Inertial Measurement Unit Measurements Research Articles

Validation of Pelvis and Trunk Range of Motion as Assessed Using Inertial Measurement Units.

Trunk and pelvis range of motion (ROM) is essential to perform activities of daily living. The ROM may become limited with aging or with neuromusculoskeletal disorders. Inertial measurement units (IMU) with out-of-the box software solutions are increasingly being used to assess motion. We hypothesize that the accuracy (validity) and reliability (consistency) of the trunk and pelvis ROM during steady-state gait in normal individuals as measured using the Opal APDM 6 sensor IMU system and calculated using Mobility Lab version 4 software will be comparable to a gold-standard optoelectric motion capture system. Thirteen healthy young adults participated in the study. Trunk ROM, measured using the IMU was within 5-7 degrees of the motion capture system for all three planes and within 10 degrees for pelvis ROM. We also used a triad of markers mounted on the sternum and sacrum IMU for a head-to-head comparison of trunk and pelvis ROM. The IMU measurements were within 5-10 degrees of the triad. A greater variability of ROM measurements was seen for the pelvis in the transverse plane. IMUs and their custom software provide a valid and reliable measurement for trunk and pelvis ROM in normal individuals, and important considerations for future applications are discussed.

Research on a Low-Cost High-Precision Positioning System for Orchard Mowers

To regulate the energy flow in orchard ecosystems and maintain the environment, weeding has become a necessary measure for fruit farmers, and the use of automated mowers can help reduce labor costs and improve the economic efficiency of orchards. However, due to the complexity of the geographic and spatial environment of the orchard, in particular, the loose and undulating road surface, the interference of satellite signals by large trees, etc., which decreases the positioning accuracy and stability of the positioning system of the mower, and the high cost of the sensor also affect the popularization of intelligent mowers for these applications. To address the above problems, this paper constructs a positioning system through a low-cost global navigation satellite system (GNSS), inertial measurement unit (IMU), and odometry, and utilizes the Kalman filter algorithm based on the error state for a combined GNSS/IMU positioning so that the inertial navigation system can maintain a more accurate positioning when the GNSS signals are poor. Considering the side-slip and error accumulation problems of the odometry of the traction mower, the combined GNSS/IMU positioning information is used to optimize the odometry model and improve the navigation and positioning accuracy. To reduce the measurement error of the IMU and the problem of error accumulation, this paper utilizes the nonholonomic constraint (NHC) of a lawn mower to suppress the dispersion of IMU measurement errors and constructs periodic and nonperiodic zero-velocity updating (ZUPT) strategies in combination with the travel paths of lawn mower navigation operations in the region to update the IMU data to improve the positioning accuracy and stability of the positioning system. The experiments show that the average error of the constructed positioning system is controlled within 0.15 m, the maximum error is maintained at approximately 0.3 m, and the positioning system constructed by using low-cost sensors can achieve a positioning accuracy similar to that of the differential global navigation satellite system (DGNSS), which is beneficial for the promotion and application of intelligent mowers in orchards.

Estimation of STEADI performance using inertial measurement unit

IntroductionFalls present a significant public health concern in the United States as a primary cause of unintentional injury-related deaths among older adults. A fall risk assessment toolkit STEADI developed by the CDC has been shown to predict future falls. However, STEADI has issues with accurate evaluations due to the disagreement on cut-off scores in functional assessments and history-taking questionnaires. Wearable sensor technology offers a practical and quantifiable alternative for assessing an individual's movement performance in real-world environments. The use of Inertial Measurement Units (IMUs) offers considerable potential to enhance fall risk screening. PurposeThe primary aim of this study is to test the agreement of STEADI functional assessment performance measured by the IMUs in comparison to human-based measurements. Method27 participants (Age: 74.37 ± 7.21) performed STEADI, including the Four-Stage Balance Test (4SBT), Timed Up & Go Test (TUG), 30-second Chair Stand (30sCS) with IMU placed at the fifth lumbar vertebra which is the proxy location of whole-body Center of Mass. By adopting an equivalent test, the STEADI agreement was tested between the human rater and IMU measurements, giving α = 0.05. ResultBetween the results from evaluators and IMU, the difference in TUG is -0.23 s, and the difference in 30sCS is 0.37, which is equivalent to within 4 % and 8 % for TUG and 30sCS, respectively. The difference in single-leg stance during the 4SBT is 0.59 s; however, the calculated equivalence zone is larger (22.7 %). ConclusionThis study demonstrates the feasibility of using IMU sensors to enhance fall risk screening protocols based on the STEADI. Future refinement may still need to enable broader application and effective screening practices on a larger scale of the population.

Stationary Detection for Zero Velocity Update of IMU Based on the Vibrational FFT Feature of Land Vehicle

Stationary Detection for Zero Velocity Update of IMU Based on the Vibrational FFT Feature of Land Vehicle

VID-SLAM: Robust Pose Estimation with RGBD-Inertial Input for Indoor Robotic Localization

This study proposes a tightly coupled multi-sensor Simultaneous Localization and Mapping (SLAM) framework that integrates RGB-D and inertial measurements to achieve highly accurate 6 degree of freedom (6DOF) metric localization in a variety of environments. Through the consideration of geometric consistency, inertial measurement unit constraints, and visual re-projection errors, we present visual-inertial-depth odometry (called VIDO), an efficient state estimation back-end, to minimise the cascading losses of all factors. Existing visual-inertial odometers rely on visual feature-based constraints to eliminate the translational displacement and angular drift produced by Inertial Measurement Unit (IMU) noise. To mitigate these constraints, we introduce the iterative closest point error of adjacent frames and update the state vectors of observed frames through the minimisation of the estimation errors of all sensors. Moreover, the closed-loop module allows for further optimization of the global attitude map to correct the long-term drift. For experiments, we collect an RGBD-inertial data set for a comprehensive evaluation of VID-SLAM. The data set contains RGB-D image pairs, IMU measurements, and two types of ground truth data. The experimental results show that VID-SLAM achieves state-of-the-art positioning accuracy and outperforms mainstream vSLAM solutions, including ElasticFusion, ORB-SLAM2, and VINS-Mono.

Sensor location influences the associations between IMU and motion capture measurements of impact landing in healthy male and female runners at multiple running speeds

ABSTRACT This study investigated the relationships between inertial measurement unit (IMU) acceleration at multiple body locations and 3D motion capture impact landing measures in runners. Thirty healthy runners ran on an instrumented treadmill at five running speeds (9–17 km/h) during 3D motion capture. Axial and resultant acceleration were collected from IMUs at the distal and proximal tibia, distal femur and sacrum. Relationships between peak acceleration from each IMU location and patellofemoral joint (PFJ) peak force and loading rate, impact peak and instantaneous vertical loading rate (IVLR) were investigated using linear mixed models. Acceleration was positively related to IVLR at all lower limb locations (p < 0.01). Models predicted a 1.9–3.2 g peak acceleration change at the tibia and distal femur, corresponding with a 10% IVLR change. Impact peak was positively related to acceleration at the distal femur only (p < 0.01). PFJ peak force was positively related to acceleration at the distal (p = 0.03) and proximal tibia (p = 0.03). PFJ loading rate was positively related to the tibia and femur acceleration in males only (p < 0.01). These findings suggest multiple IMU lower limb locations are viable for measuring peak acceleration during running as a meaningful indicator of IVLR.

A NOVEL GPS FAULT DETECTION AND EXCLUSION ALGORITHM AIDED BY IMU AND VO DATA FOR VEHICLE INTEGRATED NAVIGATION IN URBAN ENVIRONMENTS

Abstract. High accuracy positioning is crucial for various applications that require accurate and reliable positioning data, such as autonomous vehicles (AVs), agriculture, and intelligent transportation. Global Positioning System (GPS) is widely used in integration with Inertial Measurement Unit (IMU) and Visual Odometry (VO) to implement an accurate and robust navigation system. However, the performance of the integrated system can be severely degraded in urban environments due to non-line-of-sight (NLOS) reception and multipath interference. To overcome these challenges, a novel Fault Detection and Exclusion (FDE) algorithm aided with IMU and VO measurements is proposed to identify and isolate the contaminated satellite signals. The algorithm utilizes instantaneous measurements from IMU and VO to predict the current vehicle position, enabling the estimation of pseudorange errors in GPS measurements. A FDE algorithm based on Hierarchical clustering is then developed to identify GPS signals with significant errors based on the predicted pseudorange error. An experimental field test was conducted using a land vehicle to evaluate the effectiveness of the proposed algorithm. The results show that the GPS/IMU/VO integrated navigation system with the proposed FDE algorithm has significantly improved the positioning accuracy and reliability compared to the traditional system. The proposed algorithm achieves a positioning accuracy with a 3D Root Mean Square Error (RMSE) of 11.18m in urban environment, making an improvement of 68.2% over the traditional GPS/IMU/VO integrated navigation system.

Validity of Inertial Measurement Units to Measure Lower-Limb Kinematics and Pelvic Orientation at Submaximal and Maximal Effort Running Speeds

Inertial measurement units (IMUs) have been validated for measuring sagittal plane lower-limb kinematics during moderate-speed running, but their accuracy at maximal speeds remains less understood. This study aimed to assess IMU measurement accuracy during high-speed running and maximal effort sprinting on a curved non-motorized treadmill using discrete (Bland–Altman analysis) and continuous (root mean square error [RMSE], normalised RMSE, Pearson correlation, and statistical parametric mapping analysis [SPM]) metrics. The hip, knee, and ankle flexions and the pelvic orientation (tilt, obliquity, and rotation) were captured concurrently from both IMU and optical motion capture systems, as 20 participants ran steadily at 70%, 80%, 90%, and 100% of their maximal effort sprinting speed (5.36 ± 0.55, 6.02 ± 0.60, 6.66 ± 0.71, and 7.09 ± 0.73 m/s, respectively). Bland–Altman analysis indicated a systematic bias within ±1° for the peak pelvic tilt, rotation, and lower-limb kinematics and −3.3° to −4.1° for the pelvic obliquity. The SPM analysis demonstrated a good agreement in the hip and knee flexion angles for most phases of the stride cycle, albeit with significant differences noted around the ipsilateral toe-off. The RMSE ranged from 4.3° (pelvic obliquity at 70% speed) to 7.8° (hip flexion at 100% speed). Correlation coefficients ranged from 0.44 (pelvic tilt at 90%) to 0.99 (hip and knee flexions at all speeds). Running speed minimally but significantly affected the RMSE for the hip and ankle flexions. The present IMU system is effective for measuring lower-limb kinematics during sprinting, but the pelvic orientation estimation was less accurate.

Enable Fully Customized Assistance: A Novel IMU-Based Motor Intent Decoding Scheme

Assisting human locomotion is essentially related to the assistive force profile, which can be determined from four aspects: timing, magnitude, shape and duration. Most current methods of decoding human motor intent enable the customized determination of the assistive force profile by providing information of different subsets of the four aspects. Trustworthy human-exoskeleton interaction essentially relates to determining the assistive force profile. Current methods of decoding human motor intent enable the customized determination of the assistive force profile by providing limited information of human kinetics. In this paper, we propose and validate a novel motor intent decoding scheme that can enable a fully customized assistive force profile, where only inertial measurement units (IMUs) are used. First, we improve the robustness of the IMU-based kinematic estimation by sampling IMU measurements that well meet the hinge-joint assumption, and by online calibrating axes’ direction in order to avoid the post-hoc analysis of joint axes’ directions during the determination of the body-fixed coordinate frame. Second, using the calculated kinematics as input, we develop a computationally efficient dynamic model, through which kinetics of users can be calculated in real-time. Finally, we leverage a cable-driven ankle exoskeleton method to validate the assistive performance of our motor intent decoding scheme. We perform experiments on ten healthy subjects to evaluate the accuracy of our algorithm, and the change of metabolic rate and muscle efforts under the exoskeleton’s assistance. The results show the improvement from determining the assistive force profile by nominal curves and the feasibility of our algorithm.

Rotating 3D laser mapping system for multi-rotor drones

Accurate positional estimation is an essential prerequisite for the regular operation of an autonomous rotary-wing Unmanned Aerial Vehicles (UAV). However, the field of view (FOV) limitation problem of lidar makes it more challenging to locate the rotary-wing UAV in an unknown environment. To address rotor drones with an insufficient FOV and the observation blindness of lidar in complex environments, this paper designs a rotorcraft UAV system based on rotating 3D lidar and proposes a simultaneous localization and mapping algorithm for rotating 3D lidar. The algorithm distinguishes between planar and edge features based on the curvature value of the point cloud first. Then, to reduce the impact caused by the UAV motion and lidar rotation, messages about the Inertial Measurement Unit (IMU) and real-time rotation angles are used to compensate for these motions twice, while the IMU measurements are used for state prediction, and the error-sate iterative extended Kalman filter is used to update the residuals after matching line and surface features with sub-map. Finally, Smoother high-rate odometer data was obtained through IMU pre-integration and a first-order low-pass filter. The experimental results show that the proposed rotating lidar unit in indoor and outdoor conditions makes the rotorcraft UAV have a larger FOV, which not only improves the environmental perception capability and positional estimation accuracy of the rotorcraft but enhances the positioning reliability and flight stability of the rotorcraft UAV in complex environments.

Research on Indoor Fusion Positioning Based on EKF for Uwb_Imu

The study aims to improve the accuracy and stability of indoor positioning by integrating information from two types of sensors: UWB (Ultra-Wideband) and IMU (Inertial Measurement Unit) calibrated to eliminate system errors. The fusion algorithm utilizes Extended Kalman Filter (EKF) to combine UWB data and IMU measurements. First, the IMU undergoes self-calibration to correct system errors. Then, UWB ranging technology is used to estimate the target's position, and the information from the IMU's gyroscope and accelerometer is used to update the kinematic model. Finally, the EKF filters and predicts measurement errors and system noise to obtain accurate target position estimation. Experimental results demonstrate that this fusion method can enhance the precision and robustness of indoor static target positioning. The research findings are highly applicable in areas such as indoor positioning and intelligent surveillance.

Activity classification using unsupervised domain transfer from body worn sensors

Activity classification has become a vital feature of wearable health tracking devices. As innovation in this field grows, wearable devices worn on different parts of the body are emerging. To perform activity classification on a new body location, labeled data corresponding to the new locations are generally required, but this is expensive to acquire. In this work, we present an innovative method to leverage an existing activity classifier, trained on Inertial Measurement Unit (IMU) data from a reference body location (the source domain), in order to perform activity classification on a new body location (the target domain) in an unsupervised way, i.e. without the need for classification labels at the new location. Specifically, given an IMU embedding model trained to perform activity classification at the source domain, we train an embedding model to perform activity classification at the target domain by replicating the embeddings at the source domain. This is achieved using simultaneous IMU measurements at the source and target domains. The replicated embeddings at the target domain are used by a classification model that has previously been trained on the source domain to perform activity classification at the target domain. We have evaluated the proposed methods on three activity classification datasets PAMAP2, MHealth, and Opportunity, yielding high F1 scores of 67.19%, 70.40% and 68.34%, respectively when the source domain is the wrist and the target domain is the torso.

Inertial Measurement Unit Sensor-to-Segment Calibration Comparison for Sport-Specific Motion Analysis.

Wearable inertial measurement units (IMUs) can be utilized as an alternative to optical motion capture as a method of measuring joint angles. These sensors require functional calibration prior to data collection, known as sensor-to-segment calibration. This study aims to evaluate previously described sensor-to-segment calibration methods to measure joint angle range of motion (ROM) during highly dynamic sports-related movements. Seven calibration methods were selected to compare lower extremity ROM measured using IMUs to an optical motion capture system. The accuracy of ROM measurements for each calibration method varied across joints and sport-specific tasks, with absolute mean differences between IMU measurement and motion capture measurement ranging from <0.1° to 24.1°. Fewer significant differences were observed at the pelvis than at the hip, knee, or ankle across all tasks. For each task, one or more calibration movements demonstrated non-significant differences in ROM for at least nine out of the twelve ROM variables. These results suggest that IMUs may be a viable alternative to optical motion capture for sport-specific lower-extremity ROM measurement, although the sensor-to-segment calibration methods used should be selected based on the specific tasks and variables of interest for a given application.

RNGC-VIWO: Robust Neural Gyroscope Calibration Aided Visual-Inertial-Wheel Odometry for Autonomous Vehicle

Accurate and robust localization using multi-modal sensors is crucial for autonomous driving applications. Although wheel encoder measurements can provide additional velocity information for visual-inertial odometry (VIO), the existing visual-inertial-wheel odometry (VIWO) still cannot avoid long-term drift caused by the low-precision attitude acquired by the gyroscope of a low-cost inertial measurement unit (IMU), especially in visually restricted scenes where the visual information cannot accurately correct for the IMU bias. In this work, leveraging the powerful data processing capability of deep learning, we propose a novel tightly coupled monocular visual-inertial-wheel odometry with neural gyroscope calibration (NGC) to obtain accurate, robust, and long-term localization for autonomous vehicles. First, to cure the drift of the gyroscope, we design a robust neural gyroscope calibration network for low-cost IMU gyroscope measurements (called NGC-Net). Following a carefully deduced mathematical calibration model, NGC-Net leverages the temporal convolutional network to extract different scale features from raw IMU measurements in the past and regress the gyroscope corrections to output the de-noised gyroscope. A series of experiments on public datasets show that our NGC-Net has better performance on gyroscope de-noising than learning methods and competes with state-of-the-art VIO methods. Moreover, based on the more accurate de-noised gyroscope, an effective strategy for combining the advantages of VIWO and NGC-Net outputs is proposed in a tightly coupled framework, which significantly improves the accuracy of the state-of-the-art VIO/VIWO methods. In long-term and large-scale urban environments, our RNGC-VIWO tracking system performs robustly, and experimental results demonstrate the superiority of our method in terms of robustness and accuracy.

Assessing Spatiotemporal and Quality Alterations in Paretic Upper Limb Movements after Stroke in Routine Care: Proposal and Validation of a Protocol Using IMUs versus MoCap.

Accurate assessment of upper-limb movement alterations is a key component of post-stroke follow-up. Motion capture (MoCap) is the gold standard for assessment even in clinical conditions, but it requires a laboratory setting with a relatively complex implementation. Alternatively, inertial measurement units (IMUs) are the subject of growing interest, but their accuracy remains to be challenged. This study aims to assess the minimal detectable change (MDC) between spatiotemporal and quality variables obtained from these IMUs and MoCap, based on a specific protocol of IMU calibration and measurement and on data processing using the dead reckoning method. We also studied the influence of each data processing step on the level of between-system MDC. Fifteen post-stroke hemiparetic subjects performed reach or grasp tasks. The MDC for the movement time, index of curvature, smoothness (studied through the number of submovements), and trunk contribution was equal to 10.83%, 3.62%, 39.62%, and 25.11%, respectively. All calibration and data processing steps played a significant role in increasing the agreement. The between-system MDC values were found to be lower or comparable to the between-session MDC values obtained with MoCap, meaning that our results provide strong evidence that using IMUs with the proposed calibration and processing steps can successfully and accurately assess upper-limb movement alterations after stroke in clinical routine care conditions.

Enhancing Data Discretization for Smoother Drone Input Using GAN-Based IMU Data Augmentation

This study investigates the use of generative adversarial network (GAN)-based data augmentation to enhance data discretization for smoother drone input. The goal is to improve unmanned aerial vehicles’ (UAVs) performance and maneuverability by incorporating synthetic inertial measurement unit (IMU) data. The GAN model is employed to generate synthetic IMU data that closely resemble real-world IMU measurements. The methodology involves training the GAN model using a dataset of real IMU data and then using the trained model to generate synthetic IMU data. The generated synthetic data are then combined with the real data for data discretization. The resulting improved data discretization is evaluated using statistical metrics and a similarity evaluation. The improved data discretization demonstrates enhanced drone performance in terms of flight stability, control accuracy, and smoothness of movements when compared to standard data discretization methods. These results highlight the potential of GAN-based data augmentation for enhancing data discretization and improving drone performance. The proposition of improved data discretization offers a tangible benefit for the successful integration of Advanced Air Mobility (AAM) systems. Enhancing the accuracy and reliability of data acquisition and processing in UAS makes UAS operations safer and more reliable. This improvement is crucial for achieving the goal of automated and autonomous operations in diverse settlement environments, encompassing multiple mobility modes such as ground and air transportation.

An Improved UWB/IMU Tightly Coupled Positioning Algorithm Study.

The combination of ultra-wide band (UWB) and inertial measurement unit (IMU) positioning is subject to random errors and non-line-of-sight errors, and in this paper, an improved positioning strategy is proposed to address this problem. The Kalman filter (KF) is used to pre-process the original UWB measurements, suppressing the effect of range mutation values of UWB on combined positioning, and the extended Kalman filter (EKF) is used to fuse the UWB measurements with the IMU measurements, with the difference between the two measurements used as the measurement information. The non-line-of-sight (NLOS) measurement information is also used. The optimal estimate is obtained by adjusting the system measurement noise covariance matrix in real time, according to the judgment result, and suppressing the interference of non-line-of-sight factors. The optimal estimate of the current state is fed back to the UWB range value in the next state, and the range value is dynamically adjusted after one-dimensional filtering pre-processing. Compared with conventional tightly coupled positioning, the positioning accuracy of the method in this paper is improved by 46.15% in the field experimental positioning results.

Integrated beam training and environmental sensing in the mmWave prototype system

Integrated sensing and communication (ISAC) promises to be one of the major technologies in the future mobile communications. However, the lack of co-designed hardware systems and algorithms that combine both functions poses a challenge for further study of ISAC. In this case, firstly, we apply the inertial measurement unit (IMU) to access auxiliary motion information of user equipment (UE). Second, we design a 64-antenna millimeter-wave communication prototype system and estimate localization of UE and environmental features after beam measurements. Finally, the belief propagation simultaneous localization and mapping (BP SLAM) algorithm is utilized to combine IMU estimation with beam measurements to improve localization accuracy of UE and environmental features. The experiment results show that BP SLAM fused with IMU measurements can locate UE within 1 m error and virtual anchor within 2 m error in average, which increases the UE localization and environment sensing accuracy by ten times approximately.

Validity of IMU measurements on running kinematics in non-rearfoot strike runners across different speeds

ABSTRACT This study aims to determine the validity of the lower extremity joint kinematics measured by inertial measurement units (IMUs) in non-rearfoot strike pattern (NRFS) runners across different speeds. Fifteen NRFS runners completed three 2-min running tests on a treadmill in random order at 8, 10 and 12 km/h, whilst data were synchronously collected using the IMU system and an optical motion capture system. Before the offset was corrected, the validity of the knee angle waveform was higher than that of the hip and ankle; after the offset was corrected, the validity increased in all three joints. The correlation between the touchdown angles in the sagittal plane measured by the two systems was relatively high after the offset was corrected. The running speed influenced the offset-corrected measurements, with higher error values at higher speeds. The IMU system was able to provide measurements of running kinematics in the sagittal plane of NRFS runners at different running speeds but was unable to reliably measure motion in the frontal and horizontal planes. Future research should analyse the 3D gait of NRFS runners under a larger range of speed conditions to provide evidentiary support for the use of IMUs in running analysis outside the laboratory.

Inertial preintegration for VI‐SLAM by the screw motion theory

AbstractMost smoothing‐based visual‐inertial simultaneous localization and mapping algorithms (VI‐SLAM) rely on the Lie algebra processing of the inertial measurements. This approach is limited in its decoupled update of the attitude by using SO3 and velocity increments by SE3. In addition to limitations on only point transformation between frames. We present a novel approach to handling inertial measurement unit (IMU) measurements between two camera frames by the screw motion theory. Where rigid body dynamics are concisely represented by the compact unit dual quaternion. With this approach, the limitations of point transformation are mitigated by the superior Plücker line transformation and the states update is achieved by a single coupled operation. To harness this consistent framework for a smoothing‐based VI‐SLAM, the screw motion twist parameter is based on the raw IMU measurements. Then, a consistent residual cost function with the corresponding Jacobian and covariance updates is derived for graph‐optimization algorithm respecting the screw motion paradigm. A transition method is proposed to overcome the issues of over‐parametrization by the unit dual quaternion. solving all singularity threats while saving the advantages of adopting the twist operator. Finally, the loftier performance of the proposed algorithms is attested by simulation and real‐world experiments.