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The hemispherical shell resonator (HSR) is the core element of the hemispherical resonator gyroscope (HRG), and its frequency mismatch is a key property influencing gyroscope accuracy. Investigating the mechanism of frequency mismatch is vital for improving the quality of HSRs and performance of HRGs. Midsurface radius imperfections and thickness imperfections are two principal causes of frequency mismatch, but their combined effects have rarely been discussed. This paper develops a model to comprehensively analyze the frequency mismatch of HSRs with both radius and thickness imperfections. The model derives a quantitative relation between the frequency mismatch and the two imperfections, and provides principles for evaluating dominant imperfections. To validate the model, we conduct experiments on a batch of 12 HSRs with random geometric imperfections. We apply the model in calculating the theoretical frequency mismatches of these HSRs, and the results agree well with the experimental data. The experiment also confirms that for macro-HSRs, thickness imperfections have a larger impact than radius imperfections, and the frequency mismatch is approximately linear to the fourth thickness harmonic. Our research can be a useful reference for the design and fabrication of HSRs and may open new possibilities for high-precision manufacture of HRGs. [ABSTRACT FROM AUTHOR]

Inertial measurement units (IMUs) provide exciting opportunities to collect large volumes of running biomechanics data in the real world. IMU signals may, however, be affected by variation in the initial IMU placement or movement of the IMU during use. To quantify the effect that changing an IMU's location has on running data, a reference IMU was 'correctly' placed on the shank, pelvis, or sacrum of 74 participants. A second IMU was 'misplaced' 0.05 m away, simulating a 'worst-case' misplacement or movement. Participants ran over-ground while data were simultaneously recorded from the reference and misplaced IMUs. Differences were captured as root mean square errors (RMSEs) and differences in the absolute peak magnitudes and timings. RMSEs were ≤1 g and ~1 rad/s for all axes and misplacement conditions while mean differences in the peak magnitude and timing reached up to 2.45 g, 2.48 rad/s, and 9.68 ms (depending on the axis and direction of misplacement). To quantify the downstream effects of these differences, initial and terminal contact times and vertical ground reaction forces were derived from both the reference and misplaced IMU. Mean differences reached up to -10.08 ms for contact times and 95.06 N for forces. Finally, the behavior in the frequency domain revealed high coherence between the reference and misplaced IMUs (particularly at frequencies ≤~10 Hz). All differences tended to be exaggerated when data were analyzed using a wearable coordinate system instead of a segment coordinate system. Overall, these results highlight the potential errors that IMU placement and movement can introduce to running biomechanics data.

With the progress of micro electromechanical system (MEMS) technology, the performance of MEMS inertial measurement unit (IMU) composed of gyroscopes and accelerometers has been improved. Among the inertial sensors, MEMS triaxial gyroscope plays an important role in attitude estimation, navigation and positioning of intelligent mobile terminals such as unmanned aerial vehicles and unmanned vehicles. However, the measured values of low and medium cost MEMS triaxial gyroscopes are mainly affected by temperature (or thermal effect) and random errors. As results, the drift errors correlated with temperature will reduce application accuracy of MEMS triaxial gyroscope. The traditional calibration method for thermal drift errors relies on the expensive equipment, such as turntable and the temperature control system, which increases the cost of calibration. Therefore, an effective thermal calibration method that is available when using low- or high-cost tools for MEMS triaxial gyroscope will be meaningful for majority users. Hence, this paper analyzed and established the thermal drift model of MEMS triaxial gyroscope, and proposed a segmented systematic calibration method based on 24 states according to this model. Simulation shows that the proposed method can obtain the results approaching the real parameter thermal drift curves. Calibration experiments and parameters test show that the max errors of attitude calculation are reduced to 10% of the results using the original parameters, which indicates that the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the study of optical resonant cavity stability, focusing on the relevant principles, key technological advances, and applications of optical resonant cavities in a variety of high-precision measurement techniques and modern science and technology. Firstly, the vibration characteristics, thermal noise, and temperature characteristics of the reference cavity are presented. Subsequently, the report extensively discusses the advances in key technologies such as mechanical vibration isolation, thermal noise control, and resistance to temperature fluctuations. These advances not only contribute to the development of theory but also provide innovative solutions for practical applications. Typical applications of optical cavities in areas such as laser gyroscopes, high-precision measurements, and gravitational wave detection are also discussed. Future research directions are envisioned, emphasising the importance of novel material applications, advanced vibration isolation technologies, intelligent temperature control systems, multifunctional integrated optical resonator design, and deepening theoretical models and numerical simulations. [ABSTRACT FROM AUTHOR]

Circuit noise is a critical factor that affects the performances of an MEMS gyroscope. Therefore, it is essential to analyze and suppress the noises in the key analog circuits, which are the main noise sources. This study presents an optimized front-end readout circuit and noise suppression methods. First, the noise analysis of the front-end readout circuit is carried out with theoretical derivation to clarify the main noise contributors. To suppress the output noise, an improved readout circuit based on the T-resistor networks is proposed, and the corresponding noise equation is derived in detail. In addition, the noise analysis of the critical circuits of the detection and control system, such as the inverting amplifiers, the first-order low-pass filters, and the first-order high-pass filters, is carried out, and the noise suppression strategy with the optimization of the resistances and is proposed. Taking the inverting amplifier as an example, the theoretical derivation is verified by measuring and comparing the output noises of different resistance schemes. In addition, the output noises of the gyroscope before and after circuit optimization are measured. Experimental results demonstrate that the output noise with the circuit optimization is reduced from 60 μV/Hz1/2 to 30 μV/Hz1/2 and the bias instability is reduced from 3.8 deg/h to 1.38 deg/h. In addition, the ARW is significantly improved from 0.035 deg/h1/2 to 0.018 deg/h1/2, which indicates that the proposed noise analysis and suppression methods are effective and feasible. [ABSTRACT FROM AUTHOR]

An inertial navigation system (INS) utilizes three orthogonal accelerometers and gyroscopes to determine platform position, velocity, and orientation. There are countless applications for INS, including robotics, autonomous platforms, and the internet of things. Recent research explores the integration of data-driven methods with INS, highlighting significant innovations, improving accuracy and efficiency. Despite the growing interest in this field and the availability of INS datasets, no datasets are available for gyro-free INS (GFINS) and multiple inertial measurement unit (MIMU) architectures. To fill this gap and to stimulate further research in this field, we designed and recorded GFINS and MIMU datasets using 54 inertial sensors grouped in nine inertial measurement units. These sensors can be used to define and evaluate different types of MIMU and GFINS architectures. The inertial sensors were arranged in three different sensor configurations and mounted on a mobile robot, a passenger car and a turntable. In total, the dataset contains 45 hours of inertial data and corresponding ground truth trajectories. The data is freely accessible through our figshare repository. [ABSTRACT FROM AUTHOR]

This paper presents a study of the perpendicular gyroscope, which is formed of two orthogonal beams, a flexural plate and a gyroscope. Two sets of chiral-torsional boundary conditions are derived to analytically model the dynamic effects of the gyroscope while taking into account the broken symmetries of the system. The perpendicular junction causes the coupling of the compressional, flexural and torsional displacements in the system. This complex behaviour is accounted for with a comprehensive set of kinematic and dynamic junction conditions. Modal analysis demonstrates the fully coupled system and reveals how the spinning gyroscope induces dynamic chiral Chladni patterns in the plate. This article is part of the theme issue 'Current developments in elastic and acoustic metamaterials science (Part 2)'. [ABSTRACT FROM AUTHOR]

The study presents a new approach for assessing plantarflexor muscles' function using a smartphone. The test involves performing repeated heel raises for 60 s while seated. The seated heel-rise test offers a simple method for assessing plantarflexor muscles' function in those with severe balance impairment who are unable to complete tests performed while standing. The study aimed to showcase how gyroscopic data from a smartphone placed on the lower limb can be used to assess the test. Eight participants performed the seated heel-rise test with each limb. Gyroscope and 2D video analysis data (60 Hz) of limb motion were used to determine the number of cycles, the average rise (T-rise), lowering (T-lower), and cycle (T-total) times. The number of cycles detected matched exactly when the gyroscope and kinematic data were compared. There was good time domain agreement between gyroscopic and video data (T-rise = 0.0005 s, T-lower = 0.0013 s, and T-total = 0.0017 s). The 95% CI limits of agreement were small (T-total −0.1118, 0.1127 s, T-lower −0.1152, 0.1179 s, and T-total −0.0763, 0.0797 s). Results indicate that a smartphone placed on the thigh can successfully assess the seated heel-rise test. The seated heel-rise test offers an attractive alternative to test plantarflexor muscles' functionality in those unable to perform tests in standing positions. [ABSTRACT FROM AUTHOR]

This study examines the impact of sensor placement and multimodal sensor fusion on the performance of a Long Short-Term Memory (LSTM)-based model for human activity classification taking place in an agricultural harvesting scenario involving human-robot collaboration. Data were collected from twenty participants performing six distinct activities using five wearable inertial measurement units placed at various anatomical locations. The signals collected from the sensors were first processed to eliminate noise and then input into an LSTM neural network for recognizing features in sequential time-dependent data. Results indicated that the chest-mounted sensor provided the highest F1-score of 0.939, representing superior performance over other placements and combinations of them. Moreover, the magnetometer surpassed the accelerometer and gyroscope, highlighting its superior ability to capture crucial orientation and motion data related to the investigated activities. However, multimodal fusion of accelerometer, gyroscope, and magnetometer data showed the benefit of integrating data from different sensor types to improve classification accuracy. The study emphasizes the effectiveness of strategic sensor placement and fusion in optimizing human activity recognition, thus minimizing data requirements and computational expenses, and resulting in a cost-optimal system configuration. Overall, this research contributes to the development of more intelligent, safe, cost-effective adaptive synergistic systems that can be integrated into a variety of applications. [ABSTRACT FROM AUTHOR]

In order to improve the vibration isolation performance of current three-parameter damper using bellow and fluid damping, a concept of fluid damper with variable damping and secondary variable stiffness was proposed, a prototype has been designed with several frequency selective valves to adjust the damping characteristics at specific frequency bands, especially that in the low-frequency range. Followed mathematical modelling and simulation of the damper have been carried out, the results show that the designed damper has obtained excellent damping characteristics both in the high- and low-frequency ranges, the micro-vibration isolation efficiency of the damper under realistic excitation of a Control Moment Gyroscope used in a manned spacecraft reaches a significant 71.03 %.The proposed damper concept and designed prototype have laid the foundation for further testing and optimization of high-end isolators for modern spacecrafts. [ABSTRACT FROM AUTHOR]

Animal welfare is a daily concern for livestock farmers. It is known that the activity of cows characterizes their general physiological state and deviations from the normal parameters could be an indicator of different kinds of diseases and conditions. This pilot study investigated the application of machine learning for identifying the behavioral activity of cows using a collar-mounted gyroscope sensor and compared the results with the classical accelerometer approach. The sensor data were classified into three categories, describing the behavior of the animals: "standing and eating", "standing and ruminating", and "laying and ruminating". Four classification algorithms were considered—random forest ensemble (RFE), decision trees (DT), support vector machines (SVM), and naïve Bayes (NB). The training relied on manually classified data with a total duration of 6 h, which were grouped into 1s, 3s, and 5s piles. The obtained results showed that the RFE and DT algorithms performed the best. When using the accelerometer data, the obtained overall accuracy reached 88%; and when using the gyroscope data, the obtained overall accuracy reached 99%. To the best of our knowledge, no other authors have previously reported such results with a gyroscope sensor, which is the main novelty of this study. [ABSTRACT FROM AUTHOR]

The spin-exchange-pumped nuclear magnetic resonance gyroscope (NMRG) is a pivotal tool in quantum navigation. The transverse relaxation of atoms critically impacts the NMRG's performance parameters and is essential for judging normal operation. Conventional methods for measuring transverse relaxation typically use dual beams, which involves complex optical path and frequency stabilization systems, thereby complicating miniaturization and integration. This paper proposes a method to construct a 133Cs parametric resonance magnetometer using a single-beam vertical-cavity surface-emitting laser (VCSEL) to measure the transverse relaxation of 129Xe and 131Xe. Based on this method, the volume of the gyroscope probe is significantly reduced to 50 cm3. Experimental results demonstrate that the constructed Cs-Xe NMRG can achieve a transverse relaxation time (T2) of 8.1 s under static conditions. Within the cell temperature range of 70 °C to 110 °C, T2 decreases with increasing temperature, while the signal amplitude inversely increases. The research lays the foundation for continuous measurement operations of miniaturized NMRGs. [ABSTRACT FROM AUTHOR]

Mass-spring is one of the most used structures in sensors and actuators of MEMS technology, including accelerometers, gyroscopes, harvesters, etc. Some of the major design parameters of the structure are natural frequency and displacement sensitivity. The limitations of microfabrication processes should be considered in the design process. The optimized geometry topology can reduce the area occupied by the structure in the fabrication and increases the number of possible structures per wafer. This study aims to provide a novel systematic design and optimization of a MEMS mass-spring system by using a dimensionless design flowchart. Based on the dimensionless design, this algorithm provides the designer with the optimized dimensions of the mass-spring by following the provided dimensionless diagrams, taking into account stress, displacement sensitivity, natural frequency, and fabrication limitations. Also, the optimized dimensions of the mass-spring system are obtained using the dimensionless design flowchart with respect to the desired natural frequency and displacement sensitivity. In addition, genetic algorithms and dimensionless diagrams present a procedure to minimize the footprint of the structure while keeping the desired natural frequency and displacement sensitivity. Results demonstrate that up to a 3% change in both natural frequency and displacement sensitivity, leads to a 45% reduction in the structure area compared to the related literature of MEMS accelerometers. [ABSTRACT FROM AUTHOR]

In recent years, visible light positioning (VLP) techniques have been gaining popularity in research. Among them, the scheme of using a camera as a receiver provides a low-cost, high-precision positioning capability and easy integration with existing multimedia devices and robots. However, the pose changes of the receiver can lead to image distortion and light displacement, significantly increasing positioning errors. Addressing these errors is crucial for enhancing the accuracy of VLP. Most current solutions rely on gyroscopes or Inertial Measurement Units (IMUs) for error optimization, but these approaches often add complexity and cost to the system. To overcome these limitations, we propose a 3D positioning algorithm based on an attention mechanism convolutional neural network (CNN) aimed at reducing the errors caused by angles. We designed experiments and comparisons within a rotation angle range of ±15 degrees. The results demonstrate that the average error for 2D positioning is within 5.74 cm and the height error is within 3.92 cm, and the average error for 3D positioning is within 6.8 cm. Among the four groups of experiments for 3D positioning, compared to the traditional algorithm, the improvements were 7.931 cm, 15.569 cm, 6.004 cm, and 16.506 cm. The experiments indicate that it can be applied to high-precision visible light positioning for single-light imaging. [ABSTRACT FROM AUTHOR]

This paper presents the development of an analytical model of an internal vibrating ring gyroscope in a Microelectromechanical System (MEMS). The internal ring structure consists of eight semicircular beams that are attached to the externally placed anchors. This research work analyzes the vibrating ring gyroscope's in-plane displacement behavior and the resulting elliptical vibrational modes. The elliptical vibrational modes appear as pairs with the same resonance frequency due to the symmetric structure of the design. The analysis commences by conceptualizing the ring as a geometric structure with a circular shape possessing specific dimensions such as thickness, height, and radius. We construct a linear model that characterizes the vibrational dynamics of the internal vibrating ring. The analysis develops a comprehensive mathematical formulation for the radial and tangential displacements in local polar coordinates by considering the inextensional displacement of the ring structure. By utilizing the derived motion equations, we highlight the underlying relationships driving the vibrational characteristics of the MEMS' vibrating ring gyroscope. These dynamic vibrational relationships are essential in enabling the vibrating ring gyroscope's future utilization in accurate navigation and motion sensing technologies. [ABSTRACT FROM AUTHOR]

To enhance the performance of micro-hemispherical gyroscopes, achieving low-damage and high-surface-quality trimming is essential. This enables greater stability and reliability for the gyroscopes. Current methods for reducing frequency split often come with drawbacks such as high cost, adverse effects on the Q-factor, or surface damage. In this paper, a chemical etching trimming method is proposed to reduce frequency split in micro-hemispherical resonators. This method allows for trimming with minimal damage, while also being cost-effective and easy to implement. The theoretical basis of this method was analyzed, followed by a simulation to determine the optimal trimming range and location on the resonator. The simulated Q-factor analysis before and after trimming preliminarily validated the method's low-damage characteristics. Ultimately, the frequency split of the resonator was reduced to below 1 Hz. Additionally, test results of the Q-factor and surface quality before and after trimming further confirmed that chemical etching offers effective low-damage trimming capabilities. The proposed method holds significant potential for improving the performance of micro-hemispherical resonator gyroscopes. [ABSTRACT FROM AUTHOR]

On-chip photonic devices based on SiO 2 are of interest for applications such as microresonator gyroscopes and microwave sources. Although SiO 2 microdisk resonators have achieved quality factors exceeding one billion, this value remains an order of magnitude less than the intrinsic limit due to surface roughness scattering. Atomic layer etching (ALE) has potential to mitigate this scattering because of its ability to smooth surfaces to sub-nanometer length scales. While isotropic ALE processes for SiO 2 have been reported, they are not generally compatible with commercial reactors, and the effect on surface roughness has not been studied. Here, we report an ALE process for SiO 2 using sequential exposures of Al(CH3)3 (trimethylaluminum) and Ar/H2/SF6 plasma. We find that each process step is self-limiting, and that the overall process exhibits perfect synergy, with neither isolated half-cycle resulting in etching. We observe etch rates up to 0.58 Å per cycle for thermally grown SiO 2 and higher rates for ALD, plasma enhanced chemical vapor deposition, and sputtered SiO 2 up to 2.38 Å per cycle. Furthermore, we observe a decrease in surface roughness by 62% on a roughened film. The residual concentration of Al and F is around 1%–2%, which can be further decreased by O2 plasma treatment. This process could find applications in smoothing of SiO 2 optical devices and thereby enabling device quality factors to approach limits set by intrinsic dissipation. [ABSTRACT FROM AUTHOR]

The star sensor boasts the highest accuracy in spacecraft attitude measurement. However, it is vulnerable to disturbances, including high-dynamic motion, stray light, and various in-orbit environmental factors. These disruptions may lead to a significant decline in attitude accuracy or even abnormal output, potentially inducing a state of disorientation in the spacecraft. Thus, it is usually coupled with a high-frequency gyroscope to compensate for this limitation. Nevertheless, the accuracy of long-term attitude estimation using a gyroscope decreases due to the presence of bias. We propose an optimization-based tightly coupled scheme to enhance attitude estimation accuracy under dynamic conditions as well as to bolster the star sensor's robustness in cases like lost-in-space. Our approach commenced with visual–inertial measurement preprocessing and estimator initialization. Subsequently, the enhancement of attitude and bias estimation precision was achieved by minimizing visual and inertial constraints. Additionally, a keyframe-based sliding window approach was employed to mitigate potential failures in visual sensor measurements. Numerical tests were performed to validate that, under identical dynamic conditions, the proposed method achieves a 50% improvement in the accuracy of yaw, pitch, and roll angles in comparison to the star sensor only. [ABSTRACT FROM AUTHOR]

Door access control systems are important to protect the security and integrity of physical spaces. Accuracy and speed are important factors that govern their performance. In this paper, we investigate a novel approach to identify users by measuring patterns of their interactions with a doorknob via an embedded accelerometer and gyroscope and by applying deep-learning-based algorithms to these measurements. Our identification results obtained from 47 users show an accuracy of 90.2%. When the sex of the user is used as an input feature, the accuracy is 89.8% in the case of male individuals and 97.0% in the case of female individuals. We study how the accuracy is affected by the sample duration, finding that is its possible to identify users using a sample of 0.5 s with an accuracy of 68.5%. Our results demonstrate the feasibility of using patterns of motor activity to provide access control, thus extending with it the set of alternatives to be considered for behavioral biometrics. [ABSTRACT FROM AUTHOR]

Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance. [ABSTRACT FROM AUTHOR]

Modular integrated construction (MiC) is now widely adopted by industry and governments. However, its fragile and delicate logistics are still a concern for impeding project performance. MiC logistic operations involve rigorous multimode transportation, loading-unloading, and stacking during storage. Such processes may induce latent and intrinsic damage to the module. This damage causes safety hazards during assembly and deteriorates the module's structural health during the building use phase. Also, additional inspection and repairs before assembly cause uncertainties and can delay the whole supply chain. Therefore, continuous monitoring of the module's structural response during MiC logistics and the building use phase is vital. An IoT-based multi-sensing system is developed, integrating an accelerometer, gyroscope, and strain sensors to measure the module's structural response. The compact, portable, wireless sensing devices are designed to be easily installed on modules during the logistics and building use phases. The system is tested and calibrated to ensure its accuracy and efficiency. Then, a detailed field experiment is demonstrated to assess the damage, safety, and structural health during MiC logistic operations. The demonstrated damage assessment methods highlight the application for decision-makers to identify the module's structural condition before it arrives on site and proactively avoid any supply chain disruption. The developed sensing system is directly helpful for the industry in monitoring MiC logistics and module structural health during the use phase. The system enables the researchers to investigate and improve logistic strategies and module design by accessing detailed insights into the dynamics of MiC logistic operations. [ABSTRACT FROM AUTHOR]

This article addresses the important problem of tilt measurement and stabilization. This is particularly important in the case of drone stabilization and navigation in underwater environments, multibeam sonar mapping, aerial photogrammetry in densely urbanized areas, etc. The tilt measurement process involves the fusion of information from at least two different sensors. Inertial sensors (IMUs) are unique in this context because they are both autonomous and passive at the same time and are therefore very attractive. Their calibration and systematic errors or bias are known problems, briefly discussed in the article due to their importance, and are relatively simple to solve. However, problems related to the accumulation of these errors over time and their autonomous and dynamic correction remain. This article proposes a solution to the problem of IMU tilt calibration, i.e., the pitch and roll and the accelerometer bias correction in dynamic conditions, and presents the process of calculating these parameters based on combined accelerometer and gyroscope records using a new approach based on measuring increments or differences in tilt measurement. Verification was performed by simulation under typical conditions and for many different inertial units, i.e., IMU devices, which brings the proposed method closer to the real application context. The article also addresses, to some extent, the issue of navigation, especially in the context of dead reckoning. [ABSTRACT FROM AUTHOR]

The application of mobile intelligent terminal in the teaching of physical education in colleges and universities opens up a new world for teachers' education and students' study. Based on modern mobile wireless network terminal technology, this paper presents a motion attitude recognition system based on quaternion algorithm. The wireless mobile system adopts HMCL electronic module, and is equipped with MEMS chip of digital communication, which greatly eliminates the accumulation error of gyroscope and improves the accuracy of azimuth data acquisition. The data of 7 triaxial ADXL345 accelerometers and triaxial L3G4200D gyroscopes were combined with Kalman filtering for data fusion. Using the updated angular velocity data and initial value, the quaternion is constantly updated to solve the joint Angle. The experiment of human body climbing stairs with load of 0 kg, 5 kg, 10 kg and 15 kg was carried out. The experimental results were compared with those captured by high-speed camera. The error of joint Angle and motion posture of the experimenter was controlled within 5%, which verified the correctness of the model. In physical education, teachers can use this terminal system to accurately grasp whether the students' movements are standard or not, so that the teaching effect can be effectively guaranteed. [ABSTRACT FROM AUTHOR]

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To meet the strong coupling characteristics of the MSCSG deflection channel and the demand for high control accuracy, a two-degree-of-freedom deflection channel model is firstly established for the structure and working principle of the MSCSG; to meet the strong coupling between the two channels, the inverse system method is used to decouple the model; then, the operation principle of the MSCSG system is introduced, and the modeling of the power amplifier is carried out; to meet the demand for high-precision control of the MSCSG rotor system, the RBF neural network is improved using the fuzzy method to achieve high-precision estimation of the residual coupling terms and deterministic disturbances, and the adaptive sliding mode controller is designed. For the high-precision control of the MSCSG rotor system, the fuzzy method is used to improve the RBF neural network to realize the high-precision estimation of the residual coupling term and uncertain perturbation, and the adaptive sliding mode controller is designed, and the convergence of the controller is proved on the basis of the Lyapunov stability criterion. Simulation analysis shows that the method has a large improvement in decoupling performance and anti-disturbance performance compared with the traditional method, and finally, the experiment verifies the effectiveness of the present method and achieves the optimization of the deflection channel controller. The method can be extended to other magnetic levitation actuators and related fields. [ABSTRACT FROM AUTHOR]

A derailment detection algorithm for railway freight cars based on micro inertial measurement units was designed to address the complex issue of the disassembly and assembly of derailment braking devices. Firstly, a horizontal attitude measurement model for freight cars was established, and attitude measurement algorithms based on gyroscopes and accelerometers were introduced. Subsequently, a high-precision attitude measurement algorithm based on variational Bayesian Kalman filtering was proposed, which used acceleration information as the observation data to correct attitude errors. In order to improve the accuracy of derailment detection, a dual-model instantaneous attitude difference measurement technique was further proposed. In order to verify the effectiveness of the algorithm, offline data from simulation experiments and in-vehicle experiments were used to validate the proposed algorithm. The results showed that the proposed algorithm can effectively improve the measurement accuracy of horizontal attitude changes, reducing the error by 89% compared to pure inertial attitude calculation, laying a technical foundation for improving the accuracy of derailment detection. [ABSTRACT FROM AUTHOR]

In recent years, the use of mobile sensors for human activity recognition has become an intriguing research area due to the proliferation of wearable and mobile sensors. In Muslim life, the prayer (Salah) is an activity that believers are obligated to perform five times a day. In this study, a new dataset, including Salah, is presented for use in human activity recognition. Named HAR-P (Human Activity Recognition for Praying), the dataset comprises linear acceleration, acceleration, magnetic field, and gyroscope sensor data for eight activities: walking, running, typing, downstairs, upstairs, sitting, standing, and praying. Data were collected from 50 male volunteers aged 15-60 using a smartwatch for the HAR-P dataset. The classification performance of LSTM, ConvLSTM, and CNN-LSTM models was compared for the HAR-P dataset. The highest average classification accuracy of 91% was achieved with the LSTM method using linear acceleration sensor data and the ConvLSTM model using acceleration sensor data, while the lowest average accuracy of 83.6% was attained with the gyroscope sensor data and the ConvLSTM method. [ABSTRACT FROM AUTHOR]

The effective operation of surface electromyography (sEMG) signal-based controlled active prostheses and humanmachine interaction systems in daily life is crucial to work with high accuracy in different angles and positions of the arm. In this study, sEMG recordings from three different positions and angles of the arm were combined with accelerometer and gyroscope data to classify four different hand movements. The classification data (8-channel sEMG, accelerometer, and gyroscope) were collected from the right forearm of 13 participants. To create the dataset, six features were extracted from sEMG signals and three from accelerometer and gyroscope data. As a result, the methodological investigation was carried out on how different arm positions and angles affect the classification of hand movements. Evaluations were also made regarding whether the adverse effects arising from different arm positions and angles of the movement could be mitigated using accelerometer and gyroscope data, and their effects on classifier performance were discussed. As classifiers, Artificial Neural Networks (ANN) and Support Vector Machines (SVM) were used. SVM classifiers achieved an average success rate of 83% in the five different categories of analysis, while ANN classifiers achieved an average success rate of 82%. It was found that accelerometer and gyroscope data in various positions contributed very little to the performance of movement classification. As a result of the evaluation, it was discovered that collecting training data in all positions and angles of the forearm improved classification results for a sEMG-based systems. [ABSTRACT FROM AUTHOR]

In this paper, we study state estimation of multi-visual-inertial systems (MVIS) and develop sensor fusion algorithms to optimally fuse an arbitrary number of asynchronous inertial measurement units (IMUs) or gyroscopes and global and/or rolling shutter cameras. We are especially interested in the full calibration of the associated visual-inertial sensors, including the IMU/camera intrinsics and the IMU-IMU/camera spatiotemporal extrinsics as well as the image readout time of rolling-shutter cameras (if used). To this end, we develop a new analytic combined IMU integration with inertial intrinsics—termed ACI3—to pre-integrate IMU measurements, which is leveraged to fuse auxiliary IMUs and/or gyroscopes alongside a base IMU. We model the multi-inertial measurements to include all the necessary inertial intrinsic and IMU-IMU spatiotemporal extrinsic parameters, while leveraging IMU-IMU rigid-body constraints to eliminate the necessity of auxiliary inertial poses and thus reducing computational complexity. By performing observability analysis of MVIS, we prove that the standard four unobservable directions remain—no matter how many inertial sensors are used, and also identify, for the first time, degenerate motions for IMU-IMU spatiotemporal extrinsics and auxiliary inertial intrinsics. In addition to extensive simulations that validate our analysis and algorithms, we have built our own MVIS sensor rig and collected over 25 real-world datasets to experimentally verify the proposed calibration against the state-of-the-art calibration method Kalibr. We show that the proposed MVIS calibration is able to achieve competing accuracy with improved convergence and repeatability, which is open sourced to better benefit the community. [ABSTRACT FROM AUTHOR]

In conventional remaining useful life (RUL) prediction approaches grounded on maintenance, the maintenance threshold is typically established as a stationary value. However, the actual maintenance threshold may exceed its preset value due to the uncertainty of degradation and other factors. Therefore, it is necessary to consider the dynamic maintenance threshold to improve the precision of remaining useful life prediction. By considering the Wiener process, the maintenance threshold error is introduced to reflect the dynamic nature of the maintenance threshold. The influence of maintenance on degradation amount, degradation rate, and degradation path are comprehensively considered to establish a multi‐stage maintenance‐affected degradation process model. The RUL formula of the equipment is derived using the first hitting time (FHT). The maximum likelihood estimation (MLE) approach and Bayesian theory are employed to estimate the model's parameters. The proposed approach is validated using simulation data and gyroscope degradation data. The outcomes reveal that the proposed approach can significantly enhance the precision of life prediction for the equipment. [ABSTRACT FROM AUTHOR]

With considerably small structure and ultrahigh sensitivity, the graphene resonant gyroscope has been widely used in aviation, aerospace and deep-sea exploration where sensing the extremely weak angular velocity changes is required. However, small difference in the size of graphene resonant gyroscope caused by inherent uncertainties in various processing and material parameters will lead to huge differences in the output results. This will reduce the reliability of graphene resonant gyroscope. Based on the above issues, the uncertainty analysis method is adopted to establish a numerical model on the direct output resonant frequency and sensitivity of the graphene resonant gyroscope, and a random model based on sampling is introduced. The influence of the uncertainty of six input parameters on the graphene resonant frequency and sensitivity output is clarified, and thus the effect degree of the main parameters, which play a key role in the performance of the graphene resonant gyroscope, is obtained. The results show that the length, width and thickness of the graphene resonant beam have greater impacts on the output parameters, which provides theoretical guidance for the graphene resonant gyroscope to adapt to different measurement ranges. [ABSTRACT FROM AUTHOR]

Although several validated wearable devices are available for detection of generalized tonic–clonic seizures, automated detection of tonic seizures is still a challenge. In this phase 1 study, we report development and validation of an artificial neural network (ANN) model for automated detection of tonic seizures with visible clinical manifestation using a wearable wristband movement sensor (accelerometer and gyroscope). The dataset prospectively recorded for this study included 70 tonic seizures from 15 patients (seven males, age 3–46 years, median = 19 years). We trained an ANN model to detect tonic seizures. The independent test dataset comprised nocturnal recordings, including 10 tonic seizures from three patients and additional (distractor) data from three subjects without seizures. The ANN model detected nocturnal tonic seizures with visible clinical manifestation with a sensitivity of 100% (95% confidence interval = 69%–100%) and with an average false alarm rate of .16/night. The mean detection latency was 14.1 s (median = 10 s), with a maximum of 47 s. These data suggest that nocturnal tonic seizures can be reliably detected with movement sensors using ANN. Large‐scale, multicenter prospective (phase 3) trials are needed to provide compelling evidence for the clinical utility of this device and detection algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel digital closed-loop interface circuit system of MEMS vibrating gyroscope is proposed, which includes driving, detection and quadrature circuits. Because of the high sensitivity and precision of the MEMS gyroscope, the interface circuit system is established under the condition of mode separation. The driving circuit of MEMS gyroscope adopts digital automatic gain control (AGC) to realize self-excitation closed-loop control, which makes the gyroscope system simple in structure and good in robustness. First, the working principle of the gyroscope's sensitive structure is introduced and the system simulation model of the sensitive structure is developed. Second, a Σ Δ ADC and DAC with single-bit output are designed and the feasibility of the driving circuit design is verified by the system-level model. Third, the quadrature closed-loop correction circuit is designed to reduce the coupling of the quadrature component to the gyroscope detection circuit. The simulation results show that the quadrature correction loop can effectively reduce the influence of the quadrature coupling on the detection loop of MEMS gyroscope. Finally, the system-level model of the gyroscope is established by SIMULINK, the overall system performance of the gyroscope is analyzed, and the test system of MEMS gyroscope is built by field-programmable gate array (FPGA) and application specific integrated circuit (ASIC). The experiment verifies the feasibility of the digital interface circuit design, and the zero-bias instability of the gyroscope system is 1.0∘/h. [ABSTRACT FROM AUTHOR]

Angular velocity is a very important measurement parameter for autonomous driving and industrial applications. The design of MEMS quartz gyro measurement and control circuit has always been the key to restricting the measurement angular velocity performance of gyro system. This paper introduces the application-specific integrated circuit (ASIC) design and implementation of a MEMS quartz gyro measurement and control circuit for angular velocity measurement. The designed nonlinear multiplier can use a square wave to drive the gyro's sensitive structure at the beginning of gyro start-up, thereby reducing the gyro power-up time. The drive circuit replaces the PLL with an automatic gain control unit composed of peak detection and proportional integration (PI) controller, which makes the MEMS gyro system have good robustness. First, SIMULINK is used to model and simulate the MEMS gyroscope system-level model, which illustrates the feasibility of the drive circuit design scheme. Then, the operating principle of the drive loop is analyzed, and the design of the key circuit modules of the measurement and control circuit is introduced. Finally, the performance of the gyroscope drive and detection circuit is experimentally tested, the amplitude and frequency uncertainty of the gyroscope drive circuit are evaluated, and the bias instability and nonlinearity of the gyroscope are tested, the experiment results show that the gyroscope has good performance. [ABSTRACT FROM AUTHOR]

The exquisite precision of atom interferometers has sparked the interest of a large community for uses ranging from fundamental physics to geodesy and inertial navigation. However, their implementation for onboard applications is still limited, not least because rotation and acceleration are intertwined in a single phase shift, which makes the extraction of a useful signal more challenging. Moreover, the spatial separation of the wave packets due to rotations leads to a loss of signal. We present an atom interferometer operating over a large range of random angles, rotation rates and accelerations. A model of the expected phase shift allows us to untangle the rotation and acceleration signals. We also implement a real-time compensation system using fiber-optic gyroscopes and a rotating reference mirror to maintain the full contrast of the interferometer. We demonstrate a single-shot sensitivity to acceleration of 24 μg for rotation rates reaching 14° s−1. Cold atoms inertial sensors offer great precision and sensitivity, yet their use in mobile applications has been hindered by the effects of rotations on their measurements. Here, authors demonstrate an atom interferometer operating over a wide range of orientations and rotation rates, thanks to hybridisation with accelerometers and gyroscopes. [ABSTRACT FROM AUTHOR]

The primary objective of this study is to explore the spatial rotary movements of a symmetrically charged rigid body (RB) that is rotating around a fixed point, akin to Lagrange's scenario as a novel scenario where its center of mass experiences a slight displacement from the symmetry dynamic axis. The body's movement is presumed to be affected by a gyrostatic moment and a force from an electromagnetic field, attributed to the presence of a located point charge on this axis. The regulating equations of motion that are pertaining to the equations Euler–Poisson are solved through the utilization of Poincaré's small parameter method along with its adaptations when the scenario of irrational frequencies is considered. The three angles of Euler are derived and graphed to ascertain the body's position at any point throughout the motion. The temporal evolutions of the achieved outcomes are drawn to showcase the significant impact of the selected parameters on the motion. The phase plane diagrams have been generated to illustrate the stability of the body during the motion. The novelty of studying the rotatory motion of a charged RB under these specific conditions lies in the intricate interplay of gyrostatic effects, magnetic interactions, and nonlinear dynamics. This research can push the boundaries of theoretical mechanics and provide valuable insights and tools for both theoretical advancements and practical applications. Moreover, the achieved results from this analysis can be utilized to improve the dynamic performance of diverse engineering applications, particularly those dependent on gyroscopic theory. This includes enhancing the functionality of satellites, compasses, submarines, and automatic pilots used in aircraft. Essentially, the findings have practical implications for optimizing the performance and stability of these systems. [ABSTRACT FROM AUTHOR]

Background: Integrating diverse sensor technologies into smart insoles offers significant potential for monitoring biomechanical metrics; enhancing sports performance; and managing therapeutic interventions, diseases, disorders, and other health-related issues. The variation in sensor types and applications requires a systematic review to synthesize existing evidence and guide future innovations. Objectives: This review aims to identify, categorize, and critically evaluate the various sensors used in smart insoles, focusing on their technical specifications, application scopes, and validity. Methods: Following the PRISMA guidelines, a search was conducted in three major electronic databases, namely, PubMed, Scopus, and Web of Science, for relevant literature published from 2014 to 2024. Other works not located in the mentioned databases were added manually by parallel searches on related themes and suggestions from the website of the databases. To be eligible, studies were required to describe sensor implementation in insoles, specify the sensor types, and report on either validation experiments or practical outcomes. Results: The search identified 33 qualifying studies. Proper analysis revealed a dominance of pressure sensors, with accelerometers and gyroscopes also being widely used. Critical applications included gait analysis, posture correction, and real-time athletic and rehabilitation feedback. The review also examined the relative effectiveness of different sensor configurations. Conclusions: This systematic review comprehensively classifies sensor technologies within smart insoles and highlights their broad application potential across various fields. Future research should aim to standardize measurement protocols, enhance sensor integration, and advance data processing techniques to boost functionality and clinical applicability. [ABSTRACT FROM AUTHOR]

The micro inertial measurement unit (MIMU) is widely used in various fields such as aerospace, automotive industry, smartphones, and wearable devices. Field calibration is the key to ensuring measurement accuracy and reliability. To address the complex problem of solving calibration parameters for MIMU system errors in nonlinear optimization calibration methods, an alternating minimization algorithm based on soft thresholding updates was proposed in this paper. An accelerometer error model was established regarding gravity, and the gyroscope error model was established on the basis of the calibrated acceleration and rotation angular velocity in this method. Finally, the error models were used in convex optimization to design a simplified solution process. Compared with the Gauss-Newton algorithm, the scale factor calibration accuracy was improved by one order of magnitude, and in the nonorthogonal error and bias error calibration, the accuracy was improved by one to two orders of magnitude. Compared with the calibration method using a high-precision turntable with an accuracy of 0.001 °/s, the proposed method achieved an accuracy of 10-3 through manual calibration. It can still maintain stability with initial values of different orders of magnitude, and ultimately, the global optimal solution for the error was obtained. [ABSTRACT FROM AUTHOR]

Purpose. To investigate the current state of inertial sensors for navigation of unmanned aerial vehicles (UAVs), analyse their advantages, disadvantages, methods of error correction and integration with other navigation systems, as well as identify development prospects and current trends in this area. Methodology. A systematic review of scientific literature, technical documents and studies related to various types of inertial sensors, their application in UAV navigation, and data fusion methods was conducted. A comparative analysis of modern technologies and methods to sensor integration is used. Findings. The main types of inertial sensors, their advantages and disadvantages are identified, and the most effective methods of error correction and integration with other systems are revealed. Modern trends, such as the use of artificial intelligence, the development of microelectromechanical systems and the emergence of new types of sensors that contribute to the accuracy and reliability of UAV navigation, are investigated. Particular attention is paid to the integration of inertial measurement units (IMUs) with other navigation sensors, such as the Global Navigation Satellite System and visual sensors, to ensure more reliable and accurate navigation. Error correction methods, such as the Kalman filter and adaptive filters, used to improve the accuracy of navigation systems are systematised. Originality. For the first time, the types of inertial sensors are systematized and analyzed, and data on their advantages and disadvantages in the context of navigation requirements in modern UAV navigation systems are determined. Practical value. The results obtained can be used to improve UAV navigation systems, increase their reliability and accuracy. Recommendations for the selection and integration of sensors will contribute to the development of new technologies in the field of unmanned aerial vehicles. [ABSTRACT FROM AUTHOR]

Altered semitendinosus (ST) morphology and distal tendon insertion following anterior cruciate ligament reconstruction (ACLR) may reduce knee flexion torque generating capacity of the hamstrings via impaired ST force generation and/or moment arm. This study used a computational musculoskeletal model to simulate mechanical consequences of tendon harvest for ACLR on ST function by modeling changes in ST muscle tendon insertion point, moment arm, and torque generating capacity across a physiological range of motion. Simulated ST function was then compared between ACLR and uninjured contralateral limbs. Magnetic resonance imaging from 18 individuals with unilateral history of ACLR involving a hamstring autograft was used to analyse bilateral hamstring muscle (ST, semimembranosus, bicep femoris long head and short head) morphology and distal ST tendon insertion. The ACLR cohort was sub‐grouped into those with and without ST regeneration. For each participant with ST regeneration (n = 7), a personalized musculoskeletal model was created including postoperative remodeling of ST using OpenSim 4.1. Knee flexion and internal rotation moment arms and torque generating capacities of hamstrings were evaluated. Bilateral differences were calculated with an asymmetry index (%) ([unaffected limb–affected limb]/[unaffected limb + affected limb]*100%). Smaller moment arms or knee torques within injured compared to uninjured contralateral limbs were considered a deficit. Compared to uninjured contralateral limbs, ACLR limbs with tendon regeneration (n = 7) had minor reductions in knee flexion (5.80% [95% confidence interval (CI) = 3.97–7.62]) and internal rotation (4.92% [95% CI = 2.77–7.07]) moment arms. Decoupled from muscle morphology, altered ST moment arms in ACLR limbs with tendon regeneration resulted in negligible deficits in knee flexion (1.20% [95% CI = 0.34–2.06]) and internal rotation (0.24% [95% CI = 0.22–0.26]) torque generating capacity compared to uninjured contralateral limbs. Coupled with muscle morphology, ACLR limbs with tendon regeneration had substantial deficits in knee flexion (19.32% [95% CI = 18.35–20.28]) and internal rotation (15.49% [95% CI = 14.56–16.41]) torques compared to uninjured contralateral limbs. Personalized musculoskeletal models with measures of ST distal insertion and muscle morphology provided unique insights into post‐ACLR ST and hamstring function. Deficits in knee flexor and internal rotation moment arms and torque generating capacities were evident in those with ACLR even when tendon regeneration occurred. Future studies may wish to implement this framework in personalized musculoskeletal models following ACLR to better understand individual muscle function for injury prevention and treatment evaluation. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to develop a machine learning algorithm using an off‐the‐shelf digital watch, the Samsung watch (SM‐R800), and evaluate its effectiveness for the detection of generalized convulsive seizures (GCS) in persons with epilepsy. Methods: This multisite epilepsy monitoring unit (EMU) phase 2 study included 36 adult patients. Each patient wore a Samsung watch that contained accelerometer, gyroscope, and photoplethysmographic sensors. Sixty‐eight time and frequency domain features were extracted from the sensor data and were used to train a random forest algorithm. A testing framework was developed that would better reflect the EMU setting, consisting of (1) leave‐one‐patient‐out cross‐validation (LOPO CV) on GCS patients, (2) false alarm rate (FAR) testing on nonseizure patients, and (3) "fixed‐and‐frozen" prospective testing on a prospective patient cohort. Balanced accuracy, precision, sensitivity, and FAR were used to quantify the performance of the algorithm. Seizure onsets and offsets were determined by using video‐electroencephalographic (EEG) monitoring. Feature importance was calculated as the mean decrease in Gini impurity during the LOPO CV testing. Results: LOPO CV results showed balanced accuracy of.93 (95% confidence interval [CI] =.8–.98), precision of.68 (95% CI =.46–.85), sensitivity of.87 (95% CI =.62–.96), and FAR of.21/24 h (interquartile range [IQR] = 0–.90). Testing the algorithm on patients without seizure resulted in an FAR of.28/24 h (IQR = 0–.61). During the "fixed‐and‐frozen" prospective testing, two patients had three GCS, which were detected by the algorithm, while generating an FAR of.25/24 h (IQR = 0–.89). Feature importance showed that heart rate‐based features outperformed accelerometer/gyroscope‐based features. Significance: Commercially available wearable digital watches that reliably detect GCS, with minimum false alarm rates, may overcome usage adoption and other limitations of custom‐built devices. Contingent on the outcomes of a prospective phase 3 study, such devices have the potential to provide non‐EEG‐based seizure surveillance and forecasting in the clinical setting. [ABSTRACT FROM AUTHOR]

Classification of driving events is a crucial stage in driving behavior monitoring using smartphone sensory data. It has not been previously explored that to what extent classification performance depends on the classifier type and input data characteristics. To fill this gap, a real-world experiment is designed for supervised data collection. Then the effects of different machine learning (ML) classifiers, data sampling rates, and sensor combinations on the final classification accuracy are demonstrated. A considerable number of labeled events (4114) containing 11 types of driving maneuvers are collected using base sensors (accelerometer and gyroscope) and composite sensors (linear accelerometer and rotation vector) available in smartphones. Several models using 23 ML algorithms are trained. The sensitivity of these models is analyzed by changing the characteristics of the input data concerning the type of ML classifier, data sampling rate, and the bundle of mobile sensors. It is demonstrated that: (1) F1 scores vary from 70 to 96% for different ML classifiers, (2) F1 scores drop 30–40% depending on the classifier type when reducing the data sampling rate, and (3) using all four sensors as a bundle for classifying driving events is not reasonable since an approximate equal F1 score is achievable by a three-sensor bundle which includes an accelerometer and a linear accelerometer. [ABSTRACT FROM AUTHOR]

BACKGROUND: The main goal of sports science is to monitor sports injuries. Nevertheless, the existing sports injury monitoring projects have many expensive instruments and excessively extended monitoring periods, which makes it difficult to expand sports injury monitoring on a large scale. OBJECTIVE: The advancement of machine learning algorithms opens up new avenues for the tracking of sports injuries. METHODS: A training set of sports injuries was created using the Discrete Wavelet Transform (DWT) and Random Forest algorithms. Next, a basic analytic framework was created based on the lower-body movement of runners, and an athlete's injury likelihood monitoring system was established. First off, the wearable gyroscope device can efficiently plot the motion displacement curve and monitor the three-dimensional mechanics of the athlete's hips, thighs, and calves. Secondly, the system has a higher computational efficiency and an advantage over other classifier-based systems in terms of testing and training times RESULTS: The suggested system framework identifies athletes' injury propensity, providing preventive recommendations based on displacement curves, and offering a low total cost and high testing accuracy, making it easy to implement and cost-effective. CONCLUSION: All things considered, the sports injury monitoring device is very accurate and reasonably priced, making it appropriate for widespread use. [ABSTRACT FROM AUTHOR]

This paper presents a non-invasive methodology for structural health monitoring (SHM) integrated with inertial sensors and signal conditioning techniques. The proposal uses the signal of an IMU (inertial measurement unit) tri-axial accelerometer and gyroscope to continuously measure the displacements of a structure in motion due to seismic vibrations. A system, called the "Inertial Displacement Monitoring System" or "IDMS", is implemented to attenuate the signal error of the IMU with methodologies such as a Kalman filter to diminish the influence of white noise, a Chebyshev filter to isolate the frequency values of a seismic motion, and a correction algorithm called zero velocity observation update (ZVOB) to detect seismic vibrations and diminish the influence of external perturbances. As a result, the IDMS is a methodology developed to measure displacements when a structure is in motion due to seismic vibration and provides information to detect failures opportunely. [ABSTRACT FROM AUTHOR]

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Adaptive active suspension systems, integral to specialized vehicles, hold significance for their stability and safety. This study introduces a novel adaptive active suspension system featuring four independently controlled wheels employing wheel-hub motors, hydraulic cylinders, pump motor power, controllers, and sensors. A rapid and, within a certain range, leveling and height adjustment control strategy is proposed for this system, utilizing the Kalman filter algorithm. Additionally, the paper examines the front-wheel Ackermann steering model and four-wheel reverse Ackermann transition model to enhance the suspension's stability. Subsequently, experiments on leveling and height adjustment are conducted, demonstrating the system's capability to swiftly and accurately rectify the vehicle's deviation angle within the specified threshold. Following adjustments made by the leveling and height control algorithm, the vehicle body promptly returns to the preset level state and designated height. The leveling control system holds broad applicability in intelligent agriculture, logistics handling, off-road equipment, and other domains, presenting significant practical utility. [ABSTRACT FROM AUTHOR]

The field of levitated optomechanics has experienced significant advancements in manipulating the translational and rotational dynamics of optically levitated particles and exploring their sensing applications. The concept of using optically levitated particles as gyroscopes to measure angular motion has long been explored but has not yet been proven either theoretically or experimentally. In this study, we present the first rotor gyroscope based on optically levitated high-speed rotating particles. The gyroscope is composed of a micrometer-size ellipsoidal vaterite particle that is driven to rotate at MHz frequencies in a vacuum environment. When an external angular velocity is input, the optical axis deviates from its initial position, resulting in changes in the frequency and amplitude of the rotational signal. By analyzing these changes, the angular velocity of the input can be accurately detected, making it the smallest rotor gyroscope in the world. The angular rate bias instability of the gyroscope is measured to be 0.08°/s and can be further improved to as low as 10−9°/h theoretically by cooling the motion and increasing the angular moment of the levitated particle. Our work opens a new application paradigm for levitated optomechanical systems and may pave the way for the development of quantum rotor gyroscopes. [ABSTRACT FROM AUTHOR]

The use of wearable sensors, such as inertial measurement units (IMUs), and machine learning for human intent recognition in health-related areas has grown considerably. However, there is limited research exploring how IMU quantity and placement affect human movement intent prediction (HMIP) at the joint level. The objective of this study was to analyze various combinations of IMU input signals to maximize the machine learning prediction accuracy for multiple simple movements. We trained a Random Forest algorithm to predict future joint angles across these movements using various sensor features. We hypothesized that joint angle prediction accuracy would increase with the addition of IMUs attached to adjacent body segments and that non-adjacent IMUs would not increase the prediction accuracy. The results indicated that the addition of adjacent IMUs to current joint angle inputs did not significantly increase the prediction accuracy (RMSE of 1.92° vs. 3.32° at the ankle, 8.78° vs. 12.54° at the knee, and 5.48° vs. 9.67° at the hip). Additionally, including non-adjacent IMUs did not increase the prediction accuracy (RMSE of 5.35° vs. 5.55° at the ankle, 20.29° vs. 20.71° at the knee, and 14.86° vs. 13.55° at the hip). These results demonstrated how future joint angle prediction during simple movements did not improve with the addition of IMUs alongside current joint angle inputs. [ABSTRACT FROM AUTHOR]

This study focused on developing and evaluating a gyroscope-based step counter algorithm using inertial measurement unit (IMU) readings for precise athletic performance monitoring in soccer. The research aimed to provide reliable step detection and distance estimation tailored to soccer-specific movements, including various running speeds and directional changes. Real-time algorithms utilizing shank angular data from gyroscopes were created. Experiments were conducted on a specially designed soccer-specific testing circuit performed by 15 athletes, simulating a range of locomotion activities such as walking, jogging, and high-intensity actions. The algorithm outcome was compared with manually tagged data from a high-quality video camera-based system for validation, by assessing the agreement between the paired values using limits of agreement, concordance correlation coefficient, and further metrics. Results returned a step detection accuracy of 95.8% and a distance estimation Root Mean Square Error (RMSE) of 17.6 m over about 202 m of track. A sub-sample (N = 6) also wore two pairs of devices concurrently to evaluate inter-unit reliability. The performance analysis suggested that the algorithm was effective and reliable in tracking diverse soccer-specific movements. The proposed algorithm offered a robust and efficient solution for tracking step count and distance covered in soccer, particularly beneficial in indoor environments where global navigation satellite systems are not feasible. This advancement in sports technology widens the spectrum of tools for coaches and athletes in monitoring soccer performance. [ABSTRACT FROM AUTHOR]