Search Results (6,303)

Shaken Baby Syndrome (SBS) is one of the primary causes of fatal head trauma in infants and young children, occurring in about 33 per 100,000 infants annually in the U.S., with mortality rates being between 15% and 38%. Survivors frequently endure long-term disabilities, such as cognitive deficits, visual impairments, and motor dysfunction. Diagnosing SBS remains difficult due to the lack of visible injuries and delayed symptom onset. Existing detection methods—such as neuroimaging, biomechanical modeling, and infant monitoring systems—cannot perform real-time detection and face ethical, technical, and accuracy limitations. This study proposes an inertial measurement unit (IMU)-based detection system enhanced with machine learning to identify aggressive shaking patterns. Findings indicate that wearable-based motion analysis is a promising method for recognizing high-risk shaking, offering a non-invasive, real-time solution that could minimize infant harm and support timely intervention. Full article

The parameter identification of Autonomous Underwater Vehicles (AUVs) serves as a fundamental basis for achieving high-precision motion control, state monitoring, and system development. Currently, AUV parameter identification typically relies on the complete motion information obtained from onboard sensors. However, in practical applications, it is often challenging to accurately measure key state variables such as velocity and angular velocity, resulting in incomplete measurement data that compromises identification accuracy and model reliability. This issue is particularly pronounced in vertical motion tasks involving low-speed, large pitch angles, and highly maneuverable conditions, where the strong coupling and nonlinear characteristics of underwater vehicles become more significant. Traditional hydrodynamic models based on full-state measurements often suffer from limited descriptive capability and difficulties in parameter estimation under such conditions. To address these challenges, this study investigates a parameter identification method for AUVs operating under vertical, large-amplitude maneuvers with constrained measurement information. A control autoregressive (CAR) model-based identification approach is derived, which requires only pitch angle, vertical velocity, and vertical position data, thereby reducing the dependence on complete state observations. To overcome the limitations of the conventional Recursive Least Squares (RLS) algorithm—namely, its slow convergence and low accuracy under rapidly changing conditions—a Multi-Innovation Least Squares (MILS) algorithm is proposed to enable the efficient estimation of nonlinear hydrodynamic characteristics in complex dynamic environments. The simulation and experimental results validate the effectiveness of the proposed method, demonstrating high identification accuracy and robustness in scenarios involving large pitch angles and rapid maneuvering. The results confirm that the combined use of the CAR model and MILS algorithm significantly enhances model adaptability and accuracy, providing a solid data foundation and theoretical support for the design of AUV control systems in complex operational environments. Full article

The force ripple of a permanent magnet synchronous linear motor (PMSLM) caused by multi-source disturbances in practical applications seriously restricts its high-precision motion control performance. The traditional single-mechanism model has difficulty fully characterizing the nonlinear disturbance factors, while the data-driven method has real-time limitations. Therefore, this paper proposes a hybrid modeling framework that integrates the physical mechanism and measured data and realizes the dynamic compensation of the force ripple by constructing a collaborative suppression algorithm. At the mechanistic level, based on electromagnetic field theory and the virtual displacement principle, an analytical model of the core disturbance terms such as the cogging effect and the end effect is established. At the data level, the acceleration sensor is used to collect the dynamic response signal in real time, and the data-driven ripple residual model is constructed by combining frequency domain analysis and parameter fitting. In order to verify the effectiveness of the algorithm, a hardware and software experimental platform including a multi-core processor, high-precision current loop controller, real-time data acquisition module, and motion control unit is built to realize the online calculation and closed-loop injection of the hybrid compensation current. Experiments show that the hybrid framework effectively compensates the unmodeled disturbance through the data model while maintaining the physical interpretability of the mechanistic model, which provides a new idea for motor performance optimization under complex working conditions. Full article

Background: Seiza, a traditional sitting posture requiring deep ankle plantarflexion and knee flexion, often becomes difficult after ankle fracture surgery because of restricted mobility. Increased stiffness of the tibialis anterior (TA) muscle, particularly in its deep and superficial fibres, may limit plantarflexion and affect functional recovery. This study aimed to investigate the relationship between TA muscle stiffness, assessed using shear wave elastography (SWE), and the ability to assume the seiza posture after ankle fracture surgery. We also sought to determine whether the stiffness in the deep or superficial TA fibres was more strongly correlated with seiza ability. Methods: In this cross-sectional study, 38 patients who underwent open reduction and internal fixation for ankle fractures were evaluated 3 months postoperatively. Seiza ability was assessed using the ankle plantarflexion angle and heel–buttock distance. The shear moduli of the superficial and deep TA fibres were measured using SWE. Ankle range of motion, muscle strength, and self-reported seiza pain were also measured. Multiple linear regression was used to identify the predictors of seiza performance. Results: The shear moduli of both deep (β = −0.454, p < 0.001) and superficial (β = −0.339, p = 0.017) TA fibres independently predicted ankle plantarflexion angle during seiza (adjusted R², 0.624). Pain during seiza was significantly associated with reduced plantarflexion, whereas muscle strength was not a significant predictor. Conclusions: TA muscle stiffness, especially in the deep fibres, was significantly associated with limited postoperative seiza performance. Targeted interventions that reduce deep TA stiffness may enhance functional outcomes. Full article

The influence of soft tissue structures, including ligaments spanning one or more intervertebral junctions and the nuchal ligament, on motion of the equine cervical joints remains unclear. The present study addressed this using four post-mortem horse specimens extending from head to withers with all ligaments intact. Three-dimensional kinematics was obtained from markers on the head and bone-anchored markers on each cervical and the first thoracic vertebra during rotation, lateral bending, flexion and extension of the whole head, and neck segment. Yaw, pitch, and roll angles in 8 cervical joints (total 32) were calculated. Flexion and extension were expressed mainly as pitch in 27 and 22 joints, respectively. Rotation appeared as predominantly roll in 13 joints, whereas lateral bending was represented as predominantly yaw in 1 and as roll or pitch in all other joints. Significant correlations between yaw, pitch, and roll were observed at individual cervical joints in 97% of all measurements, with the atlanto-occipital joint showing complete (100%) correlation. Most non-significant correlations occurred at the C5–C6 joint, while C6–C7 exhibited significantly lower correlation coefficients compared to other levels. The overall movement of the head and neck is not replicated at individual cervical joint levels and should be considered when evaluating equine necks in vivo. Full article

Many commercial wearable sensor systems typically rely on a single continuous cardiorespiratory sensing modality, photoplethysmography (PPG), which suffers from inherent biases (i.e., differences in skin tone) and noise (e.g., motion and pressure artifacts). In this research, we present a wearable device that provides robust estimates of cardiorespiratory variables by combining three physiological signals from the upper arm: multiwavelength PPG, single-sided electrocardiography (SS-ECG), and bioimpedance plethysmography (BioZ), along with an inertial measurement unit (IMU) providing 3-axis accelerometry and gyroscope information. We evaluated the multimodal device on 16 subjects by its ability to estimate heart rate (HR) and breathing rate (BR) in the presence of various static and dynamic noise sources (e.g., skin tone and motion). We proposed a hierarchical approach that considers the subject’s skin tone and signal quality to select the optimal sensing modality for estimating HR and BR. Our results indicate that, when estimating HR, there is a trade-off between accuracy and robustness, with SS-ECG providing the highest accuracy (low mean absolute error; MAE) but low reliability (higher rates of sensor failure), and PPG/BioZ having lower accuracy but higher reliability. When estimating BR, we find that fusing estimates from multiple modalities via ensemble bagged tree regression outperforms single-modality estimates. These results indicate that multimodal approaches to cardiorespiratory monitoring can overcome the accuracy–robustness trade-off that occurs when using single-modality approaches. Full article

To address the problems of traditional methods that rely on destructive sampling, the poor adaptability of fixed equipment, and the susceptibility of single-view angle measurements to occlusions, a non-destructive and portable device for three-dimensional phenotyping and biomass detection in lettuce was developed. Based on the Structure-from-Motion Multi-View Stereo (SFM-MVS) algorithms, a high-precision three-dimensional point cloud model was reconstructed from multi-view RGB image sequences, and 12 phenotypic parameters, such as plant height, crown width, were accurately extracted. Through regression analyses of plant height, crown width, and crown height, and the R² values were 0.98, 0.99, and 0.99, respectively, the RMSE values were 2.26 mm, 1.74 mm, and 1.69 mm, respectively. On this basis, four biomass prediction models were developed using Adaptive Boosting (AdaBoost), Support Vector Regression (SVR), Gradient Boosting Decision Tree (GBDT), and Random Forest Regression (RFR). The results indicated that the RFR model based on the projected convex hull area, point cloud convex hull surface area, and projected convex hull perimeter performed the best, with an R² of 0.90, an RMSE of 2.63 g, and an RMSEn of 9.53%, indicating that the RFR was able to accurately simulate lettuce biomass. This research achieves three-dimensional reconstruction and accurate biomass prediction of facility lettuce, and provides a portable and lightweight solution for facility crop growth detection. Full article

With the diversification of space applications, the tracking of maneuvering targets has gradually gained attention. Issues such as their wide range of movement and observation outliers caused by human operation are worthy of in-depth discussion. This paper presents a novel distributed diffusion multi-noise Interacting Multiple Model (IMM) filter for maneuvering target tracking in heavy-tailed noise. The proposed approach leverages parallel Gaussian and Student-t filters to enhance robustness against non-Gaussian process and measurement noise. This hybrid filter is implemented as a node within a distributed network, where the diffusion algorithm leads to the global state asymptotically reaching consensus as the filtering time progresses. Furthermore, a fusion of multiple motion models within the IMM algorithm enables robust tracking of maneuvering targets across the distributed network and process outlier caused by maneuver compared to previous studies. Simulation results demonstrate the effectiveness of the proposed filter in tracking maneuvering targets. Full article

Pupillometry is commonly used to evaluate cognitive effort, attention, and facial expression response, offering valuable insights into human performance. The combination of eye tracking and facial expression data under the iMotions platform provides great opportunities for multimodal research. However, there is a lack of standardized pipelines for managing pupillometry data on a multimodal platform. Preprocessing pupil data in multimodal platforms poses challenges like timestamp misalignment, missing data, and inconsistencies across multiple data sources. To address these challenges, the authors introduced a systematic preprocessing pipeline for pupil diameter measurements collected using iMotions 10 (version 10.1.38911.4) during an endoscopy simulation task. The pipeline involves artifact removal, outlier detection using advanced methods such as the Median Absolute Deviation (MAD) and Moving Average (MA) algorithm filtering, interpolation of missing data using the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP), and mean pupil diameter calculation through linear regression, as well as normalization of mean pupil diameter and integration of the pupil diameter dataset with facial expression data. By following these steps, the pipeline enhances data quality, reduces noise, and facilitates the seamless integration of pupillometry other multimodal datasets. In conclusion, this pipeline provides a detailed and organized preprocessing method that improves data reliability while preserving important information for further analysis. Full article

Repairing veneer defects is the key to ensuring the quality of plywood. In order to improve the maintenance quality and material utilization efficiency during the maintenance process, this paper proposes a bidirectional maintenance method based on gear rack transmission and its related equipment. Based on the working principle, a geometric relationship model was established, which combines the structural parameters of the mold, punch, and gear system. Simultaneously, it solves the problem of motion attitude analysis of conjugate tooth profiles under non-standard meshing conditions, aiming to establish a constraint relationship between stamping motion and structural design parameters. On this basis, a constrained optimization model was developed by integrating multi-objective optimization theory to maximize maintenance efficiency. The NSGA-III algorithm is used to solve the model and obtain the Pareto front solution set. Subsequently, three optimal parameter configurations were selected for simulation analysis and experimental platform construction. The simulation and experimental results indicate that the veneer repair time ranges from 0.6 to 1.8 seconds, depending on the stamping speed. A reduction of 28 mm in die height decreases the repair time by approximately 0.1 seconds, resulting in an efficiency improvement of about 14%. The experimental results confirm the effectiveness of the proposed method in repairing veneer defects. Vibration measurements further verify the system’s stable operation under parametric modeling and optimization design. The main vibration response occurs during the meshing and disengagement phases between the gear and rack. Full article

With the rapid technological advancements in wearable devices, motion and health management have significantly improved, enabling the measurement of various biometric data with compact equipment. Our research focuses on motion measurement but, in general, full-body motion estimation requires motion capture systems or multiple inertial sensors, making it necessary to directly measure movement itself. In this study, we propose estimating full-body posture using inverse kinematics based on trunk posture and limb-end information collected through wearable devices. To enhance estimation accuracy in this underdetermined problem, we employ Physics-Informed Neural Networks (PINNs), which efficiently learn using physical laws as a loss function, along with a high-precision inverse kinematics model of a digital human. Through this approach, we enable high-accuracy full-body posture estimation even with wearable devices in underdetermined scenarios. Full article

The saddle’s adaptability to the rider–horse pair’s biomechanics is essential for equestrian comfort and performance. However, approaches to dynamic evaluation of saddle fitting are still limited in equestrian conditions. The purpose of this study is to propose a method of quantifying saddle adaptation to the rider–horse pair in motion. Eight rider–horse pairs were tested using four similar saddles with small modifications (seat depth, flap width, and front panel thickness). Seven inertial sensors were attached to the riders and horses to measure the active range of motion of the horses’ forelimbs and hindlimbs, stride duration, active range of motion of the rider’s pelvis, and rider–horse interaction. The results reveal that even small saddle changes affect the pair’s biomechanics. Some saddle configurations limit the limbs’ active range of motion, lengthen strides, or modify the rider’s pelvic motion. The temporal offset between the movements of the horse and the rider changes depending on the saddle modifications. These findings support the effect of fine saddle changes on the locomotion and synchronization of the rider–horse pair. The use of inertial sensors can be a potential way for quantifying the influence of dynamic saddle fitting and optimizing saddle adaptability in stable conditions with saddle fitter constraints. Full article

This study aimed to compare isometric strength, range of motion (RoM), and strength ratios of shoulder internal and external rotators between female volleyball and hand ball players Twenty-five volleyball players (age = 21.8 ± 4.8 years, height = 178.5 ± 7.1 cm, mass = 69.3 ± 7.7 kg) and twenty-four handball players (age = 19.5 ± 2.9 years, height = 169.7 ± 6.4 cm, mass = 67.6 ± 8.4 kg), all competing in the Slovenian 1st national league, participated. Maximal isometric strength and passive RoM of internal and external rotation were measured bilaterally using a handheld dynamometer and goniometer, respectively. A significant group × side interaction was observed for internal rotation RoM (F = 5.41; p = 0.024; η² = 0.10), with volleyball players showing lower RoM on the dominant side (p = 0.001; d = 0.89), but this was not the case for handball players (p = 0.304). External rotation strength also showed a significant interaction (F = 9.34; p = 0.004; η² = 0.17); volleyball players were stronger in the non-dominant arm (p = 0.033), while handball players were stronger in the dominant arm (p = 0.041). The external-to-internal rotation strength ratio was significantly lower on the dominant side in volleyball players compared to handball players (p = 0.047; d = 0.59). Findings suggest sport-specific adaptations and asymmetries in shoulder function, emphasizing the need for sport-specific and individually tailored injury prevention strategies. Volleyball players, in particular, may benefit from targeted strengthening of external rotators and flexibility training to address imbalances. Full article

Accurate estimation of upper-limb joint angles is essential in biomechanics, rehabilitation, and wearable robotics. While inertial measurement units (IMUs) offer portability and flexibility, systems requiring multiple inertial sensors can be intrusive and complex to deploy. In contrast, optical motion capture (MoCap) systems provide precise tracking but are constrained to controlled laboratory environments. This study presents a deep learning-based approach for estimating shoulder and elbow joint angles using only three IMU sensors positioned on the chest and both wrists, validated against reference angles obtained from a MoCap system. The input data includes Euler angles, accelerometer, and gyroscope data, synchronized and segmented into sliding windows. Two recurrent neural network architectures, Convolutional Neural Network with Long-short Term Memory (CNN-LSTM) and Bidirectional LSTM (BLSTM), were trained and evaluated using identical conditions. The CNN component enabled the LSTM to extract spatial features that enhance sequential pattern learning, improving angle reconstruction. Both models achieved accurate estimation performance: CNN-LSTM yielded lower Mean Absolute Error (MAE) in smooth trajectories, while BLSTM provided smoother predictions but underestimated some peak movements, especially in the primary axes of rotation. These findings support the development of scalable, deep learning-based wearable systems and contribute to future applications in clinical assessment, sports performance analysis, and human motion research. Full article

Objectives: This study aimed to investigate the efficacy of Percutaneous Electrolysis (PE), Percutaneous peripheral Nerve Stimulation (PNS), and Eccentric Exercise (EE) in patients with supraspinatus tendinopathy. Methods: Forty-six participants with supraspinatus tendinopathy were randomly allocated to either an invasive therapy group (four sessions in four weeks of PE+PNS and EE program) or a conventional physical therapy group (ten sessions for 2 weeks). The multimodal physical program included Ultrasound therapy (US), Transcutaneous Electric Nerve Stimulation (TENS) and the same EE program. The Numerical Pain Rating Scale (NPRS), shoulder Range of Motion (ROM), Pressure Pain Threshold (PPT), and disability (DASH and SPADI) were measured at baseline, at the end of treatment, and at 12- and 24-weeks follow-up. Results: The PE+PNS+EE group demonstrated consistently greater and statistically significant improvements across nearly all pain, mobility, and functional outcomes at all follow-up points (post-treatment, 12-weeks, and 24-weeks) compared to the TENS+US+EE group, with generally medium to large effect sizes. Conclusions: This study concludes that the combined PE+PNS+EE intervention offers safe and effective treatment for supraspinatus tendinopathy, demonstrating statistically significant improvements in pain, mobility, and function compared to conventional electrotherapy. Full article