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

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

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

Smartphones are an indispensable tool in modern day-to-day life. Their widespread use has spawned numerous applications targeting diverse domains, such as biomedical, environment sensing, and infrastructure monitoring. In such applications, the accuracy of the sensors at the core of the system is still questionable since these devices are not originally designed for high-accuracy sensing purposes. In this article, we investigate the accuracy limits of one of the commonly used sensors, namely, smartphone accelerometer. We focus on the efficacy of a smartphone as an acceleration measuring device, rather than focusing only on the accuracy of its internal accelerometer chip. This holistic approach includes additional errors that arise from the device operating system, such as sampling time uncertainty. Hence, we propose a novel smart device accelerometer error model that includes the traditional additive noise as well as sampling time uncertainty errors represented by a white Gaussian process. The model is validated experimentally using shake table experiments, and the maximum likelihood estimation (MLE) is used to estimate the sampling time uncertainty standard deviation. Moreover, we derive the Cramer–Rao lower bound (CRLB) of acceleration estimation based on the proposed model. [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]

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]

Heading the ball is a fundamental skill in soccer. However, recent studies have shown that players who headed the ball frequently were more vulnerable to concussion. To record head impacts and flag potential concussions, intraoral and head-mounted devices such as Vector MouthGuard and Biosystems xPatch have been developed. However, they are yet to gain wider acceptance among soccer players, perhaps because they are inconvenient and expensive. Since headers involve contact with the ball, we leverage this unique opportunity in soccer to explore using a smartball for monitoring headers. We develop a method to assess the impact of headers using the Adidas micoach soccer ball and compare its performance with that of the xPatch. We find that, while the micoach ball is somewhat limited in its sensing capabilities, there is enough promise that a similar ball with a more precise accelerometer can be an affordable and convenient alternative for monitoring header impacts in soccer. [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]

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]

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]

Indoor localization and tracking of moving human targets is a task of recognized importance and difficulty. In this paper, we describe a position measurement technique based on the fusion of various sensor data collected using a wearable embedded platform. Since the accumulated measurement uncertainty affecting inertial data (especially due to the on-board accelerometer) usually makes the measured position values drift away quickly, a heuristic approach is used to keep velocity estimation uncertainty in the order of a few percent. As a result, unlike other solutions proposed in the literature, localization accuracy is good when the wearable platform is worn at the waist. Unbounded uncertainty growth is prevented by injecting the position values collected at a very low rate from the nodes of an external fixed infrastructure (e.g., based on cameras) into an extended Kalman filter. If the adjustment rate is in the order of several seconds and if such corrections are performed only when the user is detected to be in movement, the infrastructure remains idle most of time with evident benefits in terms of scalability. In fact, multiple platforms could work simultaneously in the same environment without saturating the communication channels. [ABSTRACT FROM PUBLISHER]

This paper presents a novel visual-inertial integration system for human navigation in free-living environments, where the measurements from wearable inertial and monocular visual sensors are integrated. The preestimated orientation, obtained from magnet, angular rate, and gravity sensors, is used to estimate the translation based on the data from the visual and inertial sensors. This has a significant effect on the performance of the fusion sensing strategy and makes the fusion procedure much easier, because the gravitational acceleration can be correctly removed from the accelerometer measurements before the fusion procedure, where a linear Kalman filter is selected as the fusion estimator. Furthermore, the use of preestimated orientation can help to eliminate erroneous point matches based on the properties of the pure camera translation and thus the computational requirements can be significantly reduced compared with the RANdom SAmple Consensus algorithm. In addition, an adaptive-frame rate single camera is selected to not only avoid motion blur based on the angular velocity and acceleration after compensation, but also to make an effect called visual zero-velocity update for the static motion. Thus, it can recover a more accurate baseline and meanwhile reduce the computational requirements. In particular, an absolute scale factor, which is usually lost in monocular camera tracking, can be obtained by introducing it into the estimator. Simulation and experimental results are presented for different environments with different types of movement and the results from a Pioneer robot are used to demonstrate the accuracy of the proposed method. [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]

Stochastic errors characterize the performance of inertial sensors and indicate the potential improvements of the device. Accurate modeling of these errors can enhance the performance of inertial navigation system. The simplest and generally adopted method to model stochastic errors of inertial sensor is the normal nonoverlapped Allan variance, but its estimation accuracy decreases in long cluster time. The fully overlapping Allan variance improves the estimation accuracy in long cluster time very much, while the traditional total variance based on maximal-overlapping cluster samples further increases the estimation accuracy in long cluster time greatly. However, with these two methods, better estimation is achieved at the expense of much longer computation time. Besides, the computation burden for large dataset and many variance data points is extremely large, while the inertial sensors need large dataset with many variance data points to fully characterize their stochastic errors. This is because the correlation time for some stochastic error is very long (e.g., the correlation time is 3 h for rate random walk), and the Allan variance curve of some stochastic error is very complicated (e.g., sinusoidal noise). Consequently, the fully overlapping Allan variance and the traditional total variance are not suitable for inertial sensor stochastic error modeling. This paper proposes a not fully overlapping Allan variance which has similar estimation accuracy to fully overlapping Allan variance, and whose calculation time is greatly reduced relative to fully overlapping Allan variance. Then, the not fully overlapping concept is integrated to that of total variance to further improve the estimation accuracy in long cluster time with respect to Allan variance, and the calculation time of total variance is greatly reduced as well. This method enables high accuracy and high computational efficiency of Allan variance analysis at the same time, especially for large dataset and many variance data points analysis. Finally, the proposed methods are applied to 12-hour static data of gyroscopes and accelerometers from a position and orientation system, and their advantages are demonstrated. [ABSTRACT FROM PUBLISHER]

The Position and Orientation System is integrated measurement equipment for airborne remote sensing system. The calibration of gyro biases is an effective way to reduce azimuth misalignment and navigation error of strapdown inertial navigation system (SINS). According to the analytic-coarse-alignment principle, the sensitivities of Euler angles with respect to inertial sensor biases are analyzed uniquely. The errors of Earth rotation rate in body frame caused by initial misalignment are obtained. Based on the transformation model of gyro biases, a novel analytic calibration of gyro biases with arbitrary double position is proposed. The simulation and experiment results show that the method can accurately calibrate the gyro biases, improve the precision of initial alignment and inertial navigation for SINS, and verify the efficiency of the proposed method. [ABSTRACT FROM PUBLISHER]

Inexpensive inertial/magnetic measurement units can be found in numerous applications and are typically used to determine orientation. Due to the presence of nonidealities in measurement systems, the calibration of the sensor is thus needed to determine sensor parameters such as bias, misalignment, and gain/sensitivity. In this paper, an online automatic calibration method for a three-axial accelerometer is presented. Parameters are estimated using an unscented Kalman filter. The sensor is placed in a number of different orientations using a robotic arm. These orientations are calculated online from the parameter covariance matrix and represent estimated optimal sensor orientations for parameter estimation. Numerous simulations are run to evaluate the proposed calibration method, and a comparison is made with an offline least mean squares calibration method. The simulation results show that calibration with the proposed method results in higher accuracy of parameter estimation when using less than 100 iterations. The proposed calibration method is also applied to a real accelerometer using a low number of iterations. The results show only slight (less than 0.4%) changes in parameter values between different calibration runs. The proposed calibration method provides an accurate parameter estimation using a small number of iterations without the need for manually predefining orientations of the sensor, and the method can be used in combination with other offline calibration methods to achieve even higher accuracy. [ABSTRACT FROM PUBLISHER]

This paper proposes a Kalman filter-based attitude (i.e., roll and pitch) estimation algorithm using an inertial sensor composed of a triaxial accelerometer and a triaxial gyroscope. In particular, the proposed algorithm has been developed for accurate attitude estimation during dynamic conditions, in which external acceleration is present. Although external acceleration is the main source of the attitude estimation error and despite the need for its accurate estimation in many applications, this problem that can be critical for the attitude estimation has not been addressed explicitly in the literature. Accordingly, this paper addresses the combined estimation problem of the attitude and external acceleration. Experimental tests were conducted to verify the performance of the proposed algorithm in various dynamic condition settings and to provide further insight into the variations in the estimation accuracy. Furthermore, two different approaches for dealing with the estimation problem during dynamic conditions were compared, i.e., threshold-based switching approach versus acceleration model-based approach. Based on an external acceleration model, the proposed algorithm was capable of estimating accurate attitudes and external accelerations for short accelerated periods, showing its high effectiveness during short-term fast dynamic conditions. Contrariwise, when the testing condition involved prolonged high external accelerations, the proposed algorithm exhibited gradually increasing errors. However, as soon as the condition returned to static or quasi-static conditions, the algorithm was able to stabilize the estimation error, regaining its high estimation accuracy. [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]

In this paper, through relating the six-degree-of-freedom (DOF) acceleration of the motion of a rigid body to the outputs from n single-axis accelerometers mounted on the rigid body, the feasibility of sensing the six-DOF acceleration by multiple single-axis accelerometers is explored. On this basis, a six-DOF acceleration sensing method, which senses the six-DOF acceleration through accessing and decoupling the outputs from the six coplanar single-axis accelerometers fixed at the midpoints of six end-to-end edges of a cube by ensuring that the sensing axial direction of one of the six single-axis accelerometers is aligned along one of the cube edges and any three edges of the six end-to-end edges are not in one plane, is proposed. The corresponding sensing equation is established, and the sensing characteristics are analyzed according to the established sensing equation. The research results indicate that the proposed six-DOF acceleration sensing method, accompanying with the established sensing equation, can accurately sense and measure the six-DOF acceleration, the sensing error is determined by the sensing error of the angular acceleration, the sensing resolutions can be improved by enlarging the scale of the layout of the six coplanar single-axis accelerometers and/or utilizing six single-axis accelerometers with higher resolution, and the sensing ranges can be improved by decreasing the scale of the layout of the six coplanar single-axis accelerometers and/or utilizing six single-axis accelerometers with larger sensing range. [ABSTRACT FROM PUBLISHER]

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]

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]

This paper is concerned with orientation estimation using inertial and magnetic sensors. A quaternion-based indirect Kalman filter structure is used. The magnetic sensor output is only used for yaw angle estimation using two-step measurement updates. External acceleration is estimated from the residual of the filter and compensated by increasing the measurement noise covariance. Using the direction information of external information, the proposed method prevents unnecessarily increasing the measurement noise covariance corresponding to the accelerometer output, which is not affected by external acceleration. Through numerical examples, the proposed method is verified. [ABSTRACT FROM PUBLISHER]

A gearshift detector based on accelerometer readings is derived, and its performance is investigated with the aid of experimental data. The employed methodology contains a low-order parametric model of the acceleration performed by the vehicle in combination with the minimization of a fading-memory least-squares criterion for parameter estimation and tracking. It is shown that the resulting filtering equations converge so that filtered accelerometer outputs (i.e., instantaneous acceleration and its rate of change) are obtained by linear time-invariant filtering of the raw accelerometer data, where the tradeoff between noise suppression and tracking ability is entirely determined by a single user-chosen variable. Two detection variables are formed, and they are combined to produce the detector output. Evaluation on experimental data illustrates the potential of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents the measurement results of a micropower switched-capacitor front end that was designed for three-axis capacitive microaccelerometers. The designed front end can reduce the distorting effects of the electrostatic forces and can be used in single-ended and differential modes. The front end was realized with a 0.13-μm bipolar complimentary metal-oxide-semiconductor process. The silicon area of the front end is 0.30 mm². The measurements show that the functionality of the front end follows the theory in both modes Consuming 20 μA from a 1.8-V supply, it achieves noise densities of 424, 607, and 590 μg/√Hz in the x-, y-, and z-directions, respectively, when each mass is sampled at 1 kHz in the differential mode. [ABSTRACT FROM AUTHOR]

This paper introduces a method of using 3-D accelerometers, gyroscopes, and an autoreset algorithm for tracking the trunk movements and estimating the regional trunk postural changes in the sagittal and coronal planes. Three sensor modules of the newly designed sensing system were attached onto the back of the skin surface at the levels of T1/T2, T12, and S1. The angular measurements from the sensor modules were used to estimate the spinal curvature changes of nine healthy subjects during their trunk movements. The measurements of the sensing system were found to be highly correlated with that of a Vicon motion analysis system. The correlation coefficients were found to be greater than 0.994 for dynamic tilting measurements and greater than 0.776 for trunk postural measurements; and the RMS differences were less than 1.5° for dynamic calibration and less than or equal to 4.5° for the estimation of trunk postural changes. This method could be considered as a useful way of quantifying the trunk postural information. With further developments in data logging and feedback mechanism, it can become a portable posture tracking and monitoring system for training those with postural deviations even during their normal daily activities. [ABSTRACT FROM AUTHOR]

Accelerometers based on thermal convection use a tiny bubble of heated air and pairs of temperature sensors hermetically sealed inside the sensor package cavity. In this paper, we successfully design and fabricate a novel thermal-bubble-based micromachined accelerometer with the advantages of minimized solid thermal conductance and higher sensitivity. The proposed accelerometer consists of a microheater and two pairs of thermopiles floating over an etched cavity and is constructed by our proposed microlink structure. Two-dimensional acceleration detection is easily realized using the microlink structure, and it can be applied to the technology of inclinometers, anemometers, and flowmeters. The heater and thermopiles are connected by netlike microlink structures, which enhance the structure and greatly reduce the solid heat flow from the heater to the hot junctions of the thermopiles. The samples are fabricated by the TSMC 0.35-µm 2P4M CMOS process, which has been provided by the National Chip Implementation Center (CIC). Our design has proved to be applicable for commercial batch production with outstanding strong structures and uniform quality. We measure the output signal by inclining the sensor to evaluate the performance of this accelerometer. The best sensitivity of 22 µV/g was obtained from acceleration versus output voltage under several experimental conditions. [ABSTRACT FROM AUTHOR]

A methodology has been developed for indirect, noncontact, and dynamic sensing of angles for robotic applications. Two accelerometers are placed on the adjacent links close to the joint axis, and their outputs are processed to estimate the joint angle. In the proposed technique, the joint angles are obtained without integrating the accelerometer outputs. To ensure accuracy of accelerometer readings, two calibration procedures for the accelerometers are presented, which can easily be implemented in place. Both these methods solve a nonlinear least squares problem to adjust the offset parameters of the accelerometers. The accelerometer-based angle sensor is particularly suitable for the harsh working environment of heavy-duty manipulators, where conventional contact-type angle sensors cannot be deployed or problems are associated with their use. The performance of the new sensor is studied and compared with the performance of digital resolvers in two applications, involving the position control and dynamic payload measurement of a miniexcavator. The experimental results attest to the efficiency and accuracy of the new angle-sensing mechanism. [ABSTRACT FROM PUBLISHER]

In this paper, we describe a method to test and characterize accelerometers using an accurate position sensor and nonlinear Kalman filters. The method is designed to estimate parameters in nonlinear accelerometers and could be a simpler alternative to methods using centrifuges or vibrational testing. The method makes it possible to do real-time parameter estimation. In addition, the method can be integrated into an inertial tracking or navigation system with sufficiently accurate position measurements. The estimation filters have been implemented and successfully tested using Monte Carlo simulations. An experimental setup has been built, and its functionality has been verified. [ABSTRACT FROM AUTHOR]

This paper presents a method for real-time hand motion detection in two-dimensional (2-D) space. A new input device for kinetically challenged persons that uses this method is presented. The device consists of a solid-state accelerometer that senses 2-D motion, a microcontroller that samples the data in real time, and an embedded field-programmable gate array (FPGA) device that distinguishes the types of motion from programmable motion vocabularies. The system has a quadratic capability O(n²) in detecting motions, while the hardware used has a linear-complexity O(n). The motion-detection computational model is presented, along with experimental results. The system adaptation to individual requirements and the cost versus quality tradeoff can be addressed through reconfiguration. [ABSTRACT FROM AUTHOR]

In this paper, we combine inertial sensing and sensor network technology to create a pedestrian dead reckoning system. The core of the system is a lightweight sensor-and-wireless-embedded device called NavMote that is carried by a pedestrian. The NavMote gathers information about pedestrian motion from an integrated magnetic compass and accelerometers. When the Nay- Mote comes within range of a sensor network (composed of NetMotes), it downloads the compressed data to the network. The net- work relays the data via a RelayMote to an information center where the data are processed into an estimate of the pedestrian trajectory based on a dead reckoning algorithm. System details including the NavMote hardware/software, sensor network middleware services, and the dead reckoning algorithm are provided. In particular, simple but effective step detection and step length estimation methods are implemented in order to reduce computation, memory, and communication requirements on the Motes. Static and dynamic calibrations of the compass data are crucial to compensate the heading errors. The dead reckoning performance is further enhanced by wireless telemetry and map matching. Extensive testing results show that satisfactory tracking performance with relatively long operational time is achieved. The paper also serves as a brief survey on pedestrian navigation systems, sensors, and techniques. [ABSTRACT FROM AUTHOR]

A vision-based approach is evaluated for its applicability as a new sensing technology for measuring earthquake- induced motions. Traditional motion sensors used in laboratory and field experiments must be physically attached to the structure and require cumbersome cabling, configurations and substantial time for setup. Moreover, for reduced scale experiments, sensors generally add substantial mass or change the response characteristics of the system. The approach evaluated in this paper is advantageous since it requires very limited physical attachment to the structure of interest, is high-speed, high-resolution, and does not introduce additional mass or otherwise modify the properties of the structure. A demonstration experiment is described in which four digital high-speed, high-resolution, charge-coupled-device cameras outfitted with red light-emitting diodes are used to track 21 reflective (nearly) mass less spherical elements discretely mounted on a scale five-story steel frame structure. The structure is mounted on a Large bi-axial shake table and subjected to different earthquake motions. A total of eleven conventional (wired) transducers [linear variable displacement transducers and accelerometers] are also discretely mounted on the structure, providing a unique comparison with the vision-based approach. Results from these experiments show that the nonintrusive vision-based approach is extremely promising in terms of its ability to capture inter-story drift, floor level velocities, and accelerations, provided proper post-processing of the dynamic data occurs. [ABSTRACT FROM AUTHOR]