBP. The three key secondary outcomes were days to recurrence of an episode of LBP resulting in (1) activity limitation, (2) care seeking for LBP; and (3) work absence of at least 1 day. Differences in survival curves for the primary (days to recurrence) and secondary outcome (days to LBP with activity limitation, days to care seeking due to LBP, and days to work absence due to LBP) will be compared using Cox regression. Hazard ratios (HRs) and median survival times with 95% confidence intervals (CI) will be calculated. The number of adverse events, including serious adverse events will be reported and the proportion of adverse events between groups will be compared using a Chi-squared test. This paper will provide a detailed description of the planned analyses for the TOPS trial. Australian New Zealand Clinical Trials Registry (ACTRN12615000939594). [ABSTRACT FROM AUTHOR]

Locomotion scoring is time consuming and is not commonly completed on farms. Farmers also underestimate their herds' lameness prevalence, a knowledge gap that impedes lameness management. Automation of lameness detection could address this knowledge gap and facilitate improved lameness management. The literature pertinent to adding lameness detection to accelerometers is reviewed in this paper. Options for lameness detection systems are examined including the choice of sensor, raw data collected, variables extracted, and statistical classification methods used. Two categories of variables derived from accelerometer-based systems are examined. These categories are behavior measures such as lying and measures of gait. For example, one measure of gait is the time a leg is swinging during a gait cycle. Some behavior-focused studies have reported accuracy levels of greater than 80%. Cow gait measures have been investigated to a lesser extent than behavior. However, classification accuracies as high as 91% using gait measures have been reported with hardware likely to be practical for commercial farms. The need for even higher accuracy and potential barriers to adoption are discussed. Significant progress is still required to realize a system with sufficient specificity and sensitivity. Lameness detection systems using 1 accelerometer per cow and a resolution lower than 100 Hz with gait measurement functions are suggested to balance cost and data requirements. However, gait measurement using accelerometers is rather underdeveloped. Therefore, a high priority should be given to the development of novel gait measures and testing their ability to differentiate lame from nonlame cows. [ABSTRACT FROM AUTHOR]

The active radio frequency identification (RFID) technique is used for in-situ measurement of acceleration and temperature in the distribution chain of iron ore pellets. The results of this paper are based on two experiments, in which active RFID transponders were released into train wagons or product bins. RFID exciters and readers were installed downstream in a harbour storage silo to retrieve data from the active transponders. Acceleration peaks and temperatures were recorded. The results imply that in-situ data can aid the understanding of induced stresses along the distribution chain to, for example, reduce pellet breakage and dusting. In-situ data can also increase understanding of product mixing behaviour and product residence times in silos. Better knowledge of stresses, product mixing and residence times are beneficial to process and product quality improvement, to better understand the transportation process, and to reduce environmental impacts due to dusting. Unlabelled Image [ABSTRACT FROM AUTHOR]

• An on-site WFT calibration is proposed by using an embedded tri-accelerometer. • The new error model is built for the on-site calibration. • A simple calibration protocol is conducted. • The calibration procedure is fast (less than 10 min) and accurate. • Dynamical tests on soft terrain demonstrates the practicability. Precise online calibration of Wheel force transducer/sensor (WFT) is difficult due to the safety risk of large-scale (kN and kN.m) mass attached on a rolling wheel, the impossibility of applying many loads at precise orientations of the transducer, and the difficulty to avoid the interference under the wheel rotation conditions. To tackle these problems, this paper presents an effective yet fast on-site WFT calibration by using an embedded triaxial accelerometer under the vehicle's gravitational loading conditions. A basic linear model of least-square calibration for the transducer is introduced, followed by the proposed new error model of online calibration. Experiments are performed by the gravitational load of vehicle itself, and compared with the wagon balance to verify the calibration protocol. The tested results demonstrate that the proposed technique provides an accurate, practical, and effective technique for on-line calibrating the WFTs. [ABSTRACT FROM AUTHOR]

This paper presents a novel two-dimensional (2D) vector accelerometer that utilizes cladding waveguides (CWGs) to couple a single-mode fiber (SMF) and a four-core fiber (FCF). By constructing the CWGs at the fusion point between an SMF and an FCF, the connection between the SMF core and the FCF cores is achieved. The symmetric CWGs are fabricated between the SMF and two orthogonal edge cores of an FCF using the slit beam shaping technique with a femtosecond laser. To detect bending and acceleration, two fiber Bragg gratings (FBGs) are inscribed in the FCF using the Plane-by-Plane (Pl-by-Pl) method. By monitoring the wavelength shifts of the FBGs on the two orthogonal edge cores, the required directional information can be measured simultaneously. The experimental results demonstrate a maximum acceleration sensitivity of 76.68 pm / g and 82.23 pm / g for the two orthogonal FBGs, respectively. • A 2D vector accelerometer that utilizes CWGs to couple an SMF and an FCF. • By connecting the cladding waveguides, the 2D vector accelerometer can be realized by FBGs wavelength division multiplexing. • This technique allows for the connection of various types of MCFs by making the CWGs in SMF, thereby eliminating the need for fan-in and fan-out. [ABSTRACT FROM AUTHOR]

Fixed-wing Micro-Aerial Vehicles (MAVs) need effective sensors that can rapidly detect turbulence induced motion perturbations. Current MAV attitude control systems rely on inertial sensors. These systems can be described as reactive; detecting the disturbance only after the aircraft has responded to the disturbing phenomena. In this part of the paper, the current state of the art in reactive attitude sensing for fixed-wing MAVs are reviewed. A scheme for classifying the range of existing and emerging sensing techniques is presented. The features and performance of the sensing approaches are discussed in the context of their application to MAV attitude control systems in turbulent environments. It is found that the use of single sensors is insufficient for MAV control in the presence of turbulence and that potential gains can be realised from multi-sensor systems. A successive paper to be published in this journal will investigate novel attitude sensors which have the potential to improve attitude control of MAVs in Turbulence. [ABSTRACT FROM AUTHOR]