Search Results (92)

Work-related musculoskeletal disorders (WMSDs) are among the most prevalent occupational health issues, particularly affecting public transport drivers due to prolonged sitting, constrained postures, and poorly adaptable cabins. This study addresses the ergonomic risks associated with tram driving, aiming to evaluate biomechanical load and postural stress in relation to drivers’ anthropometric characteristics. A combined methodological approach was applied, integrating two standardized observational tools—RULA and REBA—with anthropometric modeling based on three representatives European morphotypes (SmallW, MidM, and TallM). ErgoFellow 3.0 software was used for digital posture evaluation, and lumbar moments at the L4/L5 vertebral level were calculated to estimate lumbar loading. The analysis was simulation-based, using digital human models, and no real subjects were involved. The results revealed uniform REBA (Rapid Entire Body Assessment) and RULA (Rapid Upper Limb Assessment) scores of 6 across all morphotypes, indicating moderate to high risk and a need for ergonomic intervention. Lumbar moments ranged from 51.35 Nm (SmallW) to 101.67 Nm (TallM), with the tallest model slightly exceeding the recommended ergonomic thresholds. These findings highlight a systemic mismatch between cabin design and user variability. In conclusion, ergonomic improvements such as adjustable seating, better control layout, and driver education are essential to reduce the risk of WMSDs. The study proposes a replicable methodology combining anthropometric, observational, and biomechanical tools for evaluating and improving transport workstation design. 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

This study investigates how three technical factors—steering wheel tilt, torque, and cabin vibration frequency—affect driver posture. Heavy-truck drivers often suffer from musculoskeletal disorders (MSDs), mainly due to poor cabin ergonomics and prolonged postures during work. In countries like Ecuador, making major structural changes to cabin design is not feasible. These factors were identified through video analysis and surveys from drivers at two Ecuadorian trucking companies. An experimental system was developed using a simplified cabin to control these variables, while posture and muscle activity were recorded in 16 participants using motion capture, inertial sensors, and electromyography (EMG) on the upper trapezius, middle trapezius, triceps brachii, quadriceps muscle, and gastrocnemius muscle. The test protocol simulated key truck-driving tasks. Data were analyzed using ANOVA (${\displaystyle p<0.05}$), with technical factors and mass index as independent variables, and posture metrics as dependent variables. Results showed that head mass index significantly affected head abduction–adduction (${\displaystyle -}$8.12 to ${\displaystyle -}$2.18°), and spine mass index influenced spine flexion–extension (${\displaystyle -}$0.38 to 6.99°). Among technical factors, steering wheel tilt impacted trunk flexion–extension (${\displaystyle -}$13.56 to 16.99°) and arm rotation (${\displaystyle -}$31.1 to 19.7°). Steering wheel torque affected arm rotation (${\displaystyle -}$30.49 to ${\displaystyle -}$6.77°), while vibration frequency influenced forearm flexion–extension (3.76 to 16.51°). EMG signals showed little variation between muscles, likely due to the protocol’s short duration. These findings offer quantitative support for improving cabin ergonomics in low-resource settings through targeted, cost-effective design changes. Full article

With the vigorous development of speedy railway technology, steel rails, as an important structural infrastructure in speedy railways, play a crucial role in ensuring the safety of the entire speedy railway operation. A brand-new type of speedy rail inspection robot and its fault detection method are proposed to solve a number of problems, such as the difficulty and low accuracy of real-time online detection of rail defects and damage in speedy railways. The brand-new rail inspection robot is driven by two drive wheels and adopts a standard rail gauge of 1435 mm, which ensures its speedy and smooth operation on the track as well as accurate motion posture information. Firstly, 12 common types of surface damage of the rail head were analyzed and classified into five categories based on their damage characteristics. The motion state of the rail inspection robot under the five types of surface damage of the rail head was analyzed and subjected to kinematic analysis. This study analyzed the relationship between the distinctive types of damage and the motion posture of the robot during the inspection of the five types of damage. Finally, experimental tests were conducted, and it was found that the robot’s motion posture would undergo sudden changes when inspecting distinctive types of injuries; the highest error rate was 3%. The effectiveness of this method was verified through experiments, and the proposed new track detection robot can greatly improve the track detection efficiency of high-speed railways and has specific academic research value and practical application value. Full article

Cloud-native architecture is becoming increasingly popular in today’s digital environment, driving the demand for robust security precautions to protect infrastructure and applications. This paper examines a variety of privacy-enhancing and trust-centric tools and techniques intended to meet the unique security requirements within cloud-native environments. Specifically, a variety of solutions are covered, such as runtime protection platforms for real-time threat detection and responses, cloud-native endpoint security solutions for ensuring trust and resilience in dynamic contexts, and service mesh technologies for secure service-to-service communication. Furthermore, we examine the roles of cloud-native encryption, cloud-native identity and access management, and container image scanning technologies in protecting containerized applications and preserving data privacy in transit and at rest. The importance of threat detection and response systems, cloud-native security information and event management (SIEM) solutions, and network security are also covered to strengthen trust and transparency in cloud-native security. We also present a thorough case study that demonstrates how security measures are applied across multiple layers, including application, network, infrastructure, and security, and compliance, to ensure holistic security in a cloud-native architecture. By investigating these privacy-enhancing methods and technologies, organizations may improve the security posture of their cloud-native implementations, reducing risks and ensuring the trustworthiness of their information and applications in the ever-changing ecosystem of today’s digital landscape. Full article

The integration of redundant structures into robotic systems enhances the degrees of freedom (DOFs), flexibility, and capability to perform complex tasks. This study evaluates the mechanical performance of a 9-DOF series-parallel hybrid redundant device designed for rehabilitation training of patients with balance disorders. The redundant structural design improves the robot’s movement flexibility, optimizes load distribution, and mitigates stress concentration in local joints or components. To optimize the robot’s overall structural parameters and reduce joint driving forces, a genetic algorithm (GA) was employed. A custom dataset was created by collecting motion-related data, including foot posture and position. The robot’s mechanical characteristics were comprehensively analyzed, followed by simulation experiments. The results demonstrate that incorporating the redundant structure, along with the optimization of structural parameters, significantly enhances the robot’s mechanical performance. This study provides a solid foundation for the functional development and control system design of rehabilitation robots, extending the capabilities of existing systems and offering a novel, reliable, and efficient therapeutic tool for patients with balance disorders. Full article

Background: Motor impairments following stroke contribute to deficits in functional mobility. Traditionally, these impairments are quantified by mean-level motor performance. However, this mean-level approach neglects the well-established fact that motor performance becomes highly variable in aging and disease. Increased intra-individual variability (IIV) in behavior predicts functional decline in neurological disorders. Despite this, the impact of stroke on IIV in motor performance and its influence on functional mobility has not been investigated. This study aimed to (1) quantify the impact of stroke on IIV in motor performance, and (2) determine the contribution of IIV and mean motor performance to functional mobility. Methods: Twenty stroke survivors and 20 age-matched controls performed a goal-directed ankle movement task over 30 trials. We measured average accuracy (mean endpoint error) and IIV (within-person SD of endpoint error). Functional mobility was assessed with postural control (sway area during quiet standing) and braking response time in a driving simulator. Results: Stroke participants showed a higher mean (p = 0.04) and greater IIV (p = 0.016) in endpoint error than controls. Sway area did not differ between groups (p = 0.24), but stroke survivors had increased braking response time (p = 0.016). In stroke survivors, IIV significantly predicted sway area (R² = 0.33, p = 0.008) and braking response time (R² = 0.27, p = 0.02), and mean error did not account for any additional variance. Conclusions: Stroke reduces the trial-to-trial consistency of executing motor tasks with precision. IIV in motor performance predicts postural balance and braking response time and can potentially serve as an indicator of increased vulnerability and an important target for stroke rehabilitation. Full article

Bleeding constitutes an essential stage in pre-processing operations for fish products. In China, this process remains entirely manual, characterized by low efficiency, high labor intensity, and operational hazards, creating a pressing demand for versatile automated equipment in the market. To address the mechanization requirements for efficient freshwater fish bloodletting, we developed an automated fish bleeding machine based on gill-based blood extraction principles, incorporating an impact-triggered positional bleeding methodology. The positional triggering mechanism was engineered through kinematic and dynamic analyses of fish sliding trajectories onto the trigger plate, informed by morphological parameters of fish specimens. This design achieved automated positional awareness and bleeding activation. A reciprocating bleeding mechanism was developed by mimicking manual bleeding motions, leveraging the quick-return motion characteristics of an offset crank-slider mechanism. The transmission system combined chain-drive and clutch mechanisms to enable sequential and intermittent power delivery. Experimental validation employed live specimens of snakehead (Channa argus), grass carp (Ctenopharyngodon idellus), and tilapia (Oreochromis mossambicus), with systematic evaluations including sensory assessments, comparative testing, and performance metrics. Results demonstrated a comprehensive sensory evaluation score of 3.8 for bleeding efficacy; significant influence of baffle geometry on performance, identifying V-shaped baffles as optimal; a bleeding success rate averaging 94% with throughput reaching 1417 fish/hour. The integrated workflow—directional feeding, postural constraint, positional triggering, reciprocating bleeding, and automated ejection—established a cyclic mechanized bleeding process with industrial applicability. Full article

Ensuring comfort in light mobility is a crucial aspect for supporting individuals’ well-being and safety while driving scooters, riding bicycles, etc. In fact, factors such as the hand grip on the handlebar, positions of the wrist and arm, overall body posture, and affecting vibrations play key roles. Wearable systems offer the ability to noninvasively monitor physiological parameters, such as body temperature and heart rate, aiding in personalized comfort assessment. In this context, user positions while driving or riding are, on the other hand, more challenging to monitor ecologically. Developing effective smart gloves as a support for comfort and movement monitoring introduces technical complexities, particularly in sensor selection and integration. Light and flexible sensors can help in this regard by ensuring reliable sensing and thus addressing the optimization of the comfort for the driver. In this work, a novel wireless smart glove is proposed, integrating four bend sensors, four force-sensitive sensors, and one inertial measurement unit for measuring the finger movements, hand orientation, and the contact force exerted by the hand while grasping the handlebar during driving or riding. The smart glove has been proven to be repeatable (1.7%) and effective, distinguishing between different grasped objects, such as a flask, a handlebar, a tennis ball, and a small box. Additionally, it proved to be a valuable tool for monitoring specific actions while riding bicycles, such as braking, and for optimizing the posture during the ride. Full article

Trajectory prediction plays a crucial role in level autonomous driving systems, as real-time and accurate trajectory predictions can significantly enhance the safety of autonomous vehicles and the robustness of the autonomous driving system. We propose a novel trajectory prediction model that adopts the encoder–decoder paradigm. In the encoder, we introduce a dual-thread interaction relationship encoding method based on a sparse graph attention mechanism, which allows our model to aggregate richer multimodal interaction information. Additionally, we introduce a non-autoregressive query generation method that reduces the model’s inference time by approximately 80% through the parallel generation of decoding queries. Finally, we propose a multi-stage decoder that generates more accurate and reasonable predicted trajectories by predicting trajectory reference points and performing spatial and posture optimization on the predicted trajectories. Comparative experiments with existing advanced algorithms demonstrate that our method improves the minimum Average Displacement Error (minADE), minimum Final Displacement Error (minFDE), and Miss Rate (MR) by 10.3%, 10.3%, and 14.5%, respectively, compared to the average performance. Lastly, we validate the effectiveness of the various modules proposed in this paper through ablation studies. Full article

Inspired by earthworm peristalsis, a novel modular robot suitable for narrow spaces is proposed, capable of elongation, contraction, deflection and crawling. Unlike motor-driven robots, the earthworm-inspired robot achieves extension and deflection in each module through “on–off” control of the SMA springs, utilizing the cooperation of mechanical skeletons and gears to avoid posture redundancy. The return to the initial posture and the maintenance of the posture are achieved through tension and torsion springs. To study the extension and deflection characteristics, we established a model through kinematic and force analysis to estimate the relationship between the length change and tensile characteristics of the SMA on both sides and the robot’s extension length and deflection angle. Through model verification and experiments, the robot’s extension, deflection and movement characteristics in narrow spaces and varying curvature narrow spaces were comprehensively studied. The results show that the earthworm-inspired robot, as predicted by the model, possesses accurate extension and deflection performance, and can perform inspection tasks in complex and narrow space environments. Additionally, compared to motor-driven robots, the robot designed in this study does not require insulation in low-temperature environments, and the cold conditions can improve its movement efficiency. This new configuration design and the extension and deflection characteristics provide valuable insights for the development of new modular robots and robot drive designs for extremely cold environments. Full article

In quiet standing, the central nervous system implements a pre-programmed ankle strategy of postural control to maintain upright balance and stability. This strategy comprises a synchronized common neural drive delivered to synergistically grouped muscles. This study evaluated connectivity between EMG signals of the unilateral and bilateral homologous muscle pairs of the lower legs during various standing balance conditions using magnitude-squared coherence (MSC). The leg muscles examined included the right and left tibialis anterior (TA), medial gastrocnemius (MG), and soleus (S). MSC is a frequency domain measure that quantifies the linear phase relation between two signals and was analyzed in the alpha (8–13 Hz), beta (13–30 Hz), and gamma (30–100 Hz) neural frequency bands for feet together and feet tandem, with eyes open and eyes closed conditions. Results showed that connectivity in the beta and lower and upper gamma bands (30–100 Hz) was influenced by standing balance conditions and was indicative of a neural drive originating from the motor cortex. Instability was evaluated by comparing less stable standing conditions with a baseline—eyes open feet together stance. Changes in connectivity in the beta and gamma bands were found to be most significant in the muscle pairs of the back leg during a tandem stance regardless of dominant foot placement. MSC identified the MG:S muscle pair as significant for the right and left leg. The results of this study provided insight into the neural mechanism of postural control. Full article

Trajectory planning in tight spaces presents a significant challenge due to the complex maneuvering required under kinematic and obstacle avoidance constraints. When obstacles are densely distributed near the target state, the limited connectivity between the feasible states and terminal state can further decrease the efficiency and success rate of trajectory planning. To address this challenge, we propose a novel Dual-Stage Motion Pattern Tree (DS-MPT) algorithm. DS-MPT decomposes the trajectory generation process into two stages: merging and posture adjustment. Each stage utilizes specific heuristic information to guide the construction of the trajectory tree. Our experimental results demonstrate the high robustness and computational efficiency of the proposed method in various parallel parking scenarios. Additionally, we introduce an enhanced driving corridor generation strategy for trajectory optimization, reducing computation time by 54% to 84% compared to traditional methods. Further experiments validate the improved stability and success rate of our approach. Full article

The rapid spread of micromobility vehicles such as bicycles and electric scooters poses new challenges to urban transportation systems, particularly in terms of road safety and infrastructure integration. This study investigates the driving behavior of micromobility users at a mini-roundabout, focusing on their speed profiles and their position within the lane during the entry, circulation, and exit phases. A structured recruitment process was used to select 20 participants with previous micromobility experience. Participants performed crossing maneuvers at a mini-roundabout in Gravina di Catania, Italy, which were monitored using drone footage and analyzed with tracking software to extract trajectories and speed data. The results show significant differences between e-scooter and bicycle users, with bicycles showing less speed variability, especially during the crossing and exit phases, while e-scooters showed greater variability, especially during the entry and exit phases. The results highlight the influence of vehicle stability and user posture on riding behavior and emphasize the need for infrastructure adaptations to increase safety. Mini-roundabouts designed for moderate speed are identified as a promising solution to improve the coexistence of micromobility and motor vehicles. This research identifies key differences in speed profiles and behavioral patterns between e-scooter and bicycle users, offering actionable insights and recommendations for safer and more efficient urban infrastructure. These contributions provide valuable guidance for urban planners and policymakers in promoting safer and more sustainable urban mobility. Full article

This study focused on lower leg swelling as a typical physical load in prolonged sitting postures such as driving. We obtained prediction equations for lower leg swelling (ratio of the inverse of the measured impedance to the initial impedance and lower leg swelling assessment value, BI [%]) from thigh pressure distribution, participants’ physical characteristics, and sitting time. A total of 22 participants (11 males and 11 females) were recruited. The impedance in the lower leg and thigh pressure distribution were measured over 90 min in a sitting posture at three tilt angles (8°, 0°, and −8° from the horizontal plane). Multiple regression analysis was performed to construct prediction equations for lower leg swelling in the males, the females, and all the participants. Bioelectrical impedance was selected as the dependent variable, with height, body fat percentage, thigh pressure distribution, and sitting time as the independent variables. The validity of all constructed prediction equations for the males, the females, and all the participants was confirmed by an adjusted R². These findings can be used to develop a device to prevent lower leg swelling (the main problem resulting from a prolonged sitting posture) and can be applied to automobile seats, aircraft seats, and office chairs. Full article