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

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

This paper presents a wireless sensor network system (WSNS) for effective, reliable, and efficient monitoring of landslides. The system incorporates a network of wireless inertial measurement unit (IMU) sensor devices for collecting movement data, a local base station for data gathering, a capture server for data processing and storage, and a warning system. The major contributions of this paper are: 1) two approaches for defining movement thresholds; 2) landslide classification concept based on IMU sensor data patterns and magnitudes; and 3) a conceptual framework for building an intelligent and reliable wireless monitoring and warning system. The IMU sensor data collected by three-axis accelerometer and three-axis gyroscope were utilized to define the movement thresholds and classify landslides based on specially designed laboratory experiments. The performances of the IMU sensors and the base station for data collection and communication were tested through a rock-fall experiment conducted in the field conditions. The WSN-IMU system is capable of monitoring all types of slope movements independent of the triggering factors. The unique ability of the WSN-IMU system to determine landslide types allows designers and authorized personnel to predict subsequent movement pattern and duration so as to implement appropriate risk management and control measures to alleviate the socioeconomic losses. This paper outcome serves as the foundation for future studies and technological advancements that will facilitate landslide stabilization or mitigation actions as well as to predict the intensity of damages associated with those landslides. [ABSTRACT FROM AUTHOR]

This paper presents an analysis of rigid-body jointangle measurement based on microelectromechanical-system (MEMS) biaxial accelerometers and uniaxial gyroscopes. In comparison to conventional magnetic and optical joint angular sensors, this new inertial sensing principle has the advantages of flexible installation and true contactless sensing. This paper focuses on the comparison of four different inertial-sensor combination methods that are reported in reference papers and utilizes the theory of rigid-body kinematics to explain and analyze their advantages and weaknesses. Experiments have also been conducted to further verify and strengthen the arguments put forward in the analysis. All experiments in this paper took place on a custom-built rigid-body robot arm model that can be manipulated by hand. Sensor calibration and accelerometer alignment issues are also described, and their details are discussed. The experiment results presented in this paper show significant differences with reference to the achieved angular accuracy for various situations when using the four different sensor combination methods. In some cases, the angular error based on one method is more than 0.04 rad, while that from another method is within ±0.005 rad. The noise levels of angular readings from different methods are also experimentally compared and analyzed. The conclusion drawn serves to guide readers toward a suitable method for their particular application. [ABSTRACT FROM AUTHOR]

This paper presents an innovative in-field coarse bias estimation (CBE) method for stationary intermediate-grade inertial measurement units (IMUs), which is based on the existence of normality and orthogonality errors in the attitude matrix supplied by the traditional three-axis attitude determination stationary coarse alignment (CA) stage. Differently from the currently existing in-field bias estimation formulations, the proposed CBE method is conceived to be conducted in stationary conditions, jointly with the CA, and it allows the down accelerometer bias and the north and down angular rate sensor biases to be adequately estimated. As a main contribution of this paper, we demonstrate that the proposed CBE method is able to greatly accelerate the estimation process of the aforementioned biases, contributing, hence, to shorten the IMU setup time. A comprehensive error analysis is presented, and the conclusions are twofold: 1) the proposed CBE method is valid regardless of the IMU orientation and 2) its estimation accuracy is mainly constrained by the north accelerometer bias and the position location uncertainty, which are, in general, very reduced. Results from simulated and experimental test confirm the adequacy of the outlined verifications, also attesting the superiority of the proposed CBE method in situations where, due to time constraints, stationary fine alignment procedures are not allowed to be carried out. [ABSTRACT FROM PUBLISHER]

Rockfalls have over the last decades become a serious and frequent hazard, especially due to larger variations in precipitation and temperatures, destabilizing rocky slopes in mountainous regions. Hence, civil engineers are applying the latest simulation tools to perform risk assessments and plan mitigation strategies. These tools are based on various models with many parameters that should be calibrated and evaluated with real-world in-field measurement data. In this paper, we present a rugged low-power multisensor node termed StoneNode that has been designed to acquire and log accurate inertial sensor measurements during induced in-field experiments with falling rocks. The node hosts low-power microelectromechanical system sensors with high dynamic ranges sampled up to 1 kHz, and provides a long battery lifetime of up to 56 h, enabling long-lasting field studies with a duration of several working days. Exhaustive in-field experiments have been carried out with several differently shaped rocks on typical terrain in the Swiss alpine region. The experiments comprise more than 100 induced tests with several heavy impacts of >400 g. This paper gives a detailed summary of these results, including unprecedented in situ data of rockfall trajectories and postexperimental validation where we compare simulated rockfall deposition distributions and motion traces with in-field measurements after calibration of the simulation module. Our results and experience gained in-field confirm that the StoneNode is a reliable easy-to-use device, which greatly facilitates the data acquisition process. Further, the results obtained with the calibrated simulation tool show good quantitative and qualitative congruence with the experiments, further reaffirming our methodological approach. [ABSTRACT FROM PUBLISHER]

On-line tilt angle detection and prognosis are the important prerequisites of real-time movement controlling. This paper develops a time-effective tilt angle prognosis methodology and its evaluation prototype. First, a complementary filter is imported to extend both the superiorities sufficiently of accelerometer and gyroscope, accurate tilt angle detection then can be achieved in a wide range of frequency. Second, an improved sparse least-squares support vector regression is proposed for tilt angle prognosis, its configurable singularity spectrum technology keeps away from the high-order matrix operation through eliminating nonprincipal components among raw vectors. Third, novel composite judgment criteria and its derived dynamic sliding window mechanism are proposed to pursue scientific balance of the prediction precision and computational cost. Fourth, double chains quantum genetic algorithm is introduced as the optimization tool to search optimal parameters of the above-mentioned models. Finally, the prototype setup and the corresponding experimental results are demonstrated and discussed in detail, which promise that our proposed methodology could be potentially applied to the actual motion control systems. [ABSTRACT FROM AUTHOR]

Motion table is a kind of test equipment, which is designed to provide precise angular and rate motions for the testing of measurement sensors and systems. To improve the application precision of the motion table, a new inclination error calibration method of the motion table is proposed in this paper. The error model of the motion table that considered the inclination error is derived, and ellipsoid fitting is employed to calibrate the inclination error of the motion table based on the output of accelerometer. To validate the effectiveness of the new calibration method, position and orientation system (POS) is used to test the motion table. Through establishment of the relationship between inclination error and output of the motion table, the inclination error of the motion table has been compensated in the test process of POS, so that the inclination error can be indicated by the output of POS on the motion table. According to the output of POS before and after inclination error compensation of the motion table, the feasibility of calibration method in this paper is verified, and the experiment results show that the proposed method can improve the application precision of the motion table greatly. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to design a torque compensation system with guaranteed performance that allows attitude control of the superconducting rotor, which is spinning at 4.2 K. The torque compensation system includes four pairs of torque-producing superconducting coils. The torques generated by the torque-producing coils are calculated using numerical analysis method. This paper presents an approach to design a torque compensation system that is used to erect the rotor prior to and during spinup, and then is also used to develop error compensation torques. [ABSTRACT FROM AUTHOR]

Natural disasters affect structural health of buildings, thus directly impacting public safety. Continuous structural monitoring can be achieved by deploying an Internet of things network of distributed sensors in buildings to capture floor movement. These sensors can be used to compute the displacements of each floor, which can then be employed to assess building damage after a seismic event. The peak relative floor displacement is computed, which is directly related to damage level according to the United States federal agencies standards. With this information, the building inventory can be classified into immediate occupancy, life safety, or collapse prevention categories. In this paper, we propose a zero velocity update technique to minimize displacement estimation error. Theoretical derivation and experimental validation are presented. In addition, we investigate modeling sensor error and interstory drift ratio distribution. Moreover, we discuss the impact of sensor error on the achieved building classification accuracy. [ABSTRACT FROM AUTHOR]

Complex and fatal road vehicle crashes are often a matter of dispute in forensic investigation. The proposed algorithms and methods, utilizing data from inertial measurement units and satellite navigation, enable road–vehicle trajectory reconstruction in 3-D suitable for postaccident forensic analysis. This paper provides an estimate of the reliability of this approach and its relation to component specifications. We start by describing an appropriate type of the strap-down inertial navigation system before providing a detailed performance analysis when the system is integrated with global navigation satellite systems. In addition, we present custom hardware and embedded software solutions, as well as experimental evaluation in real-world case study, capturing typical crash scenario performed at an approved safety performance assessment laboratory. In our experiments, strap-down inertial mechanization was used for short-term vehicle trajectory reconstruction using sensor readings and compared with ground-truth position captured on video. We have used reference equipment to validate the approach and the experiment resulted in a positioning accuracy on a decimeter level. [ABSTRACT FROM PUBLISHER]

This paper developed a measurement-while-drilling instrument based on predigested inertial measurement unit (PIMU) and presented a PIMU design with a rotational modulation device, thus improving precision of inertial sensors. Then, this paper proposed an integral surveying method based on inertial navigation system and dead reckoning, measuring motion parameters characterized by long-term high accuracy. The semi-physical simulation was conducted under the laboratory conditions. The results indicate that, the suggested methodology dramatically improves measuring accuracy of attitude angles (azimuth, pitch, and roll) and position. [ABSTRACT FROM PUBLISHER]

Accelerometers are used in a wide variety of applications including inertial navigation systems and activity monitors. In order to reduce systematic and random errors and improve accuracy and precision, this paper proposes a differential accelerometer system where two triaxial accelerometers are combined in opposite directions. The differential system allows to create additional constraints that can be used for improving calibration and estimation. Calibration methods based on nonlinear optimization are suggested to determine the offset and the scaling vectors. The differential system also permits to determine and update the offset and the scaling factors dynamically based on the system’s constraints. Sensor fusion is accomplished using a weighted least squares method. Experiments are carried out to evaluate and validate the system and the proposed methods including comparison with a single accelerometer system. It is shown that the differential system facilitates and improves acceleration estimation in terms of accuracy and precision. [ABSTRACT FROM AUTHOR]

This paper explores the notion and efficacy of harnessing the advantages of position sensitive detector (PSD) in the paradigm of noncontact vibration measurements. The study peruses an innovative foray into the domain of calibrated vibration measurement systems, wherein a first-of-its-kind strategy has been explored to nullify the self-vibration effect of the PSD platform by incorporating a micro-elctromechanical system digital accelerometer. The self-vibration compensated system has been designed and experimentally validated through NI-CompactRIO. The proposed system has been calibrated with a single axis Brüel&Kjær piezoelectric accelerometer. The system was observed to yield a maximum absolute error of 0.34 Hz in the measuring frequency range of 20–600 Hz. Furthermore, the system was observed to effectively measure displacement in the range of 2.32– $1300~\mu \text{m}$ with a maximum relative error of 2.7% across the span of gain settings varied at designated set of frequencies tested. Successive measurements yielded good repeatability characterized by a maximum standard deviation of $1 ~\mu \text{m}$. A commendable system linearity was also achieved for its intended range of measurement as characterized by a correlation coefficient of 99.8%. The accuracy and robustness of the noncontact PSD system coupled with its inherent simplicity of system integration establishes itself as a potent and cost-effective tool in the realm of vibration measurements of highly sensitive systems susceptible to microvibrations (<1 kHz) compared to its more expensive prevalent noncontact laser Doppler vibrometer counterparts. The developed system also exhibits pivotal significance in critical application areas where human involvement is not advisable. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a spectral interrogation scheme for the detection of small wavelength shift of fiber Bragg grating (FBG) accelerometer in a vibration test. Our findings indicate that the adopted upsampling procedure in the algorithm can effectively reconstruct small signals (wavelength shift < pixel resolution of diffraction grating-based interrogator) from low-resolution spectra that cannot be achieved by using other interrogation schemes. The reconstructed signals have good SNR and excellent coherence with the reference signal from a standard integrated electronic piezoelectric accelerometer in both time and frequency domains, as if they are extracted from the high-resolution spectra. The proposed method has opened a new possibility of employing low-cost FBG interrogation for accurate measurement and dynamic modal analysis in various industrial applications. [ABSTRACT FROM AUTHOR]

This paper presents the modeling and the comparison of two acquisition systems developed at the Test Centre Division, European Space and Technology Centre, of the European Space Agency in the frame of two investigations: 1) on microelectromechanical-systems-based sensors for the detection of very low-g accelerations and 2) on the feasibility of a compact and low-power multisensor acquisition platform for environmental tests of spacecrafts or subsystems. A Simulink-based approach is proposed for fast, accurate, and reconfigurable modeling of both systems, taking into consideration the sensors and the acquisition chain. This paper aims to highlight the importance of the models as key tools to predict the distortion and noise sources, to allow fast setup of the experiments, and to manage the signal conditioning process. The validity of the proposed technique, applied to the two acquisition systems, is assessed by comparing the predicted results with tests on the real system. Finally, this paper aims to prove, by comparing the two approaches (dedicated acquisition system and multisensor acquisition platform), the importance of the characterization of the whole acquisition system when high performances need to be achieved. [ABSTRACT FROM AUTHOR]

Inertial-navigation system (INS) and global position system (GPS) technologies have been widely applied in many positioning and navigation applications. INS determines the position and the attitude of a moving vehicle in real time by processing the measurements of three-axis gyroscopes and three-axis accelerometers mounted along three mutually orthogonal directions. GPS, on the other hand, provides the position and the velocity through the processing of the code and the carrier signals of at least four satellites. Each system has its own unique characteristics and limitations. Therefore, the integration of the two systems offers several advantages and overcomes each of their drawbacks. The integration of INS and GPS is usually implemented utilizing the Kalman filter, which represents one of the best solutions for INS/GPS integration. However, the Kalman filter performs adequately only under certain predefined dynamic models. Alternatively, this paper suggests an INS/GPS integration method based on artificial neural networks (ANN) to fuse uncompensated INS measurements and differential GPS (DGPS) measurements. The proposed method suggests two different architectures: the position update architecture (PUA) and the position and velocity PUA (PVUA). Both architectures were developed utilizing multilayer feed-forward neural networks with a conjugate gradient training algorithm. [ABSTRACT FROM AUTHOR]

This paper deals with an advanced approach for the monitoring of elders and people with neurological pathologies (e.g., Alzheimer). The system that adopts a multisensor approach is able to recognize critical events, such as falls or prolonged inactivity, to monitor the user posture, and to notify the alerts to caregivers. In particular, this paper focuses on smart algorithms developed for the activities of daily living (ADL) classification, which use the information provided by inertial sensors embedded in the user device. In particular, a novel multisensor data-fusion approach, combining data from an accelerometer and a gyroscope, is presented. Apart from alert management, the information provided by this system is useful to track the evolution of the user pathology, also during rehabilitation tasks. Results obtained during tests with users demonstrate suitable performances of the adopted paradigm, in terms of sensitivity and specificity, in performing falls and ADL classification tasks. The average value of the sensitivity index for the classes of falls and ADL considered through this paper is 0.81, while the average value of the specificity index is 0.98. [ABSTRACT FROM AUTHOR]

The ability to track pedestrians without any infrastructure support is required by numerous applications in the healthcare, augmented reality, and entertainment industries. In this paper, we present a simple self-contained pedestrian tracking method using a foot-mounted inertial and magnetic sensor module. Traditional methods normally incorporate double integration of the measured acceleration, but such methods are susceptible to the acceleration noise and integration drift. To avoid this issue, alternative approaches which make use of walking dynamics to aggregate individual stride have been explored. The key for stride aggregating is to accurately and reliably detect stride boundary and estimate the associated heading direction for each stride, but it is still not well solved yet due to sensor noise and external disturbance. In this paper, we propose to make use of the inertial sensor and magnetometer measurements for stride detection and heading direction determination. In our method, a simple and reliable stride detection method, which is resilient to random bouncing motions and sensor noise, is designed based on gyroscope and accelerometer measurements. Heading direction is then determined from the foot’s orientation which fuses all the three types of sensor information together. The proposed pedestrian tracking method has been evaluated using experiments, including both short distance walking with different patterns and long distance walking performed indoors and outdoors. The good experimental results have illustrated the effectiveness of the proposed pedestrian tracking method. [ABSTRACT FROM PUBLISHER]

In this paper, we present a novel method to determine the heel strike (HS) and toe-off (TO) during overground (OG) and ramp walking, including the transition. The method utilizes force myography (FMG) signals from thighs while subjects walked on OG and ramp. Five adult male subjects wore a wireless FMG data acquisition system, developed in-house using force resistive sensors and electronic components. A heuristic approach for subject-dependent and terrain-independent model was developed to determine HS and TO in a given gait cycle in steady state and transition. The average error in HS determination was 9.66 ± 8.29, 9.38 ± 9.35, and 13.94 ± 18.95 ms, while TO was determined with an average error of 16.99 ± 18.12, 13.35 ± 15.10, and 17.29 ± 21.92 ms for OG, ramp, and transition, respectively. The proposed system is less expensive, simple to develop, and friendly to wear. The reported errors are comparable to previously reported errors using pressure sensitive insole, gyroscope, accelerometers, and electromyography, which are much complex and expensive in comparison to proposed FMG-based system. Although the tests were conducted on healthy subjects, the system promises to be generalizable to amputee and other pathological gaits also. While the tests were conducted on young adults at self-selected speeds, the system also promises to be generalizable for a wide range of walking speeds across the population. [ABSTRACT FROM AUTHOR]

This paper describes an approach for characterizing the dynamic behavior of the vehicle/track interaction at railway crossings. In the approach, we integrate in situ axle box acceleration (ABA) measurements with roving-accelerometer hammer tests to evaluate the influence of train speed, train moving direction (facing and trailing directions), sensor position (leading and rear wheels of a bogie), and the natural response of track structure on ABA signals. The analysis of data from multiple sensors contributes to the following findings: the major frequency bands of the vertical ABA are related to the natural frequencies of the crossing; thus, these ABA frequency bands are not greatly affected by variations in train speed, moving direction, and sensor position. The vibration energy concentrated at the major ABA frequency bands increases at higher train speeds, along the facing moving direction and from the leading wheel. The crossing rails vibrate as a combination of bending and torsion rather than solely bending at the major ABA frequency bands, since the vibrations of the wing rails are not synchronized. These results help enhance our understanding of the vehicle/track interaction at crossings and can be used to improve the dynamic response-based system for monitoring the condition of crossings. [ABSTRACT FROM AUTHOR]

Sound and acceleration measurements are two classes of sensing modalities which frequently occur in surface material categorization. Their fusion problem is extremely important in many practical scenarios, since they provide different properties about materials. In this paper, we investigate the multimodal measurements fusion categorization problem exhibiting nontrivial challenges that there does not exist sample-to-sample pairing relation between sound and acceleration measurements. To this end, we design a dictionary learning model that can simultaneously learn the projection subspace and the latent common dictionary for the different measurements. Furthermore, an optimization algorithm is developed to effectively solve the common dictionary learning problem. Based on the obtained solutions, the fusion categorization algorithm can be easily developed. Finally, we perform experimental validations on the publicly available data set to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed an input interfacing scheme with differential output for integrated capacitive sensor applications. In the proposed scheme, the front-end interfacing is based on switched capacitor charge amplifier configuration using a fully differential operational transconductance amplifier and one differential capacitive sensor arrangement which provides differential output with minimum number of circuit components. In the back end, a sigma–delta ( $\Sigma \Delta $ ) modulator is integrated for modulated digital output. The signal conditioning circuit also includes gain programmability by selecting proper feedback capacitor and mismatch cancellation between the sensing capacitors through on-chip capacitors array. The complete application specific integrated circuit (ASIC) is designed and fabricated in United Microelectronics Corporation (UMC) 0.18-\mu \text{m} CMOS process technology. The fabricated ASIC is then integrated with a silicon-on-insulator microelectromechnical systems capacitive accelerometer to test and characterize the performance of the proposed scheme. The measured result shows that the sensitivity of the proposed circuit can be varied from 200 to 900 mV/g by changing the feedback capacitor. The integrated system is also tested with electrostatic actuation as well as with a vibrating shaker and the measurement results are presented. [ABSTRACT FROM PUBLISHER]

This paper presents an attitude estimation method from uncertain observations of inertial sensors, which is highly robust against different uncertainties. The proposed method of covariance inflated multiplicative extended Kalman filter (CI-MEKF) takes the advantage of non-singularity of covariance in MEKF as well as a novel covariance inflation (CI) approach to fuse inconsistent information. The proposed CI approach compensates the undesired effect of magnetic distortion and body acceleration (as inherent biases of magnetometer and accelerometer sensors data, respectively) on the estimated attitude. Moreover, the CI-MEKF can accurately estimate the gyro bias. A number of simulation scenarios are designed to compare the performance of the proposed method with the state of the art in attitude estimation. The results show the proposed method outperforms the state of the art in terms of estimation accuracy and robustness. Moreover, the proposed CI-MEKF method is shown to be significantly robust against different uncertainties, such as large body acceleration, magnetic distortion, and errors, in the initial condition of the attitude. [ABSTRACT FROM PUBLISHER]

In this paper, a walker movement monitoring system is proposed for a standard walker and a front-wheeled walker. The walking parameters (step length, step time, step speed, and total walking distance) are accurately estimated from the movement of walker to provide a statistical analysis of rehabilitation assessment. First, a movement classification algorithm is proposed to identify walking styles (continuous rolling, step-by-step rolling, two back tips lifting, and complete lifting). Based on this classification, the movement of a walker can be estimated using either two encoders (only for front-wheeled walker) or an inertial measurement unit. The accuracy of walking parameter estimation is verified through five subjects’ experiments. In 20-m straight walking, the walking distance root mean square error is 0.1–0.3 m depending on walking styles. [ABSTRACT FROM PUBLISHER]

This paper presents a novel calibration method for the microelectromechanical system (MEMS) gyros. Comparing with the traditional gyro calibration method that requires high precision equipment, the calibration scheme presented in this paper is based on the principle that the time derivative of a reference vector can be expressed as the cross product of the angular velocity and the reference vector itself. The proposed method can use vector observations instead of precise external references, and it is well suited for the in-field calibration of attitude and heading reference systems (AHRS). Moreover, the proposed method computes all the parameters in the sensor error model through linear optimization. Numerical simulations are performed to evaluate the effectiveness of the proposed method, and experiment on a custom-built AHRS shows that the proposed calibration method has the same accuracy as the traditional method, which uses a rate table for a low-end MEMS gyro. [ABSTRACT FROM PUBLISHER]

Error accumulation is one of the critical factors that limit the effective use of low-cost inertial navigation systems (INS) in tracking applications, especially during prolonged time intervals. This paper proposes an improved method to reduce the error accumulation by automatically resetting it in the INS-based systems. This method does not require any additional external sensors and reference systems and therefore offers various ad-vantages. Necessary calibration has been done to accurately evaluate the parameters used within the method. The perform-ance of this proposed resetting method has been experimentally evaluated by tracking the moving vehicles both in indoor and outdoor environments. Results of the experiments demonstrate that the proposed method can significantly reduce the INS errors and successfully recover the trajectories of the moving vehicles with an adequate accuracy. [ABSTRACT FROM PUBLISHER]

This paper presents the design of a wearable system for measurements of athlete’s performance in combat sports. The system provides objective measurements of athletes’ shots, posture, and movements, and of the effectiveness of their training. The proposed instrumentation is useful to overcome the limits of traditional training methods, which are characterized by a subjective evaluation of the training effectiveness by a coach. The measuring system consists of a distributed network of three battery-powered wireless-sensing node types, worn by the athletes, and one master node, which is in charge of signal acquisition and processing tasks. The master node elaborates training statistics and visualizes them, either in real time during a combat session, or off-line for posttraining analysis. The wearable measuring system has been tested through real combat training sessions of athletes with different weights, ages, and experiences, both male and female. Different from the state-of-art athletes’ biometric measurement machines, which are cumbersome and expensive, the proposed system is designed to ensure a low-cost and wearable implementation and to give easy-to-understand feedbacks during training, particularly to nonprofessional athletes. [ABSTRACT FROM PUBLISHER]

This paper investigates the autocalibration of microelectromechanical systems (MEMS) triaxial accelerometer (TA) based on experimental design (DoE). First, for a special 6-parameter second-degree model, a six-point experimental scheme is proposed, and its G-optimality has been proven based on optimal DoE. Then, a new linearization approach is introduced, by which the TA model for autocalibration can be simplified as the expected second-degree form so that the proposed optimal experimental scheme can be applied. To reliably estimate the model parameter, a convergence-guaranteed iterative algorithm is also proposed, which can significantly reduce the bias caused by linearization. Thereafter, the effectiveness and robustness of the proposed approach have been demonstrated by simulation. Finally, the proposed calibration method has been experimentally verified using two typical types of MEMS TA, and desired experimental results effectively demonstrate the efficiency and accuracy of the proposed calibration approach. [ABSTRACT FROM PUBLISHER]

The calibration, in particular about the installation errors of a star sensor, is a vital problem when improving the accuracy of stellar-inertial navigation system (INS). This paper proposed an all-parameter calibration method for a ground-based stellar-INS with 12-position rotations, using a Kalman filter that can simultaneously estimate bias, scale factor, misalignments of inertial measurement unit (IMU), and installation errors of star sensor. The difference between the star vector measured by the star sensor and the gold reference generated by the star simulator is used as an observation. On the basis of observability to all parameters, the accuracy is greatly enhanced through an iterative method. Better than previous separate calibration of INS and star sensor, the proposed method offers an advantage in that installation error is slightly influenced by IMU drift and device precision. The experimental results demonstrate that all estimated parameters have good stability and repeatability, with the maximum attitude error of integrated navigation less than 6^\prime \prime after compensation, compared with 20^\prime \prime using the traditional method. It is shown that the proposed calibration method can efficiently improve the navigation performance of stellar-INS, which has been extensively used in shipborne systems, military aircraft, and missile systems. [ABSTRACT FROM AUTHOR]

Accurately estimating the orientation of different human body segments using low cost inertial sensors is a key component in various activity-related and healthcare-related applications. Typically, the signals from a gyroscope and an accelerometer are fused inside a Kalman filter to determine the orientation. However, the accelerometer measurements are influenced by the linear accelerations of the body segments in addition to the gravitational acceleration that corrupts the orientation estimates. The conventional method to deal with linear acceleration is to model it as a first-order low-pass process and estimate it inside the Kalman filter. In this conventional method, important information from those sensor axes that do not experience linear accelerations is lost. In this paper, we modify the conventional approach to deal with the problem of linear acceleration more efficiently. The proposed approach estimates the direction of linear acceleration and assigns lower weights inside the Kalman filter to only those sensor axes that are experiencing acceleration, thus conserving important information from other axes measurements. The proposed method is compared with the conventional method using simulations and experimentation on a test subject performing daily routine tasks. The results indicate a significant performance improvement in orientation estimation. [ABSTRACT FROM AUTHOR]

The navigation performance of the rotational inertial navigation system (RINS) could be greatly improved by rotating the inertial measurement unit with gimbals, and self-calibration for error parameters could be achieved in RINS as well. However, accelerometer nonlinearity errors need to be considered and calibrated to further improve the navigation accuracy of RINS, especially in large dynamic applications. In this paper, a self-calibration method is proposed for accelerometer nonlinearity errors in triaxis RINS. Accelerometer nonlinearity errors and other errors are calibrated through optimal estimation with velocity and position error measurements. In order to guarantee that all errors are observable during calibration, some rotation scheme design principles are proposed, which are different from traditional observability analysis methods and could provide instructions for rotation scheme design directly. The effectiveness of the self-calibration method is proved by both simulation and experiment. The accelerometer nonlinearity errors could be accurately calibrated with the proposed method, while other error parameters reach higher calibration accuracy. Furthermore, experiment results from a long-term vehicle navigation show that velocity and position accuracy of the triaxis RINS have improved significantly after compensation with the self-calibration results, fully illustrating the significance of the proposed self-calibration method in improving the navigation performance of RINS. [ABSTRACT FROM AUTHOR]

This paper presents the design and implementation of a prototype of a stator-free revolutions-per-minute (RPM) sensor based on two microelectromechanical-system uniaxial accelerometers. This paper first introduces the operating principle of the stator-free RPM sensor. It then discusses the associated architecture and design issues of this new sensing method. It then describes the detail of the prototype sensor hardware and software design of the common-mode rejection method and its signal processing. Experiments using the prototype sensor have been also conducted to further verify and strengthen the arguments put forward in the previous discussion. All experiments in this paper took place on a lathe machine in a laboratory. Sensor calibration under a MATLAB environment is also described. Experimental results confirm the interesting property of this sensor, namely, that it provides higher precision at higher RPM. The conclusion summarizes the design considerations, the experimental results, and the motivation in relation to future works for this stator-free RPM sensing method. [ABSTRACT FROM PUBLISHER]

Condition-based maintenance techniques for industrial equipment and processes are described in this paper together with examples of their use and discussion of their benefits. These techniques are divided here into three categories. The first category uses signals from existing process sensors, such as resistance temperature detectors (RTDs), thermocouples, or pressure transmitters, to help verify the performance of the sensors and process-to-sensor interfaces and also to identify problems in the process. The second category depends on signals from test sensors (e.g., accelerometers) that are installed on plant equipment (e.g., rotating machinery) in order to measure such parameters as vibration amplitude. The vibration amplitude is then trended to identify the onset of degradation or failure. This second category also includes the use of wireless sensors to provide additional points for collection of data or allow plants to measure multiple parameters to cover not only vibration amplitude but also ambient temperature, pressure, humidity, etc. With each additional parameter that can be measured and correlated with equipment condition, the diagnostic capabilities of the category can increase exponentially. The first and second categories just mentioned are passive, which means that they do not involve any perturbation of the equipment or the process being monitored. In contrast, the third category is active. That is, the third category involves injecting a test signal into the equipment (sensors, cables, etc.) to measure its response and thereby diagnose its performance. For example, the response time of temperature sensors (RTDs and thermocouples) can be measured by the application of the step current signal to the sensor and analysis of the sensor response to the application of the step current. Cable anomalies can be located by a similar procedure referred to as the time domain reflectometry (TDR). This test involves a signal that is sent through the cable to the end device. Its reflection is then recorded and compared to a baseline to identify impedance changes along the cable and thereby identify and locate anomalies. Combined with measurement of cable inductance (L), capacitance (C), and loop resistance (R), or LCR testing, the TDR method can identify and locate anomalies along a cable, identify moisture in a cable or end device, and even reveal gross problems in the cable insulation material. There are also frequency domain reflectometry (FDR) methods, reverse TDR, trending of insulation resistance (IR) measurement, and other techniques which can be used in addition to or instead of TDR and LCR to provide a wide spectrum of tools for cable condition monitoring. The three categories of techniques described in this paper are the subject of current research and development projects conducted by the author and his colleagues at the AMS Corporation with funding from the U.S. Department of Energy (DOE) under the Small Business Innovation Research (SBIR) program. [ABSTRACT FROM AUTHOR]

In this paper, a personal micronavigation system that uses high-resolution gait-corrected inertial measurement units is presented. The goal of this paper is to develop a navigation system that uses secondary inertial variables, such as velocity, to enable long-term precise navigation in the absence of Global Positioning System (GPS) and beacon signals. In this scheme, measured zero-velocity duration from the ground reaction sensors is used to reset the accumulated integration errors from accelerometers and gyroscopes in position calculation. With the described system, an average position error of 4 m is achieved at the end of half-hour walks. [ABSTRACT FROM PUBLISHER]

This paper presents a new triaxial accelerometer calibration method using a mathematical model of six calibration parameters: three gain factors and three biases. The fundamental principle of the proposed calibration method is that the sum of the triaxial accelerometer outputs is equal to the gravity vector when the accelerometer is stationary. The proposed method requires the triaxial accelerometer to be placed in six different tilt angles to estimate the six calibration parameters. Since the mathematical model of the calibration parameters is nonlinear, an iterative method is used. The results are verified via simulations by comparing the estimated gain factors and biases with the true gain factors and biases. The simulation results confirm that the proposed method is applicable in extreme cases where the gain factor is 1000 V/(m/s²) and the bias is ±100 V, as well as the cases where the gain factor is 0.001 V/(m/s²) and the bias is 0 V. The proposed calibration method is also experimentally tested with two different triaxial accelerometers, and the results are validated using a mechanical inclinometer. The experimental results show that the proposed method can accurately estimate gain factors and biases even when the initial guesses are not close to the true values. In addition, the proposed method has a low computational cost because the calculation is simple, and the iterative method usually converges within three iteration steps. The error sources of the experiments are discussed in this paper. [ABSTRACT FROM AUTHOR]

The posture of a user is one of the contextual information that can be used for mobile applications and the treatment of idiopathic scoliosis. This paper describes a method for monitoring the posture of a user during operation of a mobile device in three activities such as sitting, standing, and walking. The user posture monitoring system (UPMS) proposed in this paper is based on two major technologies. The first involves a tilt-angle measurement algorithm (TAMA) using an accelerometer. Unlike most previous studies, it is based on a relative computation using the dot product from the time-series acceleration data. Because TAMA does not require a physical calibration by a user, it is more robust and accurate compared to other methods that rely on absolute computations. The second technology is an effective signal-processing method that eliminates the motion acceleration component of the accelerometer signal using a second-order Butterworth low-pass filter (SLPF). Because the posture of a user is only related to the gravity acceleration component, the motion acceleration components should be removed. The TAMA and UPMS are implemented on a personal digital assistant (PDA). They are evaluated to verify the possibility of application to a mobile device. Additionally, a posture-based intelligent control interface in context-aware computing that reacts to the posture of a PDA user is implemented on the PDA to complement the poor user interface (UI) of the mobile device, and its results are presented. [ABSTRACT FROM AUTHOR]

Techniques that could precisely monitor human motion are useful in applications such as rehabilitation, virtual reality, sports science, and surveillance. Most of the existing systems require wiring that restrains the natural movement. To overcome this limitation, a wearable wireless sensor network using accelerometers has been developed in this paper to determine the arm motion in the sagittal plane. The system provides unrestrained movements and improves its usability. The lightweight and compact size of the developed sensor node makes its attachment to the limb easy. Experimental results have shown that the system has good accuracy and response rate when compared with a goniometer. [ABSTRACT FROM AUTHOR]

This paper presents a new calibration method of the accelerometer tested on a precision centrifuge to further enhance the calibration accuracy of the high-order error model coefficients. To eliminate the influence of dynamic and static error sources of precision centrifuge on calibration accuracy, nine coordinate systems are established considering the errors of a centrifuge, which are propagated by homogeneous transformation, and the accurate specific forces acting on three axes of the tested accelerometer are obtained. Based on the error model and the accurately computed input specific forces of the accelerometer, the indicated outputs of the tested accelerometer are derived, and with the combinations of indicated outputs of six calibration position pairs, a calibration model of the accelerometer is established. By designing a test plan of 12 positions under three mounting modes and using the least-squares method, all the high-order error model coefficients of the tested accelerometer are calibrated accurately. In the calibration method, the dynamic errors of precision centrifuge are introduced into the observation vectors, and the static errors are introduced into the vectors to be identified; the influence of centrifuge errors is automatically eliminated. Error analysis implies that the calibration accuracy is improved greatly. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of robust fault detection (FD) for an integrated system of inertial navigation system and global position system (INS/GPS). Under the assumption of process disturbance and measurement noise being l2 -norm bounded, a state-space model with potential additive fault is first introduced to describe the error dynamics of the INS/GPS integrated system. For the purpose of the FD, an observer-based FD filter (FDF) is used as a residual generator and the design of the FDF is formulated so as to find an observer gain matrix and a postfilter in the framework of Hi/H_\infty optimization. By utilizing the techniques of innovation analysis and orthogonal projection, a solution to the extended Hi/H_\infty -FDF is given in terms of Riccati recursions. Furthermore, a strategy of residual evaluation is also considered, which concerns the selection of an evaluation function and a threshold. Finally, a flight experiment is implemented to show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a sensor fusion algorithm that integrates gyroscope and vision measurements using an adaptive complementary Kalman filter is proposed to estimate the attitude of a hybrid head tracker system. In order to make the filter more tolerant to vision measurement fault and more robust to system dynamics, an adaptive fading filter is implemented to the sensor fusion filter, and fuzzy logic is applied to adjust the fading factor, which adapts a Kalman gain of the sensor fusion filter. For recognizing the dynamic condition of the system and vision measurement fault, the normalized square error of attitude and the norm of gyroscope output with designed membership functions are used. The performance of the proposed algorithm is evaluated by simulations. It is confirmed that the proposed algorithm has better performance than the conventional algorithms in high-dynamic conditions and vision measurement fault case. [ABSTRACT FROM AUTHOR]

A gyroscope-free inertial measurement unit employs solely accelerometers to capture the motion of a body in the form of its linear and angular acceleration as well as its angular velocity. For that, multiple transducers are fixed at distinct locations of the body that together form an accelerometer array. To accurately estimate the motion, the poses of the sensors, i.e., their positions and orientations, must be known precisely. Unfortunately, these parameters are typically hard to assess. Current state-of-the-art calibration methods are able to reconstruct the geometrical sensor configuration based on a set of motion data and corresponding acceleration measurements. However, to impose a reference motion on the sensor array and to capture that motion with the necessary accuracy requires sophisticated laboratory equipment. In this paper, we present a method to estimate the transducer poses using only their own measurements without depending on reference motion data. It is based on an iterative graph optimization that considers both the sensor poses and the motion as target variables. Initially, this results in infinitely many solutions. We reduce the solutions to only one global optimum by explicitly modeling the used triple-axis accelerometers as sensor triads and furthermore taking the temporal dependence of the acceleration samples into account. We compare our method to the conventional calibration using reference data in terms of its estimation accuracy. Furthermore, we analyze the convergence properties of our method by evaluating its tolerance to initial pose deviations. For both, we use synthetic and experimental data recorded on a 3-D rotation table. [ABSTRACT FROM AUTHOR]

This paper presents a measurement system for measuring dynamic acceleration of infrastructure remotely using semipassive radio-frequency identification (RFID) tag. This measurement is critical to the vibration-based method for infrastructure health monitoring. Design considerations of accelerometer integrated ultrahigh-frequency RFID tag and dynamic acceleration measurements through an RFID wireless link are discussed. Measurement results of the system for a structural specimen have shown that it is capable of acquiring data which provides the information of natural frequency of the structural specimen. Moreover, the system can distinctively identify the state changes of the structural specimen by natural frequency shifts. These results are benchmarked against the results obtained with two commercial systems. It is shown that the standard deviation of the measurement of the natural frequency is ±0.01 Hz which is very close to the standard deviation of the commercial measurement systems. [ABSTRACT FROM AUTHOR]

In this paper, an inkjet-printed sensor in the mesoscale is presented with the aim to investigate its behavior as an accelerometer in the low-frequency domain (up to 20 Hz), which properly fits with the need of typical applications in the field of human and seismic monitoring. The accelerometer consists of a Polyethylene terephthalate membrane clamped by four spring legs to a fixed support. The sensing readout strategy is implemented through four strain gauges directly printed onto the flexible substrate. The advantages of the approach proposed are mainly related to the adopted low-cost direct printing technology, which allows for the realization of cheap and customizable devices. The sensor behavior has been deeply investigated. The device responsivity and resolution are 9.4 mV/g and 0.126 g, respectively, at 10 Hz, and 41.0 mV/g and 0.003 g, respectively, at 35 Hz. The performances obtained encourage the development of the all-inkjet-printed sensor proposed, especially taking into account its low cost and disposable features. [ABSTRACT FROM AUTHOR]

Real-time orientation estimation using low-cost inertial sensors is essential for all the applications where size and power consumption are critical constraints. Such applications include robotics, human motion analysis, and mobile devices. This paper presents a linear Kalman filter for magnetic angular rate and gravity sensors that processes angular rate, acceleration, and magnetic field data to obtain an estimation of the orientation in quaternion representation. Acceleration and magnetic field observations are preprocessed through a novel external algorithm, which computes the quaternion orientation as the composition of two algebraic quaternions. The decoupled nature of the two quaternions makes the roll and pitch components of the orientation immune to magnetic disturbances. The external algorithm reduces the complexity of the filter, making the measurement equations linear. Real-time implementation and the test results of the Kalman filter are presented and compared against a typical quaternion-based extended Kalman filter and a constant gain filter based on the gradient-descent algorithm. [ABSTRACT FROM AUTHOR]

This paper presents an analytic coarse transfer alignment method based on inertial measurement vector matching for the inertial navigation system (INS) on vertically launched missiles from the ground. The alignment problem is transformed into the estimation of the relative attitude between the master INS (MINS) and slave INS (SINS) by decomposing the attitude matrix of SINS. As inspired by the ideology of the traditional analytic alignment method, the measurements from the accelerometers and gyroscopes of MINS and SINS are utilized to construct matching vectors to estimate the relative attitude between them without any initial information. Furthermore, the sensitivity of Euler angles with respect to the inertial sensor errors is analyzed according to the presented coarse transfer alignment principle. An algebraic expression for alignment error estimation is given, and thus the real-time precision evaluation for the presented coarse alignment method is achieved. It allows the system to switch to fine alignment in a timely and accurate manner. The simulation and flight test demonstrate that, compared with the quaternion-optimization-based alignment method proposed by Kang et al., the presented method has a smaller calculation cost and satisfactory convergence. The corresponding precision evaluation algorithm can describe the trend of true alignment error accurately, and is good enough to judge the moment for switching to the fine alignment. [ABSTRACT FROM AUTHOR]

This paper introduces a novel method for simultaneous 3-D trajectory tracking and lower body motion capture (MoCap) under various dynamic activities such as walking and jumping. The proposed method uses wearable inertial sensors fused with an ultrawideband localization system using a cascaded Kalman filter-based sensor fusion algorithm, which consists of a separate orientation filter cascaded with a position/velocity filter. In addition, to further improve the joint angle tracking accuracy, anatomical constraints are applied to the knee joint. Currently, available self-contained inertial tracking methods are not only drift-prone over time but also their accuracy is degraded under fast movements with unstable ground contact states due to the lack of external references. However, our experimental results, which benchmark the system against a reference camera-based motion tracking system, show that the proposed fusion method can accurately capture the dynamic activities of a subject without drift. The results show that the proposed system can maintain similar accuracies between fast and slow motions in lower body MoCap and 3-D trajectory tracking. The obtained accuracies of the system for 3-D body localization and knee joint angle tracking for fast motions were less than 5 cm and 2.1°, respectively. [ABSTRACT FROM PUBLISHER]

This paper describes the design, development, and characterization of two wireless instrumented crutches for gait monitoring in order to provide clinicians quantitative parameters of upper limbs’ contributions during walking. These parameters could be used to teach orthopedic patients to correctly use these supports and minimize problems connected to their usage. These instrumented crutches allow monitoring axial forces and shear forces, tilt angles, and time of impact on the ground in real time. Each crutch is composed of three strain-gauge bridges for measuring axial and shear forces, a conditioning circuit with transmission module, a triaxial accelerometer, a power management circuit, two batteries, and a biofeedback. The data are wirelessly transmitted via Bluetooth without needing any further readout unit, from the crutches to a personal computer, where the data are processed and displayed by a program created in LabVIEW. Each instrumented crutch was tested to assess the response of the accelerometer and the three strain-gauge bridges using a setup designed ad hoc. The mean experimental standard deviation was about 42 mV for axial forces corresponding to about 8 N and about 35 mV for shear forces corresponding to about 4 N. Hysteresis, linearity, and drift were calculated, and the obtained accuracy was about 8–9 N for axial forces and 4–5 N for shear forces. Furthermore, the crutches were tested during a walking activity of ten healthy subjects along a straight path for several trials. These crutches were used for a common analysis usually reported in the literature for weight bearing evaluation. The subjects were monitored performing a nonweight bearing (NWB) and a partial weight bearing (PWB) during a three-point gait. The results showed a mean of 102% ± 16% for NWB tests and a mean of 19% ±% for 10% PWB tests; these values are in agreement with similar studies in the literature. The simplicity that includes only constitutive strain gauges and a separable circuit board allows the achievement of the objectives of simplicity, ease of use, and noninvasiveness. Therefore, these crutches could be used as a support tool for controlling the use of crutches during walking not only in hospitals but also at home. [ABSTRACT FROM PUBLISHER]

Ultralow-cost single-chip inertial measurement units (IMUs) combined into IMU arrays are opening up new possibilities for inertial sensing. However, to make these systems practical for researchers, a simple calibration procedure that aligns the sensitivity axes of the sensors in the array is needed. In this paper, we suggest a novel mechanical-rotation-rig-free calibration procedure based on blind system identification and a Platonic solid printable using a contemporary 3-D printer. The IMU array is placed inside the Platonic solid, and static measurements are taken with the solid subsequently placed on all sides. The recorded data are then used together with a maximum-likelihood-based approach to estimate the interIMU misalignment and the gain, bias, and sensitivity axis nonorthogonality of the accelerometers. The effectiveness of the method is demonstrated with calibration results from an in-house developed IMU array. MATLAB scripts for the parameter estimation and production files for the calibration device (solid) are provided. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a system that is capable of detecting the breath rate of an athlete. Furthermore, we investigate equivalences with the tidal volume over the inclination angle of the thorax to analyze correlations with the respiratory minute volume which is the product of the breath rate and the tidal volume. Inertial-based sensors such as gyroscopes and accelerometers are utilized to analyze thoracic extensions. To counteract interferences with body movements, we use two inertial sensors. One sensor is attached on the chest of an athlete, while the other is located on the back. Therefore, thoracic extensions can be determined differentially. To extract the required data out of two signals, the system has to be calibrated first so the axes of the sensors are aligned to each other. All calculations are done on a digital signal processor that is implemented on the system. [ABSTRACT FROM AUTHOR]