Digital image stabilization system based on inertial measurement module

In order to stabilize the video module to build digital image stabilization image sequence, a method of using inertial measurement system is proposed. Through applying real-time attitude in- formation of the camera that obtained by high-precision attitude sensor to estimate the image motion vector and then to compensate for image, the purpose of stabilizing the image sequence can be a- chieved. Experiments demonstrate that this method has a high image stabilization precision, and the up to 16 frame/s video output rate completely meets the real-time requirements.

Improved spatio-temporal alignment measurement method for hull deformation

In this paper,an improved spatio-temporal alignment measurement method is presented to address the inertial matching measurement of hull deformation under the coexistence of time delay and large misalignment angle.Large misalignment angle and time delay often occur simultaneously and bring great challenges to the accurate measurement of hull deformation in space and time.The proposed method utilizes coarse alignment with large misalignment angle and time delay estimation of inertial measurement unit modeling to establish a brand-new spatiotemporal aligned hull deformation measurement model.In addition,two-step loop control is designed to ensure the accurate description of dynamic deformation angle and static deformation angle by the time-space alignment method of hull deformation.The experiments illustrate that the proposed method can effectively measure the hull deformation angle when time delay and large misalignment angle coexist.

Microminiature Inertial Measurement System and Its Applications

The microminiature inertial measurement system, a new style of inertial measurement system, has many advantages compared with traditional systems, such as small size, low mass, low cost, low power consumption, high bearing capacity, and long life. Undoubtedly, it will have wide applications in military and commercial fields. However, current micro inertial sensors do not have sufficient accuracy, so, its applications are limited to some extent. This paper describes a microminiature inertial measurement system and its design, operating theory and error control techniques. In addition, its performance and applications are evaluated.

Automated pavement horizontal curve measurement methods based on inertial measurement unit and 3D profiling data

Pavement horizontal curve is designed to serve as a transition between straight segments, and its presence may cause a series of driving-related safety issues to motorists and drivers. As is recognized that traditional methods for curve geometry investigation are time consuming, labor intensive, and inaccurate, this study attempts to develop a method that can automatically conduct horizontal curve identification and measurement at network level. The digital highway data vehicle （DHDV） was utilized for data collection, in which three Euler angles, driving speed, and acceleration of survey vehicle were measured with an inertial measurement unit （IMU）. The 3D profiling data used for cross slope calibration was obtained with PaveVision3D Ultra technology at 1 mm resolution. In this study, the curve identification was based on the variation of heading angle, and the curve radius was calculated with ki- nematic method, geometry method, and lateral acceleration method. In order to verify the accuracy of the three methods, the analysis of variance （ANOVA） test was applied by using the control variable of curve radius measured by field test. Based on the measured curve radius, a curve safety analysis model was used to predict the crash rates and safe driving speeds at horizontal curves. Finally, a case study on 4.35 km road segment demonstrated that the proposed method could efficiently conduct network level analysis.

Is clinically measured knee range of motion after total knee arthroplasty‘good enough?’:A feasibility study using wearable inertial measurement units to compare knee range of motion captured during physical therapy versus at home

Total knee arthroplasty is highly successful,in part due to range of motion(RoM)recovery.This is typically estimated goniometrically/visually by physical therapists(PTs)in the clinic,which is imprecise.Accordingly,a validated inertial measurement unit(IMU)method for capturing knee RoM was deployed assessing postoperative RoM both in and outside of the clinical setting.The study's objectives were to evaluate the feasibility of continuously capturing knee RoM pre-/post-op via IMUs,dividing data into PT/non-PT portions of each day,and comparing PT/non-PT metrics.We hypothesized IMU-based clinical knee RoM would differ from IMU-based knee RoM captured outside clinical settings.10 patients(3 M,69±13 years)completed informed consent documents following ethics board approval.A validated IMU method captured long duration(8–12 h/day,~50 days)knee RoM pre-/post-op.Post-op metrics were subdivided(PT versus non-PT).Clinical RoM and patient reported outcome measures were also captured.Compliance and clinical disruption were evaluated.ANOVA compared post-op PT and non-PT means and change scores.Maximum flexion during PT was less than outside PT.PT stance/swing RoM and activity level were greater than outside PT.No temporal variable differences were found PT versus non-PT.IMU RoM measurements capture richer information than clinical measures.Maximum PT flexion was likely less than non-PT due to the exercises completed(i.e.high passive RoM vs.low RoM gait).PT gait flexion likely exceed non-PT because of‘white coat effects’wherein patients are closely monitored clinically.This implies data captured clinically represents optimum performance whereas data captured non-clinically represents realistic performance.

Monocular Visual-Inertial and Robotic-Arm Calibration in a Unifying Framework

Reliable and accurate calibration for camera,inertial measurement unit(IMU)and robot is a critical prerequisite for visual-inertial based robot pose estimation and surrounding environment perception.However,traditional calibrations suffer inaccuracy and inconsistency.To address these problems,this paper proposes a monocular visual-inertial and robotic-arm calibration in a unifying framework.In our method,the spatial relationship is geometrically correlated between the sensing units and robotic arm.The decoupled estimations on rotation and translation could reduce the coupled errors during the optimization.Additionally,the robotic calibration moving trajectory has been designed in a spiral pattern that enables full excitations on 6 DOF motions repeatably and consistently.The calibration has been evaluated on our developed platform.In the experiments,the calibration achieves the accuracy with rotation and translation RMSEs less than 0.7°and 0.01 m,respectively.The comparisons with state-of-the-art results prove our calibration consistency,accuracy and effectiveness.

A Measure System of Zero Moment Point Using Wearable Inertial Sensors

Keeping balance is the premise of human walking. ZMP(zero moment point) is a point where total torque achieves balance. It is an important evaluation parameter of balance ability in walking, since it can be used to better reflect the dynamic balance during walking. ZMP can be used in many applications, such as medical rehabilitation, disease diagnosis, treatment and etc. In this paper, wearable inertial sensors system based on MEMS is used to measure ZMP(zero moment point) during walking, which is cheap, convenient, and free from the restriction of lab. Our wearable ZMP measurement system consists of inertial measurement subsystem and PC real-time monitoring station. Inertial measurement subsystem includes 9-axis inertial sensing nodes, the body communication network and the central node. Inertial sensing nodes are mounted on different parts of the body to collect body posture information in real-time, and then the best estimation of current posture are obtained by Kalman filter. The data from sensors is aggregated to the central node through the CAN bus, and then ZMP is calculated. Finally, it can be showed in the PC monitoring station. Experiments prove the system can achieve real-time ZMP detection during walking.

Implementation and Validation of a Stride Length Estimation Algorithm,Using a Single Basic Inertial Sensor on Healthy Subjects and Patients Suffering from Parkinson’s Disease

As low cost and highly portable sensors, inertial measurements units (IMU) have become increas-ingly used in gait analysis, embodying an efficient alternative to motion capture systems. Mean-while, being able to compute reliably accurate spatial gait parameters using few sensors remains a relatively complex problematic. Providing a clinical oriented solution, our study presents a gy-rometer and accelerometer based algorithm for stride length estimation. Compared to most of the numerous existing works where only an averaged stride length is computed from several IMU, or where the use of the magnetometer is incompatible with everyday use, our challenge here has been to extract each individual stride length in an easy-to-use algorithm requiring only one inertial sensor attached to the subject shank. Our results were validated on healthy subjects and patients suffering from Parkinson’s disease (PD). Estimated stride lengths were compared to GAITRite? walkway system data: the mean error over all the strides was less than 6% for healthy group and 10.3% for PD group. This method provides a reliable portable solution for monitoring the in-stantaneous stride length and opens the way to promising applications.

HUID:DBN-Based Fingerprint Localization and Tracking System with Hybrid UWB and IMU

High-precision localization technology is attracting widespread attention in harsh indoor environments.In this paper,we present a fingerprint localization and tracking system to estimate the locations of the tag based on a deep belief network(DBN).In this system,we propose using coefficients as fingerprints to combine the ultra-wideband(UWB)and inertial measurement unit(IMU)estimation linearly,termed as a HUID system.In particular,the fingerprints are trained by a DBN and estimated by a radial basis function(RBF).However,UWB-based estimation via a trilateral method is severely affected by the non-line-of-sight(NLoS)problem,which limits the localization precision.To tackle this problem,we adopt the random forest classifier to identify line-of-sight(LoS)and NLoS conditions.Then,we adopt the random forest regressor to mitigate ranging errors based on the identification results for improving UWB localization precision.The experimental results show that the mean square error(MSE)of the localization error for the proposed HUID system reduces by 12.96%,50.16%,and 64.92%compared with that of the existing extended Kalman filter(EKF),single UWB,and single IMU estimation methods,respectively.

Ultra-tight GPS/INS integration based long-range rocket projectile navigation method

Accurate navigation is important for long-range rocket projectile＇s precise striking. To obtain stable and high-per- formance navigation result, a ultra-tight global positioning system/inertial navigation system （GPS/INS） integration based nav- igation approach is proposed. The accurate short-time output of INS is used by GPS receiver to assist in acquisition of signal, and output information of INS and GPS is fused based on federated filter. Meanwhile, the improved cubature Kalman filter with strong tracking ability is chosen to serve as the local filter, and then the federated filter is enhanced based on vector sharing theory. Finally, simulation results show that the navigation accuracy with the proposed method is higher than that with traditional methods. It provides reference for long-range rocket projectile navigation.

Hybrid ToA and IMU indoor localization system by various algorithms

In this paper, we integrate inertial navigation system (INS) with wireless sensor network (WSN) to enhance the accuracy of indoor localization. Inertial measurement unit (IMU), the core of the INS, measures the accelerated and angular rotated speed of moving objects. Meanwhile, the ranges from the object to beacons, which are sensor nodes with known coordinates, are collected by time of arrival (ToA) approach. These messages are simultaneously collected and transmitted to the terminal. At the terminal, we set up the state transition models and observation models. According to them, several recursive Bayesian algorithms are applied to producing position estimations. As shown in the experiments, all of three algorithms do not require constant moving speed and perform better than standalone ToA system or standalone IMU system. And within them, two algorithms can be applied for the tracking on any path which is not restricted by the requirement that the trajectory between the positions at two consecutive time steps is a straight line.

A novel motion tracking system for evaluation of functional rehabilitation of the upper limbs

Upper limb function impairment is one of the most common sequelae of central nervous system in jury, especially in stroke patients and when spinal cord injury produces tetraplegia. Conventional assessment methods cannot provide objective evaluation of patient performance and the effec tiveness of therapies. The most common assessment tools are based on rating scales, which are inefficient when measuring small changes and can yield subjective bias. In this study, we designed an inertial sensorbased monitoring system composed of five sensors to measure and analyze the complex movements of the upper limbs, which are common in activities of daily living. We devel oped a kinematic model with nine degrees of freedom to analyze upper limb and head movements in three dimensions. This system was then validated using a commercial optoelectronic system. These findings suggest that an inertial sensorbased motion tracking system can be used in patients who have upper limb impairment through data integration with a virtual realitybased neurorehabili tation system.

THE EFFECT OF IMU INACCURACIES ON AIRBORNE SAR IMAGING

When using motion compensation approaches based on the measurement of motion sensors, the residual uncompensated motion errors due to measurement instrument inaccuracies contribute to phase errors and hence degrade Synthetic Aperture Radar (SAR) images. This paper presents a model to compute the phase error caused by Inertial Measurement Unit (IMU) measurement inaccuracies. By analyzing SAR motion compensation method and the effect of lever arm, this model derives the con-tribution of each term of IMU inaccuracies towards the residual uncompensated motion errors and provides a method to calculate each order of the residual phase error. According to the model, com-puted results of the airborne X-band SAR system with POS AV510 accord closely with the actual image quality.

Intelligently Tuned Wavelet Parameters for GPS/INS Error Estimation

This paper presents a new algorithm for de-noising global positioning system （GPS） and inertial navigation system （INS） data and estimates the INS error using wavelet multi-resolution analysis algorithm （WMRA）-based genetic algorithm （GA） with a well-designed structure appropriate for practical and real time implementations because of its very short training time and elevated accuracy. Different techniques have been implemented to de-noise and estimate the INS and GPS errors. Wavelet de-noising is one of the most exploited techniques that have been recently used to increase the precision and reliability of the integrated GPS/INS navigation system. To ameliorate the WMRA algorithm, GA was exploited to optimize the wavelet parameters so as to determine the best wavelet filter, thresholding selection rule （TSR）, and the optimum level of decomposition （LOD）. This results in increasing the robustness of the WMRA algorithm to estimate the INS error. The proposed intelligent technique has overcome the drawbacks of the tedious selection for WMRA algorithm parameters. Finally, the proposed method improved the stability and reliability of the estimated INS error using real field test data.

Numerical algorithm for rigid body position estimation using the quaternion approach

This paper deals with rigid body attitude estimation on the basis of the data obtained from an inertial measurement unit mounted on the body. The aim of this work is to present the numerical algorithm, which can be easily applied to the wide class of problems concerning rigid body positioning, arising in aerospace and marine engineering, or in increasingly popular robotic systems and unmanned aerial vehicles. Following the considerations of kinematics of rigid bodies, the relations between accelerations of different points of the body are given. A rotation matrix is formed using the quaternion approach to avoid singularities. We present numerical procedures for determination of the absolute accelerations of the center of mass and of an arbitrary point of the body expressed in the inertial reference frame, as well as its attitude. An application of the algorithm to the example of a heavy symmetrical gyroscope is presented, where input data for the numerical procedure are obtained from the solution of differential equations of motion, instead of using sensor measurements.

Adaptive filter for a miniature MEMS based attitude and heading reference system

An extended Kalman filter with adaptive gain was used to build a miniature attitude and heading reference system based on a stochastie model. The adaptive filter has six states with a time variable transition matrix. When the system is in the non-acceleration mode, the accelerometer measurements of the gravity and the compass measurements of the heading have observability and yield good eslimates of the states. When the system is in the high dynamic mode and the bias has converged to an aceurate estimate, the attitude caleulation will be maintained for a long interval of time. The adaptive filter tunes its gain automatically based on the system dynamics sensed by the accelerometers to yield optimal performance,

Technological advancements in the analysis of human motion and posture management through digital devices

Technological development of motion and posture analyses is rapidly progressing,especially in rehabilitation settings and sport biomechanics.Consequently,clear discrimination among different measurement systems is required to diversify their use as needed.This review aims to resume the currently used motion and posture analysis systems,clarify and suggest the appropriate approaches suitable for specific cases or contexts.The currently gold standard systems of motion analysis,widely used in clinical settings,present several limitations related to marker placement or long procedure time.Fully automated and markerless systems are overcoming these drawbacks for conducting biomechanical studies,especially outside laboratories.Similarly,new posture analysis techniques are emerging,often driven by the need for fast and non-invasive methods to obtain high-precision results.These new technologies have also become effective for children or adolescents with non-specific back pain and postural insufficiencies.The evolutions of these methods aim to standardize measurements and provide manageable tools in clinical practice for the early diagnosis of musculoskeletal pathologies and to monitor daily improvements of each patient.Herein,these devices and their uses are described,providing researchers,clinicians,orthopedics,physical therapists,and sports coaches an effective guide to use new technologies in their practice as instruments of diagnosis,therapy,and prevention.

MOTION COMPENSATION FOR HIGH RESOLUTION AIRBORNE SAR WITH IMU & GPS

Inertial measurement unit (IMU) is a standard motion sensor in modern airborne SAR systems. But how to remove its systematic error is a difficult problem, which impacts the improvement of resolution in azimuth. The technique of motion compensation presented in this paper, uses the GPS as a reference system to estimate and correct the systematic error of the IMU on the concept of linear unbiased minimum variance (LUMV). This new and effective method achieves very accurate position measurement (both high and low frequency) of the APC in not only short but also long terms, so that it can satisfy the requirement of high resolution airborne SAR. In the last section of the paper, some experimental simulations from raw data are given.

High Precision Fiber SINS with Spin Technology

Fiber strapdown inertial navigation system （FSINS） is presently used in several applications related to marine navigation. However, the absolute position from FSINS contains the error that increases with time, which prevents its long-term use for the ship cruise. In order to improve the performance of FSINS based on our present inertial sensors, the spin technology was proposed in the system to mitigate the navigation errors and a prototype of the proposed system was developed in Navigation Lab. The prototype contains the IMU, temperature controller, rotating configuration, navigation and I/O electronics group, control and display, power supply subsystem and other modules. In the proposed spin technology, the IMU is rotated back and forth in azimuth through four orthogonal positions relative to the ship’s longitudinal axis. Experimental testing was conducted for the prototype in the laboratory and the results showed that the RFSINS’s navigation performance is improved 10 times.

An indoor positioning system for mobile target tracking based on VLC and IMU fusion

An indoor positioning system( IPS) is designed to realize positioning and tracking of mobile targets,by taking advantages of both the visible light communication( VLC) and inertial measurement unit( IMU). The platform of the IPS is designed,which consists of the light-emitting diode( LED)based transmitter,the receiver and the positioning server. To reduce the impact caused by measurement errors,both inertial sensing data and the received signal strength( RSS) from the VLC are calibrated. Then,a practical propagation model is established to obtain the distance between the transmitter and the receiver from the RSS measurements. Furthermore,a hybrid positioning algorithm is proposed by using the adaptive Kalman filter( AKF) and the weighted least squares( WLS)trilateration to estimate the positions of the mobile targets.Experimental results show that the developed IPS using the proposed hybrid positioning algorithm can extend the localization area of VLC,mitigate the IMU drifts and improve the positioning accuracy of mobile targets.