Search Results (175)

Background: The knee joint performs a vital function in human movement, supporting significant loads and ensuring stability during daily activities. Methods: The objective of this study was to develop and validate a subject-specific framework to model knee flexion–extension by integrating 3D gait data with individualized musculoskeletal (MS) and finite element (FE) models. In this proof of concept, gait data were collected from a 52-year-old woman using Xsens inertial sensors. The MS model was based on the same subject to define realistic loading, while the 3D knee FE model, built from another individual’s MRI, included all major anatomical structures, as subject-specific morphing was not possible due to unavailable scans. Results: The FE simulation showed principal stresses from –28.67 to +44.95 MPa, with compressive stresses between 2 and 8 MPa predominating in the tibial plateaus, consistent with normal gait. In the ACL, peak stress of 1.45 MPa occurred near the femoral insertion, decreasing non-uniformly with a compressive dip around –3.0 MPa. Displacement reached 0.99 mm in the distal tibia and decreased proximally. ACL displacement ranged from 0.45 to 0.80 mm, following a non-linear pattern likely due to ligament geometry and local constraints. Conclusions: These results support the model’s ability to replicate realistic, patient-specific joint mechanics. Full article

Work-related musculoskeletal disorders (WMSDs) are a leading concern in industrial ergonomics, often stemming from sustained non-neutral postures and repetitive tasks. This paper presents a vision-based framework for real-time, frame-level ergonomic risk classification using a lightweight Vision Transformer (ViT). The proposed system operates directly on raw RGB images without requiring skeleton reconstruction, joint angle estimation, or image segmentation. A single ViT model simultaneously classifies eight anatomical regions, enabling efficient multi-label posture assessment. Training is supervised using a multimodal dataset acquired from synchronized RGB video and full-body inertial motion capture, with ergonomic risk labels derived from RULA scores computed on joint kinematics. The system is validated on realistic, simulated industrial tasks that include common challenges such as occlusion and posture variability. Experimental results show that the ViT model achieves state-of-the-art performance, with F1-scores exceeding 0.99 and AUC values above 0.996 across all regions. Compared to previous CNN-based system, the proposed model improves classification accuracy and generalizability while reducing complexity and enabling real-time inference on edge devices. These findings demonstrate the model’s potential for unobtrusive, scalable ergonomic risk monitoring in real-world manufacturing environments. Full article

Background/Objectives: The simulation of human movement offers transformative potential for the design of medical devices, particularly in understanding the cause–effect dynamics in individuals with neurological or musculoskeletal impairments. This study presents a simulation-driven framework to determine the optimal ankle–foot orthosis (AFO) stiffness for mitigating the risk of ankle sprains due to excessive subtalar inversion during high-impact activities, such as landing from a free fall. Methods: We employed biomechanical simulations to assess the influence of translational stiffness on subtalar inversion control, given that inversion angles exceeding 25 degrees are strongly correlated with injury risk. Simulations were conducted using a musculoskeletal model with and without a passive AFO; the stiffness varied in three anatomical directions. A Design of Experiments (DoE) approach was utilized to capture nonlinear interactions among stiffness parameters. Results: The results indicated that increased translational stiffness significantly reduced inversion angles to safer levels, though direction–dependent effects were noted. Based on these insights, we developed a 4D visualization tool that integrates simulation data with an interactive color–coded interface to depict ”safe design” zones for various AFO stiffness configurations. This tool supports clinicians in selecting stiffness values that optimize both safety and functional performance. Conclusions: The proposed framework enhances clinical decision-making and engineering processes by enabling more accurate and individualized AFO designs. 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

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

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

The current pre-clinical testing standards for total hip replacements (THRs), ISO standards, use simplified loading waveforms that do not fully replicate real-world biomechanics. These standards provide a benchmark of data that may not accurately predict in vivo wear, necessitating the evaluation of physiologically relevant loading conditions. Previous studies have incorporated activities of daily living (ADLs) such as walking, jogging and stair negotiation into wear simulations. However, these studies primarily used simplified adaptations that increased axial forces and applied accelerated sinusoidal waveforms, rather than fully replicating the complex kinematics experienced by THR patients. To address this gap, this study applied patient-derived ADL profiles—jogging and stair negotiation—using a three-station hip simulator, obtained through 3D motion analysis of total hip arthroplasty patients, processed via a musculoskeletal multibody modelling approach to derive realistic hip contact forces (HCFs). The results indicate that jogging significantly increased wear rates compared to the ISO walking gait waveform, with wear increasing from 15.24 ± 0.55 to 28.68 ± 0.87 mm³/Mc. Additionally, wear was highly sensitive to changes in lubricant protein concentration, with an increase from 17 g/L to 30 g/L reducing wear by over 60%. Contrary to predictive models, stair descent resulted in higher volumetric wear (8.62 ± 0.43 mm³/0.5 Mc) compared to stair ascent (4.15 ± 0.31 mm³/0.5 Mc), despite both profiles having similar peak torques. These findings underscore the limitations of current ISO standards in replicating physiologically relevant wear patterns. The application of patient-specific loading profiles highlights the need to integrate ADLs into pre-clinical testing protocols, ensuring a more accurate assessment of implant performance and longevity. Full article

Background and Aims: Esophageal atresia (EA) and tracheoesophageal fistula (TEF) are rare but serious congenital anomalies requiring early surgical intervention. Over the past two decades, minimally invasive surgical (MIS) approaches—particularly thoracoscopic repair—have gained traction, aiming to reduce postoperative morbidity while maintaining surgical efficacy. Objective: This narrative review provides a comprehensive overview of the evolution and current status of MIS techniques for EA/TEF, assessing their clinical outcomes, technical challenges, and implications for patient care. Methods: A structured literature search was conducted to identify clinical studies, reviews, and reports on thoracoscopic, robotic-assisted, and endoscopic approaches to EA/TEF. Emerging adjuncts, including tissue engineering, botulinum toxin use, and magnet-assisted anastomosis, were also reviewed. Results: Thoracoscopic repair has demonstrated comparable anastomotic success rates to open surgery (approximately 85–95%) with significantly reduced rates of musculoskeletal complications, such as scoliosis and chest wall deformities (reported in less than 10% of cases, compared to up to 40% in open approaches). Robotic-assisted and endoscopic-assisted techniques have enabled improved visualization and precision in anatomically challenging cases, although their use remains limited to high-resource centers with specialized expertise. Common postoperative complications include anastomotic stricture (30–50%), gastroesophageal reflux disease (35–70%), and respiratory morbidity, necessitating long-term multidisciplinary follow-up. Recent innovations in simulation-based training and bioengineered adjuncts have facilitated safer MIS adoption in neonates. Conclusions: Minimally invasive techniques have improved the surgical management of EA/TEF, though challenges remain regarding technical complexity, training, and resource availability. Continued innovation and collaborative research are essential for advancing care and ensuring optimal outcomes for affected infants. Full article

Background: An exoskeleton and its wearer form a mutually dependent biomechanical system, where design choices for the exoskeleton can affect the wearer in complex and often unforeseeable ways, and this makes exoskeleton design challenging. Advanced simulation methods provide an insight into the consequences of design choices, but such analysis is usually employed towards the end of the design process. This paper demonstrates an option for musculoskeletal simulation to be used already in the conceptual design phase. Methods: We present the workflow by means of an example of box lifting. We show that the mathematical algorithm underlying the solution of the redundant equilibrium equations in musculoskeletal modeling has a structure that can be exploited to gain information about ideal actuator forces for an exoskeleton supporting the selected work task. Results: Based on the identified forces, passive or active actuators can be selected, and control strategies can be devised. Conclusions: We conclude that this methodology can save design cycles and improve exoskeleton development. Full article

Work-related musculoskeletal disorders (WMSDs) are common in the agricultural industry due to repetitive tasks, like plant transplanting, which involve sustained bending, squatting, and awkward postures. This study uses wearable sensors to evaluate human biomechanics during simulated transplanting and assesses the potential of collaborative robot (cobot) assistance to reduce physical strain. Sixteen participants performed transplanting tasks under manual and cobot-assisted conditions. Kinematic and electromyographic (EMG) data were collected using Xsens motion capture and Trigno EMG systems. Cobot assistance significantly reduced the segment velocity and acceleration in key spinal regions (L5/S1, L1/T12, T1/C7), indicating lower dynamic spinal loading. It also altered muscle activation, decreasing biceps brachii use while increasing activation in stabilizing muscles such as the flexor carpi radialis, brachioradialis, and upper trapezius. Task duration decreased by 59.46%, suggesting improved efficiency. These findings highlight cobots’ potential to enhance ergonomic outcomes by encouraging controlled movements and reducing postural stress. However, the shift in muscle activation underscores the need for task-specific cobot tuning. This research supports the use of integrated IMU and EMG systems to inform cobot design and enable real-time biomechanical monitoring in labor-intensive settings. Full article

Integrating artificial intelligence (AI) and mixed reality (MR) into orthopedic education has transformed learning. This review examines AI-powered platforms like Microsoft HoloLens, Apple Vision Pro, and HTC Vive Pro, which enhance anatomical visualization, surgical simulation, and clinical decision-making. These technologies improve the spatial understanding of musculoskeletal structures, refine procedural skills with haptic feedback, and personalize learning through AI-driven adaptive algorithms. Generative AI tools like ChatGPT further support knowledge retention and provide evidence-based insights on orthopedic topics. AI-enabled platforms and generative AI tools help address challenges in standardizing orthopedic education. However, we still face many barriers that relate to standardizing data, algorithm evaluation, ethics, and the curriculum. AI is used in preoperative planning and predictive analytics in the postoperative period that bridges theory and practice. AI and MR are key to supporting innovation and scalability in orthopedic education. However, technological innovation relies on collaborative partnerships to develop equitable, evidence-informed practices that can be implemented in orthopedic education. For sustained impact, innovation must be aligned with pedagogical theories and principles. We believe that orthopedic medical educators’ future critical role will be to enhance the next generation of competent clinicians. Full article

Background and Objectives: Although virtual reality (VR) has been shown to be effective in rehabilitation through motor learning principles, its impact on upper extremity function, particularly in the context of console use, remains unclear. Materials and Methods: This study aimed to investigate the effects of VR-based task-oriented movement on the upper extremity of healthy individuals. A total of 26 healthy individuals performed task-oriented movements in both real and virtual environments in a randomized order. All participants completed a single session of task-oriented movements using a VR Goggle system in a virtual setting. Physiotherapists designed immersive VR-based experiences and 3D screen-based exergames for this study. Upper extremity function was assessed using several measures: joint position sense (JPS) of the wrist and shoulder was evaluated using a universal goniometer, reaction time was measured via a mobile application, and gross manual dexterity was assessed using the box-and-block test (BBT). Evaluations were conducted before and after the interventions. Results: The results showed that JPS remained similar between conditions, while BBT performance improved in both groups. However, the reaction time increased significantly only after VR intervention (p < 0.05). No significant period or carryover effects were observed across the parameters. These findings suggest that VR-based task-oriented training positively influences reaction time and supports hand function. Moreover, VR systems that simulate joint position sense similar to real-world conditions may be beneficial for individuals with musculoskeletal motor deficits. Conclusions: These results highlight the potential for integrating VR technology into rehabilitation programs for patients with neurological or orthopedic impairments, providing a novel tool for enhancing upper extremity function and injury prevention strategies. Full article

Introduction: Dental professionals are susceptible to musculoskeletal disorders due to unphysiological postures during treatment, which can be mitigated by the choice of a work chair to improve ergonomic working posture. Methods: In this study, the influence of five different work chairs on the ergonomic risk assessment using RULA and the working behaviour of 22 right-handed dentists was investigated. To this end, dental treatment was simulated on a phantom head, with the body posture recorded using an inertial motion capture system. The resulting kinematic data were converted into a continuous RULA scheme, and statistical methods (Friedman test with Conover–Iman comparisons and statistical parametric mapping) were used to compare the chairs. The significance level was set at p ≤ 0.05. Results: The RULA analysis revealed no significant differences between the task chairs that were tested; however, it should be noted that all of the task chairs exhibited an increased ergonomic risk (RULA ≥ 5), which indicates an increased risk of musculoskeletal disorders. Significant multiple comparisons (SPM analysis) between the selected chairs of the relative occurrence of total joint angles were between Chair 1 vs. 5 (p ≤ 0.03 for shoulder left flexion–extension), Chair 2 vs. 4 (p ≤ 0.03 for shoulder right flexion–extension), Chair 1 vs. 3 (p ≤ 0.03 for trunk right flexion–extension, 0.04 for trunk lateral flexion and 0.05 for elbow left flexion–extension), Chair 3 vs. 4 (p ≤ 0.05 for shoulder left flexion–extension and 0.01 shoulder right flexion–extension), and between Chair 2 vs. 3 (p ≤ 0.05 for elbow left flexion–extension). Discussion: The study’s findings indicate that the selection of work chairs did not have a significant impact on the ergonomic risk, which remained consistently high across all the chairs. Nevertheless, the analysis of joint angles demonstrated that the Ghopec chair was more frequently associated with greater joint angles, with only a few significant deviations. However, it should be noted that these significant differences in joint angles occurred only sporadically, did not demonstrate a clear and consistent trend across all the chairs, and have no clinical impact. Overall, the results confirm that the working posture of the dentists has a potential risk of developing musculoskeletal disorders, while the ergonomic design of the work chairs plays a rather subordinate role. Full article

The analysis of human gait is a cornerstone in diagnosing and monitoring a variety of neuromuscular and orthopedic conditions. Recent technological advancements have paved the way for innovative methodologies that combine multimodal neuroimaging and eXtended Reality (XR) technologies to enhance the precision and applicability of gait analysis. This review explores the state-of-the-art solutions of an advanced gait analysis approach, a multidisciplinary concept that integrates neuroimaging, extended reality technologies, and sensor-based methods to study human locomotion. Several wearable neuroimaging modalities such as functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), commonly used to monitor and analyze brain activity during walking and to explore the neural mechanisms underlying motor control, balance, and gait adaptation, were considered. XR technologies, including virtual, augmented, and mixed reality, enable the creation of immersive environments for gait analysis, real-time simulation, and movement visualization, facilitating a comprehensive assessment of locomotion and its neural and biomechanical dynamics. This advanced gait analysis approach enhances the understanding of gait by examining both cerebral and biomechanical aspects, offering insights into brain–musculoskeletal coordination. We highlight its potential to provide real-time, high-resolution data and immersive visualization, facilitating improved clinical decision-making and rehabilitation strategies. Additionally, we address the challenges of integrating these technologies, such as data fusion, computational demands, and scalability. The review concludes by proposing future research directions that leverage artificial intelligence to further optimize multimodal imaging and XR applications in gait analysis, ultimately driving their translation from laboratory settings to clinical practice. This synthesis underscores the transformative potential of these approaches for personalized medicine and patient outcomes. Full article

Background: Low-field Magnetic Resonance Imaging (MRI) (fields below 0.5 T) has received increasing attention since the images produced have been shown to be diagnostically equivalent to high-field MR images for specific applications, such as musculoskeletal studies. In recent years, low-field MRI has made great strides in clinical relevance due to advances in high-performance gradients, magnet technology, and the development of organ-specific radiofrequency (RF) coils, as well as advances in acquisition sequence design. For achieving optimized image homogeneity and signal-to-noise Ratio (SNR), the design and simulation of dedicated RF coils is a constraint both in clinical and in many research studies. Methods: This paper describes the application of a numerical full-wave method based on the finite-difference time-domain (FDTD) algorithm for the simulation and the design of birdcage coils for musculoskeletal low-field MRI. In particular, the magnetic field pattern in loaded and unloaded conditions was investigated. Moreover, the magnetic field homogeneity variations and the coil detuning after an RF shield insertion were evaluated. Finally, the coil inductance and the sample-induced resistance were estimated. Results: The accuracy of the results was verified by data acquired from two lowpass birdcage prototypes designed for musculoskeletal experiments on a 0.18 T open MR clinical scanner. Conclusions: This work describes the capability of numerical simulations to design RF coils for various scenarios, including the presence of electromagnetic shields and different load conditions. Full article