Search Results (90)

This study investigates the risk of developing musculoskeletal disorders (MSDs) in individuals performing standing tasks, with a focus on real-time posture assessment using motion capture technology. Improper body posture and repetitive movements during daily work activities can impose strain on the musculoskeletal system, increasing the likelihood of discomfort and long-term injury. Data were collected from five male and female participants using the Perception Neuron motion capture system, with body-mounted sensors tracking posture and movement. Joint angles were calculated to distinguish between correct and incorrect postures based on ISO 11226:2000 ergonomic guidelines. Key physical risk factors identified included prolonged forward trunk inclination, elevated arm positions, and repetitive actions. The analysis revealed that participants frequently adopted moderate- to high-risk postures, especially when working at non-ergonomic desk heights, suggesting a heightened risk of MSDs such as back and upper limb pain. These findings underscore the importance of real-time ergonomic monitoring and adaptive workstation design to reduce musculoskeletal risks in standing work environments. Full article

This study introduces a real-time, non-intrusive monitoring system designed to support elderly care through vision-based pose estimation and hand gesture recognition. The proposed framework integrates convolutional neural networks (CNNs), temporal modeling using LSTM networks, and symmetry-aware keypoint analysis to enhance the accuracy and reliability of behavior detection under varied real-world conditions. By leveraging the bilateral symmetry of human anatomy, the system improves the robustness of posture and gesture classification, even in the presence of partial occlusion or variable lighting. A total of 21 hand landmarks and 33 body pose points are used to recognize predefined actions and communication gestures, enabling seamless interaction without wearable devices. Experimental evaluations across four distinct lighting environments confirm a consistent accuracy above 90%, with real-time alerts triggered via IoT messaging platforms. The system’s modular architecture, interpretability, and adaptability make it a scalable solution for intelligent elderly individual monitoring, offering a novel application of spatial symmetry and optimized deep learning in healthcare technology. Full article

With the rapid advancement of industrial intelligence, ensuring occupational safety has become an increasingly critical concern. Among the essential personal protective equipment (PPE), safety helmets play a vital role in preventing head injuries. There is a growing demand for real-time detection of helmet chinstrap wearing status during industrial operations. However, existing detection methods often encounter limitations such as user discomfort or potential privacy invasion. To overcome these challenges, this study proposes a non-intrusive approach for detecting the wearing state of helmet chinstraps, inspired by the mechanosensory hair arrays found on spider legs. The proposed method utilizes multiple MEMS inertial sensors to emulate the sensory functionality of spider leg hairs, thereby enabling efficient acquisition and analysis of helmet wearing states. Unlike conventional vibration-based detection techniques, posture signals reflect spatial structural characteristics; however, their integration from multiple sensors introduces increased signal complexity and background noise. To address this issue, an improved adaptive convolutional neural network (ICNN) integrated with a long short-term memory (LSTM) network is employed to classify the tightness levels of the helmet chinstrap using both single-sensor and multi-sensor data. Experimental validation was conducted based on data collected from 20 participants performing wall-climbing robot operation tasks. The results demonstrate that the proposed method achieves a high recognition accuracy of 96%. This research offers a practical, privacy-preserving, and highly effective solution for helmet-wearing status monitoring in industrial environments. Full article

A remarkably well-preserved skeleton of a male Mammuthus meridionalis, approximately 60 years old, from the Early Pleistocene that is housed at the Castle of L’Aquila (Italy) exhibits a fractured left tusk with severe bone erosion of the alveolus and premaxillary bone, as well as marked spinal deformities. The cranial region underwent ultrasonographic, radiological, and histological examinations, while morphological and biomechanical analyses were conducted on the vertebral column. Microscopic analysis revealed intra vitam lesions, including woven bone fibers indicative of early bone remodeling and lamellar bone with expanded and remodeled Haversian systems. These findings are consistent with osteomyelitis and bone sequestration, likely resulting from chronic pulpitis following the tusk fracture, possibly due to an accident or interspecific combat. The vertebral column shows cervical scoliosis, compensatory curves, fusion between the first cervical vertebrae, and asymmetric articular facets, suggesting postural adaptations. Evidence of altered molar wear and masticatory function also support long-term survival post-trauma. Additionally, lesions compatible with spondyloarthropathy, an inflammatory spinal condition not previously documented in Mammuthus meridionalis, were identified. These findings provide new insights into the pathology and adaptive responses of extinct proboscideans, demonstrating the critical role of (paleo)histological methods in reconstructing trauma, disease, and aspects of life history in fossil vertebrates. Full article

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

Physiological tremor analysis is a practical tool for assessing the neuromuscular impacts of sport-specific training. The purpose of this study was to examine and compare the physiological characteristics of lower limb resting postural tremor in athletes from Poland’s national speed skating team, following both sprint and endurance workouts. The study included 19 male, well-trained, elite athletes (with a mean age of 18 ± 3.1 years, body mass of 71.4 ± 10.1 kg, height of 178.5 ± 9.0 cm, and training experience of 12.6 ± 2.8 years) and a control group of 19 physically active but non-athlete men (with a mean age of 19 ± 2.3 years, body mass of 78.9 ± 12.1 kg, and height of 181.5 ± 11.0 cm). This group was assessed under resting conditions to provide baseline reference values for physiological tremor and to evaluate whether the neuromuscular tremor response is specific to trained athletes. Tremor amplitude and frequency were measured using an accelerometer, with data log-transformed to normalize the power spectrum distribution. Key findings indicate a significant effect of training condition on tremor amplitude in the low-frequency range (L(2_5); F_(1,18) = 38.42; p < 0.0001; η_p² = 0.68) and high-frequency range (L(9_14); F_(1,36) = 19.19; p < 0.0001; η_p² = 0.51). Post hoc analysis showed that tremor amplitude increased significantly after both sprint (p < 0.001) and endurance training (p < 0.001) compared to rest. No significant differences were observed between sprint and endurance training conditions for L(2_5) (p = 0.1014), but sprint training resulted in a greater increase in tremor in the high-frequency range (L(9_14); p < 0.0001). Tremor frequency (F(2_5) and F(9_14)) also increased significantly post-training. Notably, no differences were observed between limbs, indicating symmetrical neuromuscular adaptation. These findings highlight the utility of tremor analysis in monitoring neuromuscular fatigue and performance in speed skaters. Future research should explore the application of this method in broader athletic populations and evaluate its potential integration into training programs. Full article

Core stability is fundamental to posture, balance, and force transmission throughout the kinetic chain. Although traditionally associated with athletic performance, emerging research highlights its broader applicability to recreational fitness. This study investigates the effects of an eight-week core training program on muscle hypertrophy, static balance, and neuromuscular control in recreationally active, non-athletic adults. Participants will undertake a structured intervention comprising progressive triads targeting core stability, strength, and power. Assessment methods include surface electromyography (EMG), ultrasound imaging, three-dimensional force plates, Kinovea motion analysis, and the Satisfaction With Life Scale (SWLS) questionnaire. Expected outcomes include enhanced core muscle activation, improved static balance, and increased core-generated force during overhead medicine ball slam trials. Additionally, the intervention aims to facilitate hypertrophy of the transverse abdominis, internal oblique, and lumbar multifidus muscles, contributing to spinal resilience and motor control. This protocol bridges gaps in core training methodologies and advances their scalability for recreational populations. The proposed model offers a structured, evidence-informed framework for improving core activation, postural stability, muscle adaptation, movement efficiency, and perceived quality of life in recreationally active individuals. Full article

Service-Learning (SL) is an innovative educational methodology that integrates academic learning with active community engagement, fostering both technical and transversal competencies. This pilot study explores the implementation of an SL-based experience within the Physiotherapy Degree at the University of Extremadura. The primary objective was to design and deliver therapeutic exercise programs targeting patients with cardiorespiratory conditions, utilizing local community resources. A total of 44 third-year physiotherapy students participated in the design and simulated the implementation of community-based interventions targeting muscular strength, postural control, balance, and endurance. A mixed-methods approach was used, combining descriptive statistics (SPSS v23) and thematic analysis of student reflections to assess the impact of SL on the development of specific professional competencies, including clinical reasoning, patient communication, therapeutic planning, and adaptation of interventions to diverse environments. The results show a significant improvement in students’ theoretical and practical understanding, with over 70% of participants rating their learning experience between 8 and 10 (on a 0–10 scale) in aspects such as pathology description, clinical assessment, and exercise planning. Additionally, 92% reported improved teamwork, 89% noted better adaptability, and 87% reported enhanced decision-making skills. The findings suggest that SL can enhance perceived learning in musculoskeletal rehabilitation and support the transition from academic training to clinical practice. However, the study is exploratory and based on perceived outcomes, and future research should include validated tools and real patients to assess its impact more rigorously. This pilot study highlights the value of integrating musculoskeletal-focused training—targeting strength, balance, and endurance—into physiotherapy education through Service-Learning methodology. The study highlights SL’s potential to enrich physiotherapy education while leveraging community spaces—such as those in Extremadura, a region with three UNESCO World Heritage Sites—as dynamic learning environments. Full article

Resilient artificial intelligence (Resilient AI) is relevant in many areas where technology needs to adapt quickly to changing and unexpected conditions, such as in the medical, environmental, security, and agrifood sectors. In the case study involving the therapeutic rehabilitation of patients with motor problems, the Resilient AI system is crucial to ensure that systems can effectively respond to changes, maintain high performance, cope with uncertainties and complex variables, and enable the dynamic monitoring and adaptation of therapy in real time. The proposed system integrates advanced technologies, such as computer vision and deep learning models, focusing on non-invasive solutions for monitoring and adapting rehabilitation therapies. The system combines the Microsoft Kinect v3 sensor with MoveNet Thunder – SinglePose, a state-of-the-art deep-learning model for human pose estimation. Kinect’s 3D skeletal tracking and MoveNet’s high-precision 2D keypoint detection together improve the accuracy and reliability of postural analysis. The main objective is to develop an intelligent system that captures and analyzes a patient’s movements in real time using Motion Capture techniques and artificial intelligence (AI) models to improve the effectiveness of therapies. Computer vision tracks human movement, identifying crucial biomechanical parameters and improving the quality of rehabilitation. Full article

Objectives: Assessing balance in highly trained individuals, such as military pilots, poses challenges, as deficits may be underestimated when compared to general population norms. To address this, several studies have proposed tailored databases providing reference values for specific populations. This study retrospectively analyzed balance characteristics in active-duty military pilots of the Italian Air Force. Methods: We enrolled 106 subjects split into two groups: 53 military pilots from the Italian Air Force and 53 civilians without flight experience or exposure to specific vestibular stimuli. All participants underwent ENT examinations with audiometric testing to exclude related pathologies, followed by a personal history collection. Subsequently, they completed the EquiTest protocol across six standard conditions. Results: Significant differences were observed between Army Aviators and Non-Aviators. The PREF variable showed the most consistent distinction, with military pilots demonstrating a superior performance (p < 0.01). Additionally, borderline differences were noted in Condition 6 of the equilibrium scores (p = 0.056), and in the Centre of Gravity (COG) analysis along the X-axis for Conditions 1 and 5 (p = 0.090), and for Condition 2 (p = 0.050). These findings suggest enhanced postural control strategies among Army Aviators under conditions of sensory conflict. Conclusions: These findings suggest that normative balance values specific to military pilots should be used when evaluating aviators recovering from balance deficits. Such tailored benchmarks can help determine the need for rehabilitation before returning to duty, ensuring optimal performance under demanding conditions. Further research is necessary to explore the underlying mechanisms responsible for these adaptations and to identify the specific stimuli that contribute to the enhanced balance capabilities observed in this highly trained population. Full article

Background: Basketball carries a high risk of both chronic and acute musculoskeletal injuries, affecting various parts of the body. Additionally, stress is a critical factor that influences athletic performance, particularly in high-pressure sports like basketball. This study aimed to investigate the impact of a specific proprioceptive training protocol on professional basketball players. Methods: Thirty male basketball players (M = 21.93, SD = 3.75 years) were divided into two groups: an experimental group (n = 15) and a control group (n = 15). The experimental group completed an adapted proprioceptive training program designed to enhance position-specific skills, following their regular team training. The control group continued to follow their routine training program without any additional proprioceptive intervention. The parameters assessed included stress levels, longitudinal body axis alignment, spinal range of motion, and total plantar load distribution. These were measured at three time points: baseline (T₀), after 4 weeks of training (T₁), and after 8 weeks of training (T₂). Results: Data analysis showed a significant reduction in stress (p < 0.001), postural alignment (p < 0.001), and spinal range of motion (p < 0.001) in the experimental group compared to the control group. Conclusions: In conclusion, the findings highlight the effectiveness of specific and detailed training programs in injury prevention, offering valuable insights for coaches and sports psychologists. Full article

As cyber threats escalate in scale and sophistication, the imperative to secure public data through theoretically grounded and practically viable frameworks becomes increasingly urgent. This review investigates whether and how criminology theories have effectively informed the development and implementation of information security management frameworks (ISMFs) to prevent cybercrime and fortify the digital ecosystem’s resilience. Anchored in a comprehensive bibliometric analysis of 617 peer-reviewed records extracted from Scopus and Web of Science, the study employs Multiple Correspondence Analysis (MCA), conceptual co-word mapping, and citation coupling to systematically chart the intellectual landscape bridging criminology and cybersecurity. The review reveals those foundational criminology theories—particularly routine activity theory, rational choice theory, and deterrence theory—have been progressively adapted to cyber contexts, offering novel insights into offender behaviour, target vulnerability, and systemic guardianship. In parallel, the study critically engages with global cybersecurity standards such as National Institute of Standards and Technology (NIST) and ISO, to evaluate how criminological principles are embedded in practice. Using data from the Global Cybersecurity Index (GCI), the paper introduces an innovative visual mapping of the divergence between cybersecurity preparedness and digital development across 170+ countries, revealing strategic gaps and overperformers. This paper ultimately argues for an interdisciplinary convergence between criminology and cybersecurity governance, proposing that the integration of criminological logic into cybersecurity frameworks can enhance risk anticipation, attacker deterrence, and the overall security posture of digital public infrastructures. Full article

Background: Aging is associated with musculoskeletal pain and postural adaptations which may affect functionality. This study aims to analyse the effect of a combined protocol of self-assisted manual therapy and high-intensity walking on musculoskeletal back pain, functionality, and shoulder posture in older adults, and to establish the short- and medium-term effects of this program. Methods: A multicentre, double-blind, randomized trial was conducted on older adults. The sample was divided into two groups: the self-assisted manual therapy plus walking at high-intensity group (MTWG) and the Control Group (CG), with the latter undergoing supervised high-intensity walking only. Pain (Pressure Pain Threshold and Visual Analogue Scale), functional capacity (5-times sit-to-stand test), and change in thorax position (acromion position) were assessed at T0 (baseline), T1 (after 4-week intervention) and T2 (follow-up, 4 weeks after the end of the intervention). Results: A total of 95 older adults (41 in the MTWG and 54 in CG) completed the study and were analyzed. After isolating the effect of correlations among our primary outcomes, our analysis revealed statistically significant between-subject (p < 0.01), within-subject (p < 0.001) and between-within subject differences (p < 0.05) in Pressure Pain Threshold scores in favour of the MTWG. We also detected within-subjects (p < 0.001) and between-within subject differences (p < 0.05) in scores for the Visual Analogue Scale, in favour of the MTWG. These patterns of results remained stable at T2. The 5-times sit-to-stand test (p < 0.01) and the acromion position (p < 0.05) improved at T1 for the MTWG but not at T2. Conclusions: A combined protocol of self-assisted manual therapy and high-intensity walking, compared to high-intensity walking alone, improved musculoskeletal pain, functionality, and posture in older adults in the short term (over one month), with pain reduction maintained in the medium term (at the one-month follow-up). Full article

To address lower limb fatigue in workers engaged in prolonged standing, this study proposes a structural design for a medial-support passive exoskeleton seat. The design incorporates support rods positioned along the medial aspect of the user’s lower limbs and features an adaptive telescopic rod system, enhancing sitting stability and reducing collision risks in workplace environments. Human motion capture technology was used to collect kinematic data of the lower limbs, and a mathematical model of center-of-gravity variation was developed to calculate and optimize the exoskeleton’s structural parameters. Static analysis was performed using ANSYS software (2025 R1) to evaluate the structural integrity of the design. The effectiveness of the exoskeleton seat was validated through surface electromyography (sEMG) experiments, with results showing that the exoskeleton significantly reduces lower limb muscle load by 49.2% to 72.9%. Additionally, force plate experiments demonstrated that the exoskeleton seat improves stability, with a 39.2% reduction in the average displacement of the center of pressure (CoP), confirming its superior postural alignment and balance. The design was also compared with existing exoskeleton chairs, showing comparable or better performance in terms of muscle load reduction, stability, and overall effectiveness. Full article