Search Results (96)

Accurate, subject-specific estimation of cervical muscle forces is a critical prerequisite for advancing spinal biomechanics and clinical diagnostics. However, this task remains challenging due to substantial inter-individual anatomical variability and the invasiveness of direct measurement techniques. In this study, we propose a novel data-driven biomechanical framework that addresses these limitations by integrating massive-scale personalized musculoskeletal simulations with an efficient Feedforward Neural Network (FNN) model. We generated an unprecedented dataset comprising one million personalized OpenSim cervical models, systematically varying key anthropometric parameters (neck length, shoulder width, head mass) to robustly capture human morphological diversity. A random subset was selected for inverse dynamics simulations to establish a comprehensive, physics-based training dataset. Subsequently, an FNN was trained to learn a robust, nonlinear mapping from non-invasive kinematic and anthropometric inputs to the forces of 72 cervical muscles. The model’s accuracy was validated on a test set, achieving a coefficient of determination (R²) exceeding 0.95 for all 72 muscle forces. This approach effectively transforms a computationally intensive biomechanical problem into a rapid tool. Additionally, the framework incorporates a functional assessment module that evaluates motion deficits by comparing observed head trajectories against a simulated idealized motion envelope. Validation using data from a healthy subject and a patient with restricted mobility demonstrated the framework’s ability to accurately track muscle force trends and precisely identify regions of functional limitations. This methodology offers a scalable and clinically translatable solution for personalized cervical muscle evaluation, supporting targeted rehabilitation and injury risk assessment based on readily obtainable sensor data. Full article

Background: Temporomandibular disorders (TMDs) are associated with altered masticatory muscle function and pain. Although electromyographic parameters have been extensively studied, the rate of force development (RFD) remains an underexplored biomarker in this context. Objective: Analyze the RFD differences in women diagnosed with and without TMD. As a secondary outcome, the masseter and temporalis muscle pre-activation values were compared between groups based on the biting force onset. Additionally, neuromuscular efficiency analysis was also performed. Methods: A retrospective analysis of 62 medical records (41 with TMD, 21 controls) was conducted. Electromyographic activity and bite force were measured during three 5-s maximal biting tasks using synchronized surface electromyography (sEMG) and a laboratory-grade load cell. RFD was computed from force–time curves. Muscle pre-activation was assessed based on sEMG activity immediately preceding contraction onset. Results: The TMD group showed a significantly smaller RFD (mean = 85.5 N/s) compared to controls (mean = 109.0 N/s; p = 0.03; Cohen’s d = 0.5). No significant differences were found in neuromuscular efficiency and pre-activation or post-activation levels of the masseter and temporalis muscles between groups. Conclusions: RFD distinguishes women with TMD from healthy controls and may represent a sensitive biomechanical marker of neuromuscular adaptation in TMD, although confirmatory studies are needed. The absence of neuromuscular efficiency and pre-activation differences suggests compensatory neuromuscular mechanisms. Further prospective studies are needed to validate these findings and explore clinical applications. Full article

Approximately two-thirds of athletes who are submitted to Anterior Cruciate Ligament Reconstruction (ACLR) never return to their preinjury level of performance, potentially due to muscle strength deficiencies or altered loading patterns during landing or jumping tasks. This study aimed to estimate individual muscle forces during a double-leg drop jump task, and assess sagittal plane between-limb asymmetries in muscle forces and ground reaction forces using a musculoskeletal modelling approach, in athletes who underwent ACLR. Thirty male field-sport athletes (age: 18–35 years; mass: 84.3 ± 12.3 kg; height: 180.2 ± 8.4 cm) post-ACLR (39.8 ± 3.9 weeks) using patellar or quadriceps tendon grafts were tested. Scaled musculoskeletal models were implemented in OpenSim, and muscle forces were estimated using the Computed Muscle Control optimization method. The contralateral limb exhibited greater vertical ground reaction forces across most of the rebound phase (d = 2.01). Compared with the contralateral limb, the ACLR limb showed reduced quadriceps (d = 1.72), soleus (d = 0.95), and gluteus maximus (d = 0.83) forces, indicating deficits in knee extensor, plantarflexor, and hip extensor neuromuscular function. Smaller asymmetries were found for the gluteus medius (d = 0.60) and hamstrings (d = 0.72), while other muscles showed symmetrical activation patterns. These results reveal persistent between-limb asymmetries in muscle recruitment and loading up to nine months post-ACLR, emphasizing the importance of targeted rehabilitation to restore symmetrical neuromuscular control during explosive movements. Full article

End-stage temporomandibular joint (TMJ) disorders often necessitate total joint replacement, and the selection of biomaterial directly impacts long-term outcomes. Ti6Al4V and CoCrMo are commonly used alloys, yet their biomechanical performance in patient-specific prostheses remains insufficiently compared. This study aimed to evaluate the mechanical response of custom TMJ prostheses fabricated from these alloys using finite element analysis (FEA). A three-dimensional mandibular model was created from computed tomography data, and a patient-specific prosthesis was designed in SolidWorks (Dassault Systèmes, SolidWorks Corp., Waltham, MA, USA) and analyzed in ANSYS Workbench 2022 R1 (Ansys Inc., Canonsburg, PA, USA). Physiological loading was simulated by applying forces at the insertion sites of the temporalis, masseter, and medial pterygoid muscles. In the Ti6Al4V model, maximum von Mises stresses reached 192.18 MPa on the mandibular component and 92.004 MPa on the fossa prosthesis, whereas the CoCrMo model demonstrated higher stresses of 204.31 MPa and 94.182 MPa, respectively. Both alloys exhibited similar stress distributions, but Ti6Al4V generated lower stress magnitudes, indicating more favorable load transfer and a reduced risk of mechanical overload on articulating components. These findings underscore the significance of alloy selection in optimizing TMJ prostheses and demonstrate the value of FEA as a tool for guiding future patient-specific designs. Full article

Understanding neuromuscular adaptations resulting from specific training modalities is crucial for optimizing athletic performance and injury prevention. This in silico proof-of-concept study aimed to computationally model and predict neuromuscular adaptations induced by strength and plyometric training, integrating musculoskeletal simulations and machine learning techniques. A validated musculoskeletal model (OpenSim 4.4; 23 DOF, 92 musculotendon actuators) was scaled to a representative athlete (180 cm, 75 kg). Plyometric (vertical jumps, horizontal broad jumps, drop jumps) and strength exercises (back squat, deadlift, leg press) were simulated to evaluate biomechanical responses, including ground reaction forces, muscle activations, joint kinetics, and rate of force development (RFD). Predictive analyses employed artificial neural networks and random forest regression models trained on extracted biomechanical data. The results show plyometric tasks with GRF 22.1–30.2 N·kg⁻¹ and RFD 3200–3600 N·s⁻¹, 10–12% higher activation synchrony, and 7–12% lower moment variability. Strength tasks produced moments of 3.2–3.8 N·m·kg⁻¹; combined strength + plyometric training reached 3.7–4.2 N·m·kg⁻¹, 10–16% above strength only. Machine learning predictions revealed superior neuromuscular gains through combined training, especially pairing back squats with high-intensity drop jumps (50 cm). This integrated computational approach demonstrates significant practical potential, enabling precise optimization of training interventions and injury risk reduction in athletic populations. Full article

Symmetry and asymmetry significantly influence running biomechanics, performance, and injury risk. Given the practical, ethical, and methodological constraints inherent in human-subject studies, computational modeling emerges as a valuable alternative for exploring biomechanical asymmetries in detail. This study systematically evaluated the mechanical effects of lower limb imbalance during running using a simulation-based musculoskeletal framework in OpenSim. A total of 130 simulations were performed, incorporating controlled asymmetries in limb strength, stride length, and ground reaction forces (±5% and ±10%), to quantify alterations in joint moments, ground reaction forces (GRF), and muscular activation patterns. Results demonstrated clear biomechanical deviations under asymmetric conditions. Vertical ground reaction forces (GRF) decreased on the weaker limb and increased on the stronger limb, with peak knee joint moments rising by up to 20% under pronounced asymmetry. Muscle activation in major lower limb muscles, including the gastrocnemius and quadriceps, increased substantially on the stronger side, reflecting compensatory mechanical loading. These findings highlight the negative consequences of uneven limb loading and support the use of computational modeling to guide personalized training, rehabilitation, and injury prevention strategies. Full article

Background: Short-stem hip replacements are designed to provide improved load distribution and to mimic natural biomechanics. The interplay between implant design, positioning, and resulting bone biomechanics in individual patients remains underexplored, and the relationship between radiographically assessed bone remodeling around short stems and biomechanical predictions has not been previously reported. Methods: This study evaluated three short-stem hip implant designs: Proxima, Collo-MIS, and Minima. Postoperative bone remodeling patterns were analyzed, categorizing remodeling as bone gain, bone loss, or no observable activity, with changes tracked over time. Patient-specific biomechanical models were generated from 6-week postoperative radiographs. Finite element simulations incorporated body weight and gluteal muscle forces to estimate stress and strain distributions within the proximal femur. Strain energy was then applied to a mechanostat-based remodeling algorithm to predict bone remodeling patterns. These biomechanical predictions were compared to observed radiographic remodeling at 2 years post-surgery. A validated biomechanical model was further used to simulate different postoperative positions of the three types of stems. Results: No differences in bone remodeling patterns were observed among the three short-stem designs. Computational modeling demonstrated a statistically significant correlation between predicted remodeling and radiographic measurements at 2 years (p < 0.001). Proxima stems showed a tendency towards increased cortical bone loading under pronounced varus or valgus position in comparison to other two stems, although this observation requires further validation. Conclusions: This exploratory study demonstrates the feasibility of using biomechanical modeling to estimate bone remodeling around short-stem hip implants based on early postoperative radiographs. While the results are promising, they should be interpreted with caution due to the limited cohort size. The proposed modeling approach may offer clinical value in evaluating implant behavior and informing patient-specific treatment strategies. However, further research with larger populations is necessary to refine and validate these predictive tools. Full article

The electrodynamics of current provide much of our technology, from telegraphs to the wired infrastructure powering the circuits of our electronic technology. Current flow is analyzed by its own rules that involve the Maxwell Ampere law and magnetism. Electrostatics does not involve magnetism, and so current flow and electrodynamics cannot be derived from electrostatics. Practical considerations also prevent current flow from being analyzed one charge at a time. There are too many charges, and far too many interactions to allow computation. Current flow is essential in biology. Currents are carried by electrons in mitochondria in an electron transport chain. Currents are carried by ions in nerve and muscle cells. Currents everywhere follow the rules of current flow: Kirchhoff’s current law and its generalizations. The importance of electron and proton flows in generating ATP was discovered long ago but they were not analyzed as electrical currents. The flow of protons and transport of electrons form circuits that must be analyzed by Kirchhoff’s law. A chemiosmotic theory that ignores the laws of current flow is incorrect physics. Circuit analysis is easily applied to short systems like mitochondria that have just one internal electrical potential in the form of the Hodgkin Huxley Katz (HHK) equation. The HHK equation combined with classical descriptions of chemical reactions forms a computable model of cytochrome c oxidase, part of the electron transport chain. The proton motive force is included as just one of the components of the total electrochemical potential. Circuit analysis includes its role just as it includes the role of any other ionic current. Current laws are now needed to analyze the flow of electrons and protons, as they generate ATP in mitochondria and chloroplasts. Chemiosmotic theory must be replaced by an electro-osmotic theory of ATP production that conforms to the Maxwell Ampere equation of electrodynamics while including proton movement and the proton motive force. Full article

Background and Objectives: Idiopathic pulmonary fibrosis (IPF) is a specific form of chronic, progressive interstitial lung disease with an unknown etiology. It is often accompanied by skeletal muscle mass loss. Chest wall muscles play a crucial role in respiratory movements and form part of the skeletal muscles. The aim of this study is to evaluate the relationship between chest wall muscle thickness and pulmonary function test (PFT) results, as well as other prognostic markers, in patients with IPF. Materials and Methods: A retrospective analysis was conducted on 108 patients diagnosed with IPF and 53 control subjects. Chest wall muscle thickness was measured on thoracic computed tomography (CT) images at specific anatomical levels. PFT parameters, the Gender-Age-Physiology (GAP) index, number of acute exacerbations, and mortality data were evaluated in relation to muscle thickness. Results: IPF patients had significantly reduced thickness in the bilateral external scapular muscles at both the aortic and pulmonary trunk levels compared to controls. Bilateral pectoral muscle thickness at the aortic level was positively correlated with forced vital capacity (FVC) and negatively correlated with the number of exacerbations. Internal scapular muscle thickness at the aortic level showed a significant positive correlation with diffusion capacity of the lung for carbon monoxide (DLCO) and a negative correlation with both GAP scores and exacerbation frequency. External scapular muscle thickness at the pulmonary trunk level was positively associated with PFT parameters and inversely correlated with the GAP index, exacerbations, and mortality. Conclusions: In patients with IPF, the bilateral external scapular muscle thickness at the aortic and pulmonary trunk levels was significantly reduced compared to controls. Significant associations were found between some chest wall muscle thicknesses and the GAP index, pulmonary function, acute exacerbations, and mortality, underscoring the prognostic value of baseline muscle measurements. Measurement of chest wall muscle thickness using routine thoracic CT scans may offer additional prognostic value in IPF. Incorporating this parameter into clinical evaluation may help identify patients who could benefit from supportive interventions, such as nutritional therapy or pulmonary rehabilitation. Full article

Objective: Exploring how body composition and musculoskeletal characteristics relate to physical performance may provide insights for optimising training outcomes. We explored if body composition and musculoskeletal characteristics were associated with tactical and cardiorespiratory performance. Methods: A cross-sectional study of police recruits within the Western Australia Police Force was performed. Total and regional body composition was assessed using Dual-energy X-ray Absorptiometry, with the tibial morphology and mid-thigh muscle cross-sectional area assessed using peripheral Quantitative Computed Tomography. Tactical performance was measured with a Physical Performance Evaluation, and cardiorespiratory fitness assessed using the Beep Test. Variables that were significant in univariate regressions progressed to generalised linear models, assessing relationships between measures and performance outcomes. Results: Twenty-seven recruits aged 21–51 years (40.7% female) participated. Better tactical performance was associated with lower body fat percentage (p < 0.001), lower body mass index (p < 0.001), higher appendicular muscle mass (p = 0.005), and a lower proximal (66%) tibia polar cross-section moment of inertia (p = 0.007). Better cardiorespiratory fitness was associated with lower body fat percentage (p = 0.004), higher appendicular lean mass (p = 0.006), a lower proximal (66%) tibia polar cross-section moment of inertia (p = 0.005), and a higher mid-thigh muscle cross-sectional area (p < 0.001). Conclusions: Various body composition and musculoskeletal characteristics are associated with tactical performance and cardiorespiratory fitness in WA police recruits. Lower body fat percentage and higher appendicular muscle mass were associated with both better cardiorespiratory fitness and tactical performance, highlighting the potential relevance of these characteristics in preparing police recruits for operational duties. Full article

In the design of mandibular implants, the application of previous research findings, which highlight the significance of asymmetric occlusal load transfer schemes, is often lacking. The generated identical oblique occlusal force for maximum muscle efficiency may not be the sole criterion for assessing the load-bearing capacity of a mandibular prosthesis. The hypothesis of this study was that the load-bearing capacity of extensive mandibular prostheses is significantly underestimated when assuming mandibular support at the temporomandibular joint on the occlusal side compared to the assumption of perfect osseointegration between the implant and bone. Finite Element Method (FEM) simulation studies were conducted to analyze occlusal load transfer, considering two joint support conditions: support in both temporo-mandibular joints and support in only one joint, opposite the bite side. Additionally, two variants of the implant-bone connection were examined: an optimistic scenario assuming complete osseointegration and a pessimistic scenario assuming no osseointegration, with fixation achieved solely through bone fixation plates. The findings indicated a significant underestimation of the loads transferred by the implant and bone tissue when symmetrical joint support and osseointegration were assumed. Although there is currently no conclusive evidence supporting the complete exclusion of the joint, the computational results demonstrated that, in the absence of precise data regarding the percentage contribution of the joint on the occlusal side, it is preferable to employ more stringent criteria for assessing the load-bearing capacity of mandibular prostheses. This assessment should include the exclusion of joint support on the occlusal side to ensure a more conservative and reliable evaluation of the prosthesis’s mechanical performance. Full article

Background: This study investigates the differences in limb coordination patterns and energy transfer strategies during sit-to-stand (STS) transitions among young adults (18–30 years) with overweight (OW), normal weight (NW), and underweight (UW) conditions, providing a theoretical foundation for understanding the impact of BMI variations on movement control mechanisms and informing health intervention strategies. Methods: Forty participants were classified into OW, NW, and UW groups. Motion data were collected via an infrared motion capture system and force plate. Biomechanical indices were computed using Visual 3D and MATLAB2020a. Coordination patterns were assessed using vector coding, and the segmental net power was analyzed to evaluate energy flow during STS. Statistical analyses were performed using one-way ANOVA (α = 0.05). Results: Compared to the NW and UW groups, the OW group exhibited significant differences in movement coordination patterns and energy flow. In terms of coordination patterns, the OW group adopted more hip-knee distal coordination patterns in the FMP phase and more knee-ankle proximal coordination patterns. In the MTP phase, the OW group exhibited a lower frequency of hip-ankle anti-phase coordination patterns compared to the UW group. In the EP phase, the OW group showed a lower frequency of trunk-pelvis proximal coordination patterns than the UW group (p < 0.05). Regarding energy flow, in the FMP phase, the OW group exhibited higher joint power (JP) and segment power (SP) in the trunk compared to the UW group. In the pelvic segment, both JP and SP were higher in the OW group than in the NW and UW groups. In the thigh segment, muscle power (MP) was higher in the OW group than in the NW and UW groups, and SP was higher than in the NW group (p < 0.05). Conclusion: Changes in BMI affect movement coordination and energy transfer strategies during STS. OW individuals compensate for insufficient hip drive by relying on trunk and pelvic power, which may increase the knee and trunk load over time. In contrast, UW individuals exhibit greater lower-limb flexibility and rely on trunk-pelvis coordination to compensate for stability deficits. Future research should develop targeted exercise interventions to optimize movement patterns and reduce injury risk across BMI groups. Full article

Microcirculation is an essential system that regulates oxygen and nutrients to cells and tissues in response to various environmental stimuli and pathophysiological conditions. Diabetes mellitus can cause microvascular complications including nephropathy, neuropathy, and retinopathy. The pathogenesis of microvascular dysfunction in diabetes is associated with hyperglycemia and the result of an interplay of various factors. Research studies have demonstrated that functional microvascular dysfunction appears much earlier than structural alterations in vasculature in diabetes. This finding of the progression from microvascular dysfunction to macrovascular disease establishes a foundation for the screening and early diagnosis of diabetes by assessing the microvascular function. This comprehensive review discusses technologies (laser Doppler, transcutaneous oximetry, infrared thermography and near-infrared spectroscopy) with computational methods (linear (time and frequency domains), nonlinear and machine learning approaches) for diagnosing microvascular dysfunction in diabetes. Pathophysiological changes of microvascular dysfunction leading to impaired vasomotion and blood flow oscillations in diabetes are reviewed. Recent findings in managing microvascular dysfunction using lifestyle modifications and force-based modulations are evaluated. A consensus endorsed by the American Diabetes Association has been reached that an effective exercise program would greatly slow down the progression of microvascular dysfunction and its impact on diabetic foot ulcers, muscle fatigue and weakness and peripheral neuropathy. However, it is imperative to determine the dose–response relationship of exercise and microvascular responses in patients with diabetes. Research studies have demonstrated that local vibration and whole-body vibration can improve microcirculation in various pathological conditions, including diabetes. Due to the complex nature of microvascular regulation, various computational methods have been developed to shed light on the influence of diabetes on microvascular dysfunction. This comprehensive review will contribute to the diagnosis and management of microvascular dysfunction in diabetes. Full article

Objectives: The objective of this paper is to introduce a method to measure the force or pressure over the carpal tunnel indirectly, using a new device to drive the pointer of a computer system. The measurements were compared with those obtained using an ergonomic mouse. Simultaneously, measurements of muscular stress on the digitorum extensor muscle were performed to correlate the applied force against muscle activity. Methods: An experimental setup was constructed using an infrared static receiver plus two wearable moving light emitters, which can be displaced inside a rectangular projected region. The pointer functions are performed through two finger gestures, while the hand is naturally extended. A microcontroller was used to communicate with the computer, which works as a human interface device and possesses firmware to associate the position of each light source with the pointer functions. Meanwhile, force and electromyography sensing circuits were developed to transmit and measure carpal tunnel strength and muscular stress. The system was tested on five healthy volunteers, who were encouraged to solve the same computational tasks using this new device and a trademark ergonomic mouse. Results: Our results show great differences (greater than one magnitude) between the efforts of the same volunteers performing the same predefined tasks using both pointer controllers. Only when the new device was used did the Pearson’s correlation coefficients show a higher correlation between the effort measured on the carpal tunnel and the muscular activity. Conclusions: The optic pointer driver diminishes the strength on the carpal tunnel, causing slightly increased stress on the digitorum extensor muscle. Full article

Accurate measurement of pedaling kinetics and kinematics is vital for optimizing rehabilitation, exercise training, and understanding musculoskeletal biomechanics. Pedal reaction force, the main external force in cycling, is essential for musculoskeletal modeling and closely correlates with lower-limb muscle activity and joint reaction forces. However, sensor instrumentation like 3-axis pedal force sensors is costly and requires extensive postprocessing. Recent advancements in machine learning (ML), particularly neural network (NN) models, provide promising solutions for kinetic analyses. In this study, an NN model was developed to predict radial and mediolateral forces, providing a low-cost solution to study pedaling biomechanics with stationary cycling ergometers. Fifteen healthy individuals performed a 2 min pedaling task at two different self-selected (58 ± 5 RPM) and higher (72 ± 7 RPM) cadences. Pedal forces were recorded using a 3-axis force system. The dataset included pedal force, crank angle, cadence, power, and participants’ weight and height. The NN model achieved an inter-subject normalized root mean square error (nRMSE) of 0.15 ± 0.02 and 0.26 ± 0.05 for radial and mediolateral forces at high cadence, respectively, and 0.20 ± 0.04 and 0.22 ± 0.04 at self-selected cadence. The NN model’s low computational time suits real-time pedal force predictions, matching the accuracy of previous ML algorithms for estimating ground reaction forces in gait. Full article