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For inertial platforms with unknown model parameters and internal information, traditional model-free controllers fail to resist external vibrations solely based on the platform gyroscope, deteriorating the performance of inertial platforms. Therefore, we apply the light gradient boosting machine (LightGBM) to identify an end-to-end platform model, followed by proposing a data-driven MPC scheme to improve the control performance. Furthermore, an expectation maximization (EM) method is designed to solve the optimization problems with non-differentiable identification models, which are challenges for the traditional gradient descent-based optimizer. In addition, an adaptive compensation strategy is designed for generalizing the data-driven control scheme to different external vibrations. Finally, experimental results demonstrate the feasibility, efficacy, and generalization ability of the proposed method.

An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.

In order to estimate the ship heave displacement information in real time and accurately, combined with the ship heave motion model, a dual inertial measurement units (IMU) ship heave displacement estimation method is proposed, the corresponding observer is established, and the heave information of the two Imus is fused. The simulation results show that the dual IMU ship heave estimation method has higher accuracy than the single IMU ship heave estimation method.

In response to the need for an inertial navigation system with a rotation modulation function to achieve accurate alignment in a short time, an alignment method based on recursive least squares (RLS) and using two-position alignment is proposed. This method first simplifies the correlation equation between error velocity and attitude misalignment angle and the zero bias of the inertial measurement unit (IMU) and uses RLS to estimate and analyze the initial target alignment error parameters. Then, iterative optimization is used to further refine the error parameters. Finally, it is verified through simulation experiments. The results show that in the case of initial alignment in a short time, this method is correct and reasonable.

There is widespread and growing use of inertial measurement technology for human motion analysis in biomechanics and clinical research. Due to advancements in sensor miniaturization, inertial measurement units can be used to obtain a description of human body and joint kinematics both inside and outside the laboratory. While algorithms for data processing continue to improve, a lack of standard reporting guidelines compromises the interpretation and reproducibility of results, which hinders advances in research and development of measurement and intervention tools. To address this need, the International Society of Biomechanics approved our proposal to develop recommendations on the use of inertial measurement units for joint kinematics analysis. A collaborative effort that incorporated feedback from the biomechanics community has produced recommendations in five categories: sensor characteristics and calibration, experimental protocol, definition of a kinematic model and subject-specific calibration, analysis of joint kinematics, and quality assessment. We have avoided an overly prescriptive set of recommendations for algorithms and protocols, and instead offer reporting guidelines to facilitate reproducibility and comparability across studies. In addition to a conceptual framework and reporting guidelines, we provide a checklist to guide the design and review of research using inertial measurement units for joint kinematics.

Background Inertial measurement units (IMUs) offer the ability to measure walking gait through a variety of biomechanical outcomes (e.g., spatiotemporal, kinematics, other). Although many studies have assessed their validity and reliability, there remains no quantitive summary of this vast body of literature. Therefore, we aimed to conduct a systematic review and meta-analysis to determine the i) concurrent validity and ii) test-retest reliability of IMUs for measuring biomechanical gait outcomes during level walking in healthy adults. Methods Five electronic databases were searched for journal articles assessing the validity or reliability of IMUs during healthy adult walking. Two reviewers screened titles, abstracts, and full texts for studies to be included, before two reviewers examined the methodological quality of all included studies. When sufficient data were present for a given biomechanical outcome, data were meta-analyzed on Pearson correlation coefficients (r) or intraclass correlation coefficients (ICC) for validity and reliability, respectively. Alternatively, qualitative summaries of outcomes were conducted on those that could not be meta-analyzed. Results A total of 82 articles, assessing the validity or reliability of over 100 outcomes, were included in this review. Seventeen biomechanical outcomes, primarily spatiotemporal parameters, were meta-analyzed. The validity and reliability of step and stride times were found to be excellent. Similarly, the validity and reliability of step and stride length, as well as swing and stance time, were found to be good to excellent. Alternatively, spatiotemporal parameter variability and symmetry displayed poor to moderate validity and reliability. IMUs were also found to display moderate reliability for the assessment of local dynamic stability during walking. The remaining biomechanical outcomes were qualitatively summarized to provide a variety of recommendations for future IMU research. Conclusions The findings of this review demonstrate the excellent validity and reliability of IMUs for mean spatiotemporal parameters during walking, but caution the use of spatiotemporal variability and symmetry metrics without strict protocol. Further, this work tentatively supports the use of IMUs for joint angle measurement and other biomechanical outcomes such as stability, regularity, and segmental accelerations. Unfortunately, the strength of these recommendations are limited based on the lack of high-quality studies for each outcome, with underpowered and/or unjustified sample sizes (sample size median 12; range: 2–95) being the primary limitation.

Inertial sensing enables field studies of human movement and ambulant assessment of patients. However, the challenge is to obtain a comprehensive analysis from low-quality data and sparse measurements. In this paper, we present a method to estimate gait kinematics and kinetics directly from raw inertial sensor data performing a single dynamic optimization. We formulated an optimal control problem to track accelerometer and gyroscope data with a planar musculoskeletal model. In addition, we minimized muscular effort to ensure a unique solution and to prevent the model from tracking noisy measurements too closely. For evaluation, we recorded data of ten subjects walking and running at six different speeds using seven inertial measurement units (IMUs). Results were compared to a conventional analysis using optical motion capture and a force plate. High correlations were achieved for gait kinematics (ρ⩾0.93) and kinetics (ρ⩾0.90). In contrast to existing IMU processing methods, a dynamically consistent simulation was obtained and we were able to estimate running kinetics. Besides kinematics and kinetics, further metrics such as muscle activations and metabolic cost can be directly obtained from simulated model movements. In summary, the method is insensitive to sensor noise and drift and provides a detailed analysis solely based on inertial sensor data.

Simultaneous Localization and Mapping (SLAM) in dynamic environments remains a significant challenge for autonomous systems. To address this, a series of dynamic SLAM methods have been studied, including semantic-based and geometry-based models. Although these methods are effective, they usually incur substantial computational costs or sacrifice the overall accuracy of the model. In this work, an enhanced system built upon ORB-SLAM3 is proposed. It integrates lightweight YOLOv8-tiny object detection and IMU-based epipolar geometry for dynamic object detection, and leverages logit probability fusion to dynamically update the likelihood of a feature point being on a moving object. In addition, we put forward an adaptive thresholding strategy that gauges the level of scene dynamics by considering Inertial Measurement Unit (IMU) acceleration fluctuations, geometric outlier ratio, and the count of detected objects, so as to classify feature points effectively. Comprehensive experiments on the TUM dataset demonstrate that this method achieves significant effectiveness in both accuracy and robustness in dynamic scenarios.

As the imaging distance and focal length of photoelectric systems increase, the requirements for line-of–sight stabilization of optical inertial stabilized platforms (ISPs) become higher. Disturbance rejection directly determines the stability accuracy of optical inertial stabilized platforms. However, the accurate observation and suppression of wide-band and rapidly changing disturbances remains a challenge in current engineering applications. This paper proposes a robust extended sliding mode observer (ESMO) method to improve disturbance estimation performance. First, the linear extended state observer (LESO) is designed by taking the total disturbances as extended states. Then, a sliding mode observer (SMO) is incorporated in the extended states of the extended observer, forming a robust ESMO. Subsequently, the robustness and convergence characteristics of the proposed method are mathematically proved, revealing that it operates robustly without knowing the disturbance’s upper bound and offers faster dynamics and higher accuracy than the LESO. Finally, a series of simulation experimental tests are performed to demonstrate the effectiveness of the proposed method. The proposed method observes wide-band and rapidly changing disturbances utilizing the rapidly switching characteristic of the SMO and smooths the jitter of the SMO by cascading sliding mode estimation to the differentiation term of extended observation, achieving the integral effect of the reaching law. Meanwhile, this method only requires adjusting two parameters, making it suitable for engineering applications. It can be effectively used in optical inertial stabilized platform control systems for disturbance estimation and compensation.

Despite the exponential growth in using inertial measurement units (IMUs) for biomechanical studies, future growth in “inertial motion capture” is stymied by a fundamental challenge - how to estimate the orientation of underlying bony anatomy using skin-mounted IMUs. This challenge is of paramount importance given the need to deduce the orientation of the bony anatomy to estimate joint angles. This paper systematically surveys a large number (N = 112) of studies from 2000 to 2018 that employ four broad categories of methods to address this challenge across a range of body segments and joints. We categorize these methods as: (1) Assumed Alignment methods, (2) Functional Alignment methods, (3) Model Based methods, and (4) Augmented Data methods. Assumed Alignment methods, which are simple and commonly used, require the researcher to visually align the IMU sense axes with the underlying anatomical axes. Functional Alignment methods, also commonly used, relax the need for visual alignment but require the subject to complete prescribed movements. Model Based methods further relax the need for prescribed movements but instead assume a model for the joint. Finally, Augmented Data methods shed all of the above assumptions, but require data from additional sensors. Significantly different estimates of the underlying anatomical axes arise both across and within these categories, and to a degree that renders it difficult, if not impossible, to compare results across studies. Consequently, a significant future need remains for creating and adopting a standard for defining anatomical axes via inertial motion capture to fully realize this technology’s potential for biomechanical studies.