Florian Michaud

Background: Patellofemoral complications remain a frequent cause of dissatisfaction following total knee arthroplasty (TKA), despite continuous advances in implant design and surgical techniques. Abnormal patellar tracking may lead to increased contact stresses, instability, and anterior knee pain, particularly in patients with preoperative quadriceps malalignment. Subject-specific assessment tools are therefore needed to improve surgical planning and postoperative outcomes. Methods: A subject-specific multibody dynamics (MBD) framework was developed to simulate patellofemoral mechanics following TKA. A single representative patient with pronounced quadriceps malalignment was selected for this proof-of-concept study to enable detailed subject-specific modeling and experimental validation under controlled conditions. The effects of femoral and tibial component positioning, including internal/external rotation and varus/valgus alignment, were systematically evaluated. Numerical predictions were validated using a sensorized 3D-printed knee rig. Results: Quadriceps malalignment was the primary determinant of patellar instability, leading to increased lateralization (bisect offset index up to 0.90, 25% higher than the reference condition), elevated peak contact forces (11% increase), and near-dislocation during knee extension. Valgus alignment increased peak contact forces by up to 3.6% (femur) and 2.9% (tibia) and further exacerbated lateral patellar shift, whereas internal rotation increased peak contact forces by ~ 2.2% and worsened tracking. In contrast, varus alignment and external rotation produced moderate reductions in contact forces (up to − 2.5%) and partial improvement in patellar alignment. The average total computation time was 48 s, and the simulated kinematics and contact forces showed strong agreement with the experimental measurements. Conclusion: The proposed computational framework enables rapid, subject-specific evaluation of patellofemoral mechanics and implant positioning, incorporating individual anatomical and alignment characteristics. Although demonstrated here in a proof-of-concept setting, its computational efficiency and predictive capability suggest potential for future use in intraoperative assessment and last-minute surgical optimization in TKA.

Correction on: Michaud F, Beron S, Lugrís U and Cuadrado J (2024) Four-compartment muscle fatigue model to predict metabolic inhibition and long-lasting nonmetabolic components. Front. Physiol. 15:1366172. doi: 10.3389/fphys.2024.1366172 EquationsWrongEquation 2, in Section 2 Material and methods, Subsection 2.2 Four-compartment muscle fatigue model, was erroneously given as . The correct equation is .The original version of this article has been updated.

Muscle force and fatigue modeling and simulation are powerful tools for rehabilitation, sports performance, ergonomics, and injury prevention. However, their accuracy is challenged by dynamic mechanical and physiological factors. Since musculoskeletal models are typically derived from cadaver data and scaled to individuals, careful subject-specific calibration is recommended to achieve accurate simulation results. This study investigates how different muscle models and calibration strategies affect the accuracy of muscle force estimation at the elbow level. Two models—a simplified static model and a rigid-tendon Hill-type model—were compared. Several calibration approaches were tested using isometric and isokinetic measurements to identify the parameters that most enhance model performance. The models were used to estimate muscle forces, and their outputs were compared to experimental data collected from seventeen healthy subjects. In the first phase, estimations were made during short maximal voluntary contractions (MVCs) without fatigue, in order to isolate muscle force from fatigue effects. In the second phase, the calibrated parameters from each strategy were used to estimate muscle forces and fatigue during a short-duration, high-intensity dynamic exercise by incorporating a muscle fatigue model. The highest accuracy was achieved with the Hill-type model, which involved refining individual muscle length and force parameters based on concentric and eccentric MVCs and adjusting two parameters of the force–velocity relationship. However, incorporating subject-specific muscle fatigue parameters did not significantly improve force estimation under fatigue conditions.

Experimental validation of computational simulations is important because it provides empirical evidence to verify the accuracy and reliability of the simulated results. This validation ensures that the simulation accurately represents real-world phenomena, increasing confidence in the model’s predictive capabilities and its applicability to practical scenarios. The use of musculoskeletal models in orthopedic surgery allows for objective prediction of postoperative function and optimization of results for each patient. To ensure that simulations are trustworthy and can be used for predictive purposes, comparing simulation results with experimental data is crucial. Although progress has been made in obtaining 3D bone geometry and estimating contact forces, validation of these predictions has been limited due to the lack of direct in vivo measurements and the economic and ethical constraints associated with available alternatives. In this study, an existing commercial surgical training station was transformed into a sensorized test bench to replicate a knee subject to a total knee replacement. The original knee inserts of the training station were replaced with personalized 3D-printed bones incorporating their corresponding implants, and multiple sensors with their respective supports were added. The recorded movement of the patella was used in combination with the forces recorded by the pressure sensor and the load cells, to validate the results obtained from the simulation, which was performed by means of a multibody dynamics formulation implemented in a custom-developed library. The utilization of 3D-printed models and sensors facilitated cost-effective and replicable experimental validation of computational simulations, thereby advancing orthopedic surgery while circumventing ethical concerns.

Introduction: Computational muscle force models aim to mathematically represent the mechanics of movement and the factors influencing force generation. These tools allow the prediction of the nonlinear and task-related muscle behavior, aiding biomechanics, sports science, and rehabilitation. Despite often overlooking muscle fatigue in low-force scenarios, these simulations are crucial for high-intensity activities where fatigue and force loss play a significant role. Applications include functional electrical stimulation, motor control, and ergonomic considerations in diverse contexts, encompassing rehabilitation and the prevention of injuries in sports and workplaces.Methods: In this work, the authors enhance the pre-existing 3CCr muscle fatigue model by introducing an additional component of force decay associated with central fatigue and a long-term fatigue state. The innovative four-compartment model distinguishes between the short-term fatigued state (related to metabolic inhibition) and the long-term fatigued state (emulating central fatigue and potential microtraumas).Results: Its validation process involved experimental measurements during both short- and long-duration exercises, shedding light on the limitations of the traditional 3CCr in addressing dynamic force profiles.

Introduction: Poor patellar tracking can result in painful contact pressures, patella subluxation, or dislocation. The use of musculoskeletal models and simulations in orthopedic surgeries allows for objective predictions of post-treatment function, empowering clinicians to explore diverse treatment options for patients. Although a promising approach for managing knee surgeries, the high computational cost of the Finite Element Method hampers its clinical usability. In anticipation of minimal elastic deformations in the involved bodies, the exploration of the Multibody Dynamics approach emerged as a viable solution, providing a computationally efficient methodology to address clinical concerns related to the knee joint.Methods: This work, with a focus on high-performance computing, achieved the simulation of the patellofemoral joint through rigid-body multibody dynamics formulations. A comparison was made between two collision detection algorithms employed in the simulation of contact between the patellar and femoral implants: a generic mesh-to-mesh collision detection algorithm, which identifies potential collisions between bodies by checking for proximity or overlap between their discretized mesh surface elements, and an analytical contact algorithm, which uses a mathematical model to provide closed-form solutions for specific contact problems, but cannot handle arbitrary geometries. In addition, different digital twins (3D model geometries) of the femoral implant were compared.Results: Computational efficiency was considered, and histories of position, orientation, and contact force of the patella during the motion were compared with experimental measurements obtained from a sensorized 3D-printed test bench under pathological and treatment scenarios. The best results were achieved through a purely analytical contact detection algorithm, allowing for clinical usability and optimization of clinical outcomes.

Total knee arthroplasty aims to relieve pain and restore function in the affected joint through artificial implants. Despite advances in design and surgical techniques, there are complications associated with surgery, especially concerning the patella. The use of musculoskeletal models in orthopedic surgery allows for objective prediction of postoperative function and optimization of results for each patient. To ensure that simulations are trustworthy and can be used for predictive purposes, comparing simulation results with experimental data is crucial. Although progress has been made in obtaining 3D bone geometry and estimating contact forces, validation of these predictions has been limited due to the lack of direct in vivo measurements and the economic and ethical constraints associated with available alternatives. In this study, an existing commercial surgical training station was transformed into a sensorized test bench. The use of 3D-printed models and sensors allowed low-cost and reproducible experimental validation of computer simulation (patellar movement and forces) while avoiding ethical issues.

People whose jobs involve repetitive motions of the wrist and forearm can suffer from lateral epicondylitis, which is a significant burden on both the individual and the employer due to treatment costs, reduced productivity, and work absenteeism. This paper describes an ergonomic intervention to reduce lateral epicondylitis in the workstation of a textile logistics center. The intervention includes workplace-based exercise programs, evaluation of risk factors, and movement correction. An injury- and subject-specific score was calculated from the motion captured with wearable inertial sensors at the workplace to evaluate the risk factors of 93 workers. Then, a new working movement was adapted to the workplace, which limited the observed risk factors and took into account the subject-specific physical abilities. The movement was taught to the workers during personalized sessions. The risk factors of 27 workers were evaluated again after the intervention to validate the effectiveness of the movement correction. In addition, active warm-up and stretching programs were introduced as part of the workday to promote muscle endurance and improve resistance to repetitive stress. The present strategy offered good results at low cost, without any physical modification of the workplace and without any detriment to productivity.

Introduction: Multiple different mathematical models have been developed to represent muscle force, to represent multiple muscles in the musculoskeletal system, and to represent muscle fatigue. However, incorporating these different models together to describe the behavior of a high-intensity exercise has not been well described.Methods: In this work, we adapted the three-compartment controller (3CCr) muscle fatigue model to be implemented with an inverse-dynamics based optimization algorithm for the muscle recruitment problem for 7 elbow muscles to model a benchmark case: elbow flexion/extension moments. We highlight the difficulties in achieving an accurate subject-specific approach for this multi-level modeling problem, considering different muscular models, compared with experimental measurements. Both an isometric effort and a dynamic bicep curl were considered, where muscle activity and resting periods were simulated to obtain the fatigue behavior. Muscle parameter correction, scaling and calibration are addressed in this study. Moreover, fiber-type recruitment hierarchy in force generation was added to the optimization problem, thus offering an additional novel muscle modeling criterion.Results: It was observed that: i) the results were most accurate for the static case; ii) insufficient torque was predicted by the model at some time points for the dynamic case, which benefitted from a more precise calibration of muscle parameters; iii) modeling the effects of muscular potentiation may be important; and iv) for this multilevel model approach, the 3CCr model had to be modified to avoid reaching situations of unrealistic constant fatigue in high intensity exercise-resting cycles.Discussion: All the methods yield reasonable estimations, but the complexity of obtaining accurate subject-specific human models is highlighted in this study. The proposed novel muscle modeling and force recruitment criterion, which consider the muscular fiber-type distinction, show interesting preliminary results.

Determination of spine posture is of great interest for the effective prevention, evaluation, treatment and evolution monitoring of spinal disorders. Limitations of traditional imaging systems, including cost, radiation exposure (for X-ray based systems), projection volume issues and subject positioning requirements, etc., make non-invasive motion assessment tools effective alternatives for clinical and non-clinical use. In this work, a procedure was developed to obtain a subject-specific multibody model of the spine using either inertial or optical sensors and, based on this multibody model, to estimate the locations and orientations of the 17 vertebrae constituting the thoracolumbar spine. The number and calibration of the sensors, angular offsets, scaling difficulties and gender differences were addressed to achieve an accurate 3D-representation of the spine. The approach was validated by comparing the estimated positions of the sensors on 14 healthy subjects with those provided by an optical motion capture system. A mean position error of lower than 12 mm was obtained, thus showing that the proposed method can offer an effective non-invasive tool for the assessment of spine posture.