Search Results (147)

The growing demand for elderly and bedridden patient care in hospitals, nursing homes, and long-term care facilities has increased the need for safe and efficient repositioning methods. Repositioning immobile patients is essential for preventing pressure injuries and other complications associated with prolonged immobility. However, this task is still commonly performed manually using bed sheets, pillows, and similar support aids, making it physically demanding and increasing the risk of musculoskeletal injury among caregivers. This paper presents a two-stage soft growing robot for safe and assisted patient repositioning from a supine posture to a side-lying position. The proposed mechanism consists of two soft pneumatic chambers with distinct roles. The first chamber enables pressure-driven eversion, allowing the robot to deploy smoothly beneath the patient with minimal friction. The second chamber is then pressurized to generate the lifting and rolling motion required for repositioning. A first-principles dynamic model of the pressure-driven vine robot is developed by integrating pneumatic supply dynamics, internal pressure evolution, and tip-extension mechanics within a Lagrangian framework. Based on this model, a robust multi-stage nonlinear model predictive control strategy is formulated to regulate deployment beneath the patient under parameter uncertainty. The rolling dynamics of the second stage are also analyzed to determine the minimum pressure required for repositioning as a function of patient weight and roll angle. Simulation results show that the proposed controller achieves smooth and accurate deployment while satisfying input and state constraints under uncertainty. The rolling analysis further indicates that the required pressure increases with patient weight and decreases with roll angle. These findings demonstrate the potential of the proposed mechanism to reduce caregiver effort and enable safe, controlled patient repositioning. Full article

Background: Walking a dog on-leash is a common activity for a large portion of our society. Many dogs consistently pull on the leash, which transmits potentially dangerous forces to the human body. The purpose of this in silico study was to determine the effects of dog-leash tension and human posture on lumbar compression, and how comparable the effects of dog walking on lumbar compression are to lifting, an activity known to contribute to low back pain. Methods: Dog-leash simulations were performed with 50–300 N directed along the arm segment of a static three-dimensional musculoskeletal model across a range of trunk segment and shoulder joint angles. Lifting simulations were performed across a range of test postures with the model holding a 50–300 N weight close to the ground. Lumbar compression was computed for each simulation using McGill’s polynomial equation and compared with the 3400 N cutoff used to develop occupational safety guidelines. Results: Lumbar compression increased as trunk segment flexion increased for all simulation conditions. With 200 N of leash tension, lumbar compression exceeded 3400 N for all postures with 25° or more of trunk segment flexion. When lifting 150 N, lumbar compression exceeded 3400 N for all postures with shank segment angle of 80° or greater and knee flexion angle of 100° or less. Conclusions: Our in silico results suggest that dog owners should seek intervention if their dog routinely pulls on the leash with a force of 200 N or greater and should attempt to lean backward when resisting leash pulling to reduce lumbar compression and injury risk. Full article

Background: Hip implant fractures are rare, yet difficult to correct once they occur. For cemented implants, fracture is often associated with increased stresses at the implant stem when proximal regions of the implant have debonded. While deterministic analyses offer predictive power by using averages for model inputs, averages fail to capture the variability inherent in device manufacturing and musculoskeletal biology. This study developed a probabilistic finite element model of a debonded hip implant to better account for some of these variabilities to predict the most likely failure mode. The hypothesis was that fatigue would be more likely to occur than overloading. Methods and Materials: Monte Carlo sampling generated 1000 simulations varying the material elastic modulus (implant, cement, and bone) and loading magnitude at stance phase of the gait. The resultant distributions of maximum von Mises stress at the stem were compared to distributions for failure properties in the literature. Results: The analysis found the likelihood of the implant failing due to overloading was remote. In contrast, fatigue failure had a 99.4% chance of occurring. Fracture mechanics predicted that the debonded implant would reach critical flaw length between 1.8 and 26.4 months, with a mean of 7.2 months. Conclusions: The results show good agreement with the findings of the case study the model was based on, particularly in predicting the location of failure and fatigue life. The results of this study provide a framework for developing future decision-making tools that ultimately may assist clinicians in deciding when interventions are necessary to minimize the risk of implant or periprosthetic fracture. Full article

Background/Objectives: Digital twin technology has attracted growing attention in orthopedics for its potential to support patient-specific modeling, simulation, and data-driven clinical decision-making. However, despite the rapid growth in the literature, clinical adoption remains limited, and the term “digital twin” is often applied inconsistently to fundamentally different technological approaches. To establish a clear, function-oriented definition and taxonomy of digital twins in orthopedics, to map current applications across subspecialties, and to critically assess the level of clinical evidence supporting their use. Methods: A structured narrative review was conducted using targeted searches of major bibliographic databases (PubMed, Web of Science, Scopus), publisher platforms, and complementary semantic search tools. The retrieved literature was interpreted using a functional analytical framework focusing on patient specificity, data integration, intended clinical role, and degree of clinical validation. Rather than conducting a formal, systematic appraisal, the aim was to provide a concept-driven synthesis of the field and identify patterns of use, maturity, and translational limitations. Results: Most reported orthopedic digital twin implementations appear to represent static patient-specific simulations supported primarily by preclinical or feasibility-level evidence. Monitoring-oriented digital twins have been more commonly reported in spine care, rehabilitation, and sports medicine, enabling longitudinal assessments but offering limited predictive or decision-support value. Decision-oriented digital twins are uncommon, yet they seem to be the most clinically mature type described in the current literature; so far, only one randomized controlled trial has demonstrated improved decision quality in arthroplasty care. Fully integrated hybrid or closed-loop digital twins remain largely experimental. Conclusions: Digital twin technology in orthopedics is characterized by substantial conceptual heterogeneity and limited clinical validation. Near-term clinical impact is most likely to arise from narrowly focused, decision-oriented, and monitoring-based digital twins, although this projection remains dependent on further clinical validation. Greater definitional clarity, functional transparency, and rigorous clinical evaluation are essential to support meaningful translation into routine orthopedic practice. Full article

Playing-related musculoskeletal disorders (PRMDs) are highly prevalent among bass-guitar players due to sustained asymmetrical postures, repetitive finger movements, and prolonged support of instrument weight. This study proposes a Human Digital Biomechanical Twin-driven, simulation-based approach to optimize bass-guitar support systems, integrating biomechanical modelling, motion capture, and musculoskeletal simulation. A preliminary survey among 63 Italian bass-guitar players was performed to define the experimental conditions regarding posture, instrument type, and session duration. Fifteen experienced bassists participated in laboratory trials using motion capture and postural assessment tools, including MediaPipe Pose, RULA, and AnyBody Modelling System. Baseline results highlighted significant activation of the trapezius and spinal extensor muscles (19–26% MVC), confirming high ergonomic risk. Three alternative support configurations were digitally simulated, revealing that a three-point harness system (bilateral shoulder straps plus thoracic anchoring) reduced spinal stabilizer activation by 15–25% across four anthropometric percentiles. Experimental validation confirmed enhanced comfort, reduced fatigue, and improved instrument stability, with the majority of participants preferring the ergonomic configuration. These findings demonstrate the feasibility of a simulation-based, prospective, and human-centred ergonomic design framework, offering a scalable methodology to compare and optimize adaptive instrument-support systems before physical prototyping. Full article

Biomechanics sits at the interface of engineering and medical sciences, offering essential insight into how tissues, organs, and biological systems respond to mechanical loading. This review brings together recent advances in musculoskeletal and cardiovascular biomechanics, illustrating how experimental techniques, computational modeling, and multiscale analysis are used to characterize load transfer, tissue deformation, fatigue, and injury mechanisms. In musculoskeletal applications, predictive simulations, wearable sensing technologies, and neuromechanical assessment tools support improved injury prevention, rehabilitation planning, and assistive device development. In the cardiovascular domain, patient-specific modeling, fluid–structure interaction analyses, and advanced imaging approaches clarify how hemodynamics, vessel wall mechanics, and device–tissue interactions influence disease progression, implant performance, and therapeutic outcomes. Emerging technologies including artificial intelligence, machine learning, digital twin frameworks, biofabrication, soft robotics, and self-powered sensing are enabling data-driven, real-time, and personalized interventions that connect mechanistic understanding with clinical practice. Despite these advances, challenges remain in accounting for individual variability, integrating multiscale data, and translating computational predictions into clinically validated solutions. By emphasizing interdisciplinary strategies that unite biomechanics, computational analytics, and innovative device engineering, this review outlines a pathway toward predictive, patient-centered healthcare and next-generation therapeutic and rehabilitation solutions. Full article

Scoliosis is a three-dimensional spinal deformity that may affect musculoskeletal alignment, respiratory mechanics, and neuromotor control. Rigid thoraco-lumbo-sacral orthoses (TLSOs) remain the primary conservative treatment during skeletal growth. Most brace systems rely on three-point pressure mechanisms that primarily generate lateral compression forces, while the contribution of shear components to corrective biomechanics has been insufficiently quantified. This study presents the experimental and analytical validation of the Canali Front-Push Orthopedic Brace, a rigid orthotic system designed to generate controlled compressive and shear forces through a frontal thrust mechanism and anterior rib cage engagement. By applying anterior force, the device reduces the frontal-plane lever arm, thereby limiting the mechanical moment that contributes to transverse plane rotation. An instrumented four-segment torso model derived from the internal CAD geometry of the brace was developed to independently measure upper compression, lower compression, and intersegmental shear forces. Controlled misalignment conditions (0 mm, 2 mm, and 4 mm) were introduced to simulate asymmetric engagement of the orthosis. Three load cell configurations (200 N and 500 N capacity) were tested. Mechanical endurance of the rack–latch fastening system was also evaluated. A predictive shear–misalignment relationship was derived and experimentally validated. Peak compressive forces reached approximately 370 N, while shear forces increased from less than 40 N under symmetric alignment (D0) to approximately 170 N under maximal misalignment (D4). Shear activation demonstrated near-linear proportionality to imposed geometric asymmetry (R² > 0.94). Following cyclic loading, the fastening system stabilized mechanically around 300 N. Measurement repeatability showed a coefficient of variation below 5%. These findings demonstrate that the brace produces predictable and controllable shear activation while maintaining high mechanical repeatability. The results provide a quantitative biomechanical framework for understanding shear-induced corrective mechanics in scoliosis bracing and support future studies integrating computational modeling and clinical validation. The proposed mechanical framework may contribute to the development of next-generation orthotic strategies aimed at controlling spinal rotation through vector modulation rather than purely compressive correction. Full article

Platelet-rich plasma (PRP) therapies are increasingly used in musculoskeletal and regenerative medicine; however, substantial variability in reported outcomes persists even when similar preparation protocols are employed. In quantitative terms, PRP preparation can be interpreted as a stochastic process in which uncertainty propagates through multiple biological and technical inputs. Herein we propose a probabilistic framework to quantify variability in the platelet dose delivered (PDD) using Monte Carlo simulations. The platelet dose was formulated as a random variable defined by a multiplicative model involving the platelet count (modeled as a normal distribution), concentration factor (log-normal), injected volume (uniform), and processing efficiency (beta). Input parameters were represented by probability distributions derived from ranges reported in the literature, and uncertainty propagation was explored through 100,000 Monte Carlo iterations. The resulting simulations revealed a wide dispersion in PDD, characterized by a right-skewed distribution with a median of 3.1 × 10⁹ platelets and an interquartile range of 1.9 × 10⁹ platelets, yielding a coefficient of variation exceeding 50%. Sensitivity analysis based on variance-based global sensitivity measures (Sobol indices) identified the injected volume and concentration factor as the dominant contributors to output variability, with substantial interaction effects between these parameters accounting for a considerable portion of total variance. The baseline platelet count and processing efficiency had comparatively smaller effects. These results demonstrate how probabilistic modeling can clarify the sources of variability in PRP preparation and provide a generalizable framework for uncertainty quantification in multiplicative biomedical systems. Full article

Background: Multibody models are essential for studying knee joint mechanics, but their reliability and subsequent clinical utility are limited by uncertainties in ligament and contact parameters. Currently, no consensus exists on which parameters to prioritize or which statistical distributions best establish model credibility. Objectives: This scoping review aims to systematize reported uncertainty values for ligament and contact parameters in multibody models of the natural knee to identify trends and research gaps. Methods: Following PRISMA-ScR guidelines, a systematic search was conducted across PubMed, Scopus, and Web of Science. Methodological quality was assessed using a customized 13-item checklist, and the data were synthesized via a narrative approach by charting parameter types, quantification methods, and model structures. Results: In total, 19 articles were included (out of 494 identified), showing a wide variability in uncertain parameter types, values, and modeling approaches. Ligaments were typically represented as deformable cables with quadratic–linear behavior, while articular contact utilized elastic foundation formulations or mechanisms. Standard deviations of 30% of the mean for ligament stiffness and 0.02 for reference strain (typically modeled within Gaussian distributions) were the most frequently quantified uncertain parameters. Geometric uncertainties for ligament attachment points varied widely, ranging from 1.0 to 5.0 mm. Idealized contact geometry also varied within 2.5 mm for linear coordinates and 15° for angular coordinates. Conclusions: Wide variability and inconsistent reports highlight a need for standardized definitions of parameter uncertainty in multibody knee modeling to improve reproducibility of musculoskeletal knee simulations and ensure a reliable transition of these models into clinical practice. Full article

The closed kinetic chain is an essential movement method for humans in daily life, and is also important as a training method. However, there have been few studies focusing on the hip adductor muscles. We used electromyography to measure the muscle activity of the hip adductor muscles during walking and standing movements as part of daily living activities, as well as bicycle ergometer exercise and squats. Concerning the role of the adductor muscles, they are thought to stabilize the pelvis during the unilateral support phase when walking, and to act as hip extension and hip alignment adjustment during cycle ergometer exercise. By using electromyography and inertial sensors, the results of this study showed that wearable technologies can be used to quantify neuromuscular function during closed kinetic chain movements. The results serve as a reference for the development of rehabilitation devices, assistive technologies, and computational models that need the simulation of hip joint mechanics. Linking muscle activity data to engineering-based strategies enables precise musculoskeletal assessment and intervention beyond biological observation. Full article

Computational simulation is a valuable tool for advancing personalized ergonomics. This study evaluated the ability of musculoskeletal simulation to estimate individual lumbar loading during manual lifting tasks representative of construction activities. Fifty-six Colombian adults were recruited to reflect national anthropometric distributions and grouped by BMI and stature. Participants performed two standardized lifting tasks with a 10 kg load: symmetric lifting from the floor to xiphoid height and lateral lifting from a 0.40 m surface to shoulder height with contralateral transfer. Whole-body kinematics and ground reaction forces were processed in OpenSim software using the validated model to estimate L5–S1 compression and shear forces. Results showed a moderate association between lumbar compression and body weight, while shear forces exhibited low correlations with kinematic variables. Subject-specific scaled models revealed substantial inter-individual differences in lumbar loading related to lifting technique and anthropometric characteristics, highlighting the potential of musculoskeletal simulation for personalized risk assessment in construction. Full article

Background: Pumping in Laser-class sailing is a dynamic propulsion technique used in marginal wind conditions and characterized by repetitive, coordinated oscillations of the sailor–sail system. Despite its practical relevance, its biomechanical and ergonomic demands remain insufficiently characterized. Methods: A mixed-methods framework was applied combining questionnaire data, kinematic analysis, ergonomic assessment, and musculoskeletal modelling. Thirty-six competitive Laser sailors completed a Borg CR-10-based questionnaire on perceived discomfort/fatigue across body regions at predefined time points (during pumping, immediately after training, and the following day). A controlled land-based multi-angle video acquisition was used to reconstruct a standardized pumping posture and parameterize a digital human model in DELMIA^® for postural/kinematic analysis. Ergonomic risk was assessed using REBA, and muscle activity was estimated using the AnyBody^® Modeling System (simulation-derived normalized muscle activity across 129 muscles). Results: the simulation identified high neuromuscular demand in the trunk and shoulder complex, with several deep trunk stabilizers and the left latissimus dorsi reaching 100% modeled normalized muscle activity. Marked lateral asymmetry was observed, with right-sided trunk dominance and left-sided shoulder dominance. Kinematic analysis showed substantial joint excursions, with large lumbar motion amplitudes, while REBA yielded a score of 11 (Very-High Risk). Questionnaire data indicated a high prevalence of pumping-related musculoskeletal discomfort (72.2%), most frequently involving the lower back, shoulders, and knees. A dissociation was observed between modeled muscle activity and perceived fatigue, with the lower limbs rated as most fatigued despite lower modeled activation than the trunk. Conclusions: Findings identify the deep trunk stabilizers, latissimus dorsi, and lower extremities as key regions involved in pumping, with marked lateral asymmetry and high ergonomic risk. They support targeted training, injury-prevention, and ergonomic strategies to improve performance and reduce injury risk in competitive sailing. Full article

Background and Objectives: Surface electromyography (EMG) is widely used to assess muscle activation. However, direct interpretation of its functional biomechanical consequences remains challenging. This study aimed to develop and evaluate an EMG-driven musculoskeletal simulation framework for investigating how controlled modifications of muscle activation patterns influence joint-level biomechanics in the upper limb. The objective was not to reproduce specific clinical pathologies but to enable systematic virtual scenario analysis of activation-dependent movement alterations. Materials and Methods: Surface EMG signals were recorded from five healthy adults (3 males, 2 females; age 22 ± 1 years) during cyclic elbow flexion/extension tasks using a wireless system (sampling frequency: 2000 Hz). Processed and normalized EMG envelopes were directly applied as prescribed neural inputs in forward dynamic simulations implemented in OpenSim, without optimization-based muscle recruitment. Controlled virtual scenarios were generated through parametric modification of activation signals to represent reduced activation capacity, increased antagonist co-activation, spasticity-like activation modulation, and tremor-like oscillatory modulation. Joint kinematics, joint moments, and movement stability were evaluated. A Movement Quality Index (MQI) was introduced as a comparative research metric integrating biomechanical performance indicators. Simulations were deterministic and analyzed descriptively. Results: Distinct activation modifications produced characteristic kinematic and kinetic responses. Reduced activation capacity decreased simulated joint moment output, increased co-activation altered joint moment timing and mechanical stability, and tremor-like oscillatory modulation generated periodic fluctuations in joint kinematics and kinetics. The MQI enabled quantitative differentiation between simulated scenarios and severity levels within the controlled modelling framework. Conclusions: The proposed EMG-driven forward dynamic simulation framework provides a methodological platform for controlled virtual scenario analysis of activation-dependent biomechanical changes. The findings highlight the sensitivity of joint-level mechanics to altered muscle activation patterns, within the deterministic modelling environment. The framework is intended for research-oriented biomechanical investigation and hypothesis testing rather than direct clinical diagnosis of neuromuscular disorders. Full article

Forestry, nursery, and planting tasks involve repetitive trunk flexion, squatting, and kneeling, as well as manual handling, increasing musculoskeletal load, and the need for mobility-related safety measures. Passive exoskeletons could mitigate postural exposure and reduce the overall body workload. We conducted a preliminary study (n = 14) to test the feasibility of a protocol and estimated model- and task-specific trends during standardized simulated nursery activities in a laboratory setting. Participants simulated planting and seeding tasks (loads of 0.5–2 kg) and material handling and preparation tasks (loads of 5–15 kg) without an exoskeleton (No-EXO) and with three passive models (EXO 1–EXO 3). EXO 3 was excluded from the planting tasks for feasibility reasons. Whole-body kinematics were recorded using an IMU-based motion capture system and converted into time-based ergonomic exposure outcomes (OWAS and RULA). Physiological load was monitored via heart-rate (HR) measurements. Compared to the No-EXO condition, exoskeleton use shifted posture exposure towards lower-risk categories. The largest improvements were observed with EXO 2 and EXO 3 during material handling (OWAS: −18%/−20%; RULA action-level reduction: −25%/−39%) and with EXO 2 during planting/seeding (OWAS: −15%; RULA: −26%). HR_max did not increase across tasks or conditions and HR tended not to rise with higher workload when exoskeletons were used. Overall, the results suggest positive ergonomic and workload trends related to the model and tasks. Field validation on uneven terrain with full personal protective equipment and harness integration is needed to confirm usability and support and to define implementation requirements (fit, compatibility with PPE, and safe-use conditions). Full article

Background/Objectives: Quasi-static inverse dynamics is widely used in biomechanical analyses due to its computational simplicity; however, neglecting inertial effects may introduce joint-specific torque estimation errors during dynamic movements. The purpose of this study was to quantify torque estimation errors introduced by quasi-static assumptions during bodyweight squats performed at different movement frequencies. Methods: A planar MATLAB-based (version R2022a) musculoskeletal model incorporating standard anthropometric parameters was developed to simulate squat motions at 1.00, 0.75, 0.50, and 0.25 Hz. Joint torques calculated using quasi-static inverse dynamics were compared with fully dynamic inverse dynamics at the ankle, knee, and hip. Model agreement was evaluated using Root Mean Square Error (RMSE), normalized percentage error relative to peak dynamic torque, and bootstrapped 95% confidence intervals (CI). Results: Quasi-static modeling produced negligible torque estimation errors at the ankle and knee across all movement frequencies, with percentage errors consistently below 0.1% and narrow confidence intervals. In contrast, the hip joint demonstrated a clear frequency-dependent underestimation of torque when inertial effects were neglected. At 1.00 Hz, the hip RMSE reached 14.4 Nm, corresponding to 14.01% of peak dynamic torque (95% CI: 13.97–14.06%). Error magnitude increased systematically with movement speed. Conclusions: The validity of quasi-static inverse dynamics strongly depends on joint location and movement frequency. While quasi-static models are appropriate for ankle and knee torque estimation during moderate-speed squats, accurate hip torque assessment during faster squats requires full dynamic modeling. These findings provide quantitative benchmarks to inform model selection in biomechanical research, rehabilitation engineering, and assistive device design. Full article