Search Results (869)

Background: Residual deficits after early treatment of developmental dysplasia of the hip (DDH) using osteotomy often led to asymmetrical gait deviations with increased repetitive rates of ground reaction force (GRF) in both hips, resulting in a higher risk of early osteoarthritis. This study investigated lower limb inter-joint coordination and swing foot control during level walking in adolescents with early-treated unilateral DDH. Methods: Eleven female adolescents treated early for DDH using Pemberton osteotomy were compared with 11 age-matched healthy controls. The joint angles and angular velocities of the hip, knee, and ankle were measured, and the corresponding phase angles and continuous relative phase (CRP) for hip–knee and knee–ankle coordination were obtained. The variability of inter-joint coordination was quantified using the deviation phase values obtained as the time-averaged standard deviations of the CRP curves over multiple trials. Results: The DDH group exhibited a flexed posture with increased variability in knee–ankle coordination of the affected limb throughout the gait cycle compared to the control group. In contrast, the unaffected limb compensated for the kinematic alterations of the affected limb with reduced peak angular velocities but increased knee–ankle CRP over double-limb support and trajectory variability over the swing phase. Conclusions: The identified changes in inter-joint coordination in adolescents with early treated DDH provide a plausible explanation for the previously reported increased GRF loading rates in the unaffected limb, a risk factor of premature OA. Full article

Background: Few studies have examined exercise-based treatments for migraine and tension-type headache (TTH), and even fewer have focused on strength training and chronic headache, as these present greater challenges. Objectives: This study aimed to evaluate the effectiveness of a group-based neck and shoulder strength training intervention combined with postural correction for patients with chronic headache. Methods: This prospective, single-arm, uncontrolled pilot study with a pre–post design included patients with chronic migraine (n = 10) and TTH (n = 12) who participated in an 8-week group-based program consisting of neck and shoulder strength training three times per week, along with instructions for postural correction. The primary outcome was change in headache frequency. Secondary outcomes included changes in the intensity and duration of headache, number of days of analgesic use, and functionality. Results: In total, 22 patients completed the intervention and were included in the analysis. Headache frequency decreased at follow-up for the overall group (r = 0.531; p = 0.014). In-depth analysis showed that 45% of participants experienced an average reduction of 38% in headache frequency. Additionally, large to moderate effect sizes were observed for the secondary outcomes. Conclusions: This is the first study to introduce a group-based exercise program targeting the neck and shoulder muscles, combined with postural correction and standard pharmacological treatment, for patients with chronic primary headache. It was found to be a safe, well-tolerated, useful, and promising intervention for improving headache frequency, duration, and functionality. Full article

The heart is innervated by the autonomic nervous system (ANS), which plays a role in regulating the heart rate. Cross-country mountain biking (MTBXC) is a sport with high physiological demands, where the autonomic nervous system plays a significant role. The main objective of this study was to analyze the asymmetry of heart rate in Colombian National Team mountain bikers, sub-23 category, during a 20 min cardiovascular autonomic profile test. Method: The cardiovascular autonomic profile was measured through heart rate variability during a 20 min test, divided into eight phases (supine, controlled ventilation at 10 cycles/min, controlled ventilation at 12 cycles/min, postural change, orthostasis, Ruffier test, 1 min recovery, and final recovery) in a group of n = 10 MTB cyclists from the National Sub-23 Team, including 5 males and 5 females. Results: The results for the male athletes were as follows: age: 19 ± 1 years; VO2max: 67.5 mL/kg/min; max power: 355 W; HRmax: 204 bpm. The results for the female athletes were as follows: age: 19 ± 1 years; VOmax: 58.5 mL/kg/min; max power: 265 W; HRmax: 194 bpm. Both genders showed the expected autonomic behavior in each phase. Asymmetrical propagation of heart rate was observed, with a greater deceleration pattern after postural changes and effort and a symmetrical acceleration pattern in these two phases. Discussion: Athletes exhibit increased vagal response compared to non-athletes. Mountain bikers show rapid heart rate reduction after exertion. Conclusion: This study demonstrates how mountain bikers exhibit increased heart rate deceleration following sympathetic stimuli. Full article

Parturition is accepted as a painful situation. Few studies explore pain-specific behaviours during farrowing in sows. The objectives of this study were, first, to assess if behavioural pain indicators (BPIs) are affected by the farrowing moment, parity, and suckling events, and second, to determine the relationship between the Facial Action Units (FAUs) and BPIs during farrowing. Ten Danbred sows were recorded throughout farrowing and on day 19 post-farrowing. Continuous observations of five BPIs and five FAUs were obtained across the three moments studied: (i) at the expulsion of the piglets, (ii) the time interval between the delivery of each piglet, and (iii) 19 days after farrowing, used as a control. Primiparous sows had more BPIs but fewer postural changes than multiparous sows. The BPIs were more frequent during suckling events in the pre-weaning moment. All the FAUs and BPIs were rare or absent post-farrowing (p < 0.05), and almost all of them were more frequent during farrowing (especially at the moment of delivery). Back arching showed the highest correlation with all the FAUs, and tension above the eyes showed the highest correlation with four of the BPIs. The BPIs and FAUs indicate that sows experience more pain during farrowing than during the third week post-farrowing, and piglet expulsion is the most painful moment in farrowing. Full article

Modern electronic devices such as smartphones, wearable devices, and robots typically integrate three-dimensional sensors to track the device’s movement in the 3D space. However, sensor measurements in three-dimensional vectors are highly sensitive to device orientation since a slight change in the device’s tilt or heading can change the vector values. To avoid complications, applications using these sensors often use only the magnitude of the vector, as in geomagnetic-based indoor positioning, or assume fixed device holding postures such as holding a smartphone in portrait mode only. However, using only the magnitude of the vector loses the directional information, while ad hoc posture assumptions work under controlled laboratory conditions but often fail in real-world scenarios. To resolve these problems, we propose a universal vector calibration algorithm that enables consistent three-dimensional vector measurements for the same physical activity, regardless of device orientation. The algorithm works in two stages. First, it transforms vector values in local coordinates to those in global coordinates by calibrating device tilting using pitch and roll angles computed from the initial vector values. Second, it additionally transforms vector values from the global coordinate to a reference coordinate when the target coordinate is different from the global coordinate by correcting yaw rotation to align with application-specific reference coordinate systems. We evaluated our algorithm on geomagnetic field-based indoor positioning and bidirectional step detection. For indoor positioning, our vector calibration achieved an 83.6% reduction in mismatches between sampled magnetic vectors and magnetic field map vectors and reduced the LSTM-based positioning error from 31.14 m to 0.66 m. For bidirectional step detection, the proposed algorithm with vector calibration improved step detection accuracy from 67.63% to 99.25% and forward/backward classification from 65.54% to 100% across various device orientations. Full article

In wind-assisted orchard spraying operations, the dynamic response of leaves—manifested through changes in their posture—critically influences droplet deposition on both sides of the leaf surface and the penetration depth into the canopy. These factors are pivotal in determining spray coverage and the spatial distribution of pesticide efficacy. However, current research lacks comprehensive quantification and correlation analysis of the temporal response characteristics of leaves under wind disturbances. To address this gap, a systematic analytical framework was proposed, integrating real-time leaf segmentation and tracking, geometric feature quantification, and statistical correlation modeling. High-frame-rate videos of fluttering leaves were acquired under controlled wind conditions, and background segmentation was performed using principal component analysis (PCA) followed by clustering in the reduced feature space. A fine-tuned Segment Anything Model 2 (SAM2-FT) was employed to extract dynamic leaf masks and enable frame-by-frame tracking. Based on the extracted masks, time series of leaf area and inclination angle were constructed. Subsequently, regression analysis, cross-correlation functions, and Granger causality tests were applied to investigate cooperative responses and potential driving relationships among leaves. Results showed that the SAM2-FT model significantly outperformed the YOLO series in segmentation accuracy, achieving a precision of 98.7% and recall of 97.48%. Leaf area exhibited strong linear coupling and directional causality, while angular responses showed weaker correlations but demonstrated localized synchronization. This study offers a methodological foundation for quantifying temporal dynamics in wind–leaf systems and provides theoretical insights for the adaptive control and optimization of intelligent spraying strategies. Full article

Continuous blood pressure (BP) monitoring provides valuable insight into the body’s dynamic cardiovascular regulation across various physiological states such as physical activity, emotional stress, postural changes, and sleep. Continuous BP monitoring captures different variations in systolic and diastolic pressures, reflecting autonomic nervous system activity, vascular compliance, and circadian rhythms. This enables early identification of abnormal BP trends and allows for timely diagnosis and interventions to reduce the risk of cardiovascular diseases (CVDs) such as hypertension, stroke, heart failure, and chronic kidney disease as well as chronic stress or anxiety disorders. To facilitate continuous BP monitoring, we propose an AI-powered estimation framework. The proposed framework first uses an expert-driven feature engineering approach that systematically extracts physiological features from photoplethysmogram (PPG)-based arterial pulse waveforms (APWs). Extracted features include pulse rate, ascending/descending times, pulse width, slopes, intensity variations, and waveform areas. These features are fused with demographic data (age, gender, height, weight, BMI) to enhance model robustness and accuracy across diverse populations. The framework utilizes a Tab-Transformer to learn rich feature embeddings, which are then processed through an ensemble machine learning framework consisting of CatBoost, XGBoost, and LightGBM. Evaluated on a dataset of 1000 subjects, the model achieves Mean Absolute Errors (MAE) of 3.87 mmHg (SBP) and 2.50 mmHg (DBP), meeting British Hypertension Society (BHS) Grade A and Association for the Advancement of Medical Instrumentation (AAMI) standards. The proposed architecture advances non-invasive, AI-driven solutions for dynamic cardiovascular health monitoring. Full article

Objectives: The objective of this study was to evaluate changes in anatomical point position, cranio-cervical posture, and respiratory dimensions following conventional bimaxillary total prosthetic rehabilitation. Methods: A prospective, longitudinal, observational, analytical study was conducted on 12 patients, aged 55 to 75 years, at the Department of Dental Prosthetics at the University of Medicine and Pharmacy in Cluj-Napoca. All patients had complete bimaxillary edentulism and received removable dentures as treatment. Clinical and cephalometric analyses were performed before and after prosthetic treatment to compare changes. The cephalometric analysis was based on the guidelines of Tweed and Rocabado for evaluation. Quantitative data were described using the mean and standard deviation for normal distribution and represented by bar graphs with error bars. A paired samples t-test was used to determine differences between groups, with a significance threshold of 0.05 for the bilateral p-value. Results: When analyzing changes in cranial base inclination, the corresponding angles exhibited an increase, indicating cephalic extension. A statistically significant difference in the anteroposterior diameter of the oropharyngeal lumen with and without bimaxillary complete dentures was identified (p < 0.05). For hyperdivergent patients, modifications in the position of anatomical features on cephalometry slightly reduced the VDO and had a slight compensatory effect on skeletal typology. In contrast, for hypodivergent patients, modifications to the position of anatomical landmarks also had a compensatory effect on skeletal typology, increasing the VDO. Conclusion: Changes in the position of anatomical features on cephalometry generally have a compensatory effect on skeletal typology after complete denture placement. Complete prosthetic treatment with removable dentures can significantly influence respiratory function by reducing the oropharyngeal lumen and body posture by cephalic extension and attenuation of the lordotic curvature of the cervical spine. Full article

Gait evaluation approaches using small, lightweight inertial sensors have recently been developed, offering improvements in terms of both portability and usability. However, accelerometer outputs include both the acceleration that is generated by human motion and gravitational acceleration, which changes along with the posture of the body part to which the sensor is attached. This study presents a gait analysis method that uses the gravitational, centrifugal, tangential, and translational accelerations obtained from sensors attached to the lower legs. In this method, each sensor pose is sequentially estimated using sensor fusion to combine data obtained from a three-axis gyroscope, a three-axis accelerometer, and a three-axis magnetometer. The estimated sensor pose is then used to calculate the gravitational acceleration that is included in each axis of the sensor coordinate system. The centrifugal and tangential accelerations are determined from the gyroscope output. The translational acceleration is then obtained by subtracting the centrifugal, tangential, and gravitational accelerations from the accelerometer output. As a result, the acceleration components contained in the outputs of the accelerometers attached to the lower legs are provided. As only the acceleration components caused by walking motion are captured, thus reflecting their characteristics, it is expected that the developed method can be used for gait evaluation. Full article

Ball-end milling cutters are normally used for complex surface machining. During the milling process, the tool posture and cutting parameters of the ball-end milling cutters have a significant impact on chip formations and morphological changes. Based on the Cutter Workpiece Engagement (CWE) model, this study establishes a chip flow model for ball-end milling cutters with consideration of the tool posture variation. The machining experiments of Ti-6Al-4V with a 15° inclined plane and different feed directions were carried out. The influence mechanism of time-varying tool posture on chip formation was systematically investigated. The results reveal an interaction between the chip flow direction and the cutting velocity direction. The included angle between the chip flow directions at the maximum and minimum contact points in the CWE area affects the degree of chip curling, with a smaller angle leading to weaker curling. This research provides a theoretical foundation for the optimization of posture parameters of ball-end milling cutters and expounds on the influence of the chip flow angle on chip deformation. Full article

This study investigated the interplay of bodily degrees of freedom (DoFs) governing the collective variable comprising the center of pressure (CoP) and center of mass (CoM) in postural control through the analytical lens of multiplicative interactions across scales. We employed a task combination involving a wobble board, introducing mechanical instability mainly along the mediolateral (ML) axis and the Trail Making Task (TMT), which imposes precise visual demands primarily along the anteroposterior (AP) axis. Using Multiscale Regression Analysis (MRA), a novel analytical method rooted in Detrended Fluctuation Analysis (DFA), we scrutinized CoP-to-CoM and CoM-to-CoP effects across multiple timescales ranging from $100\mathrm{ms}$ to $10\mathrm{s}$. CoP was computed from ground reaction forces recorded via a force plate, and CoM was derived from full-body 3D motion capture using a biomechanical model. We found that the wobble board attenuated CoM-to-CoP effects across timescales ranging from $100\mathrm{to}400\mathrm{ms}$. Further analysis revealed nuanced changes: while there was an overall reduction, this encompassed an accentuation of CoM-to-CoP effects along the AP axis and a decrease along the ML axis. Importantly, these alterations in CoP’s responses to CoM movements outweighed any nonsignificant effects attributable to the TMT. CoM exhibited no sensitivity to CoP movements, regardless of the visual and mechanical task demands. In addition to identifying the characteristic timescales associated with bodily DoFs in facilitating upright posture, our findings underscore the critical significance of directionally challenging biomechanical constraints, particularly evident in the amplification of CoP-to-CoM effects along the AP axis in response to ML instability. These results underscore the potential of wobble board training to enhance the coordinative and compensatory responses of bodily DoFs to the shifting CoM by prompting appropriate adjustments in CoP, thereby suggesting their application for reinstating healthy CoM–CoP dynamics in clinical populations with postural deficits. Full article

Accurate gait assessment is essential for managing pathological locomotion, especially in elderly patients recovering from hip joint surgeries. Inertial measurement units (IMUs) provide real-time, objective data in clinical settings. This study examined pelvic oscillations in sagittal, frontal, and transverse planes using a wearable IMU system in two groups: Group A (n = 15, osteosynthesis metallica) and Group B (n = 34, arthroplasty), all over age 65. Gait analysis was conducted during assisted and unassisted walking. In the frontal plane, both groups showed statistically significant improvements: Group A from 46.4% to 75.2% (p = 0.001) and Group B from 52.6% to 72.2% (p = 0.001), reflecting enhanced lateral stability. In the transverse plane, Group A improved significantly from 47.7% to 80.2% (p = 0.001), while Group B showed a non-significant increase from 73.0% to 80.5% (p = 0.068). Sagittal plane changes were not statistically significant (Group A: 68.8% to 71.1%, p = 0.313; Group B: 76.4% to 69.1%, p = 0.065). These improvements correspond to better pelvic symmetry and postural control, which are critical for a safe and stable gait. Improvements were more pronounced during unassisted walking, indicating better pelvic control. These results confirm the clinical utility of IMUs in capturing subtle gait asymmetries and monitoring recovery progress. The findings support their use in tailoring rehabilitation strategies, particularly for enhancing frontal and transverse pelvic stability in elderly orthopedic patients. Full article

The Internet of Things (IoT) is rapidly advancing toward increased autonomy; however, the inherent dynamism, environmental uncertainty, device heterogeneity, and diverse data modalities pose serious challenges to its reliability and security. This paper proposes a novel framework for embedding security awareness into IoT systems—where security awareness refers to the system’s ability to detect uncertain changes and understand their impact on its security posture. While machine learning and deep learning (ML/DL) models integrated with explainable AI (XAI) methods offer capabilities for threat detection, they often lack contextual interpretation linked to system security. To bridge this gap, our framework maps XAI-generated explanations to a system’s structured security profile, enabling the identification of components affected by detected anomalies or threats. Additionally, we introduce a procedural method to compute an Importance Factor (IF) for each component, reflecting its operational criticality. This framework generates actionable insights by highlighting contextual changes, impacted components, and their respective IFs. We validate the framework using a smart irrigation IoT testbed, demonstrating its capability to enhance security awareness by tracking evolving conditions and providing real-time insights into potential Distributed Denial of Service (DDoS) attacks. Full article

In recent years, the application of ergonomics to workplace safety monitoring has gained increasing interest from companies and public institutions, allowing for the evaluation of the potential impact that dangerous situations may have on workers during their routine activities. This study presents a method for real-time monitoring of human physiological and motor responses to simulated workplace hazards during virtual reality safety training. The setup allows for precise measurements of both physiological and postural parameters during simulated scenarios. Moreover, a representative case study involving the sudden arrival of a forklift in a warehouse is presented. Five healthy participants were exposed to this scenario, with changes in heart rate variability and trunk posture being captured. The results demonstrate the effectiveness of sensor-based monitoring in detecting stress responses and postural adaptations to hazardous stimuli. This approach provides a basis for understanding human responses in simulated hazardous environments and may help to optimize safety training aimed at increasing workers’ risk perception and improving overall workplace safety. Although based on a small sample, the findings provide preliminary insights into the feasibility of sensor-based monitoring during VR safety training. Full article

With the increasing importance of animal welfare, behavioral indicators such as changes in head and ear posture are widely recognized as non-invasive and field-applicable markers for evaluating the emotional state and stress levels of animals. However, traditional visual observation methods are often subjective, as assessments can vary between observers, and are unsuitable for long-term, quantitative monitoring. This study proposes an artificial intelligence (AI)-based system for the detection and pose estimation of cattle heads and ears using deep learning techniques. The system integrates Mask R-CNN for accurate object detection and FSA-Net for robust 3D pose estimation (yaw, pitch, and roll) of cattle heads and left ears. Comprehensive datasets were constructed from images of Japanese Black cattle, collected under natural conditions and annotated for both detection and pose estimation tasks. The proposed framework achieved mean average precision (mAP) values of 0.79 for head detection and 0.71 for left ear detection and mean absolute error (MAE) of approximately 8–9° for pose estimation, demonstrating reliable performance across diverse orientations. This approach enables long-term, quantitative, and objective monitoring of cattle behavior, offering significant advantages over traditional subjective stress assessment methods. The developed system holds promise for practical applications in animal welfare research and real-time farm management. Full article