Grand challenge competition to predict in vivo knee loads

Optimal Implant Positioning Following Total Knee Arthroplasty Using Predictive Dynamic Simulation

In this paper, a novel method is proposed for the determination of the optimal subject-specific placement of knee implants based on predictive dynamic simulations of human movement following total knee arthroplasty (TKA). Two knee implant models are introduced. The first model is a comprehensive 12-degree-of-freedom (DoF) representation that incorporates volumetric contact between femoral and tibial implants, as well as patellofemoral contact. The second model employs a single-degree-of-freedom equivalent kinematic (SEK) approach for the knee joint. A cosimulation framework is proposed to leverage both knee models in our simulations. The knee model is calibrated and validated using patient-specific data, including knee kinematics and ground reaction forces. Additionally, quantitative indices are introduced to evaluate the optimality of implant positioning based on three criteria: balancing medial and lateral load distributions, ligament balancing, and varus/valgus alignment. The knee implant placement is optimized by minimizing the deviation of the indices from their user-defined desired values during predicted sit-to-stand motion. The method presented in this paper has the potential to enhance the results of knee arthroplasty and serve as a valuable instrument for surgeons when planning and performing this procedure.

Machine Learning Approaches for 3D Motion Synthesis and Musculoskeletal Dynamics Estimation: A Survey

The inference of 3D motion and dynamics of the human musculoskeletal system has traditionally been solved using physics-based methods that exploit physical parameters to provide realistic simulations. Yet, such methods suffer from computational complexity and reduced stability, hindering their use in computer graphics applications that require real-time performance. With the recent explosion of data capture (mocap, video) machine learning (ML) has started to become popular as it is able to create surrogate models harnessing the huge amount of data stemming from various sources, minimizing computational time (instead of resource usage), and most importantly, approximate real-time solutions. The main purpose of this paper is to provide a review and classification of the most recent works regarding motion prediction, motion synthesis as well as musculoskeletal dynamics estimation problems using ML techniques, in order to offer sufficient insight into the state-of-the-art and draw new research directions. While the study of motion may appear distinct to musculoskeletal dynamics, these application domains provide jointly the link for more natural computer graphics character animation, since ML-based musculoskeletal dynamics estimation enables modeling of more long-term, temporally evolving, ergonomic effects, while offering automated and fast solutions. Overall, our review offers an in-depth presentation and classification of ML applications in human motion analysis, unlike previous survey articles focusing on specific aspects of motion prediction.

The effect of impaired unilateral ankle propulsion on contralateral knee joint loading

BACKGROUND

Impairments in unilateral ankle propulsion may result from restriction by an external device or pathology such as lower limb amputation. Models of gait suggest this reduction may lead to increased collisional force on the contralateral side, potentially increasing force through the knee and increasing the risk of knee pain or osteoarthritis.

RESEARCH QUESTION

How do restrictions in unilateral ankle propulsive force affect contralateral knee joint loading in otherwise healthy individuals?

METHODS

18 individuals without impairment walked on a treadmill at 1.5 m/s for two conditions: one free of restrictions, and one where a randomized limb's ankle propulsive force was restricted using an off-the-shelf ankle-foot orthosis (AFO). Ankle propulsive power, lower extremity joint work, and ground reaction force variables were calculated for the final 3 gait cycles of each condition. Tibiofemoral joint contact force (TJCF) for the limb contralateral to the AFO was calculated through a standard OpenSim workflow utilizing the gait2392 model. Intra-limb pair-wise comparisons were made between conditions.

RESULTS

Compared to walking unrestricted, the limb wearing the AFO demonstrated a significant reduction in peak ankle propulsive power and positive ankle work by approximately 50 % each (p<0.01). With ankle restriction, the ipsilateral knee significantly increased positive work (p<0.01). The overall propulsion produced by that limb did not change between conditions, demonstrated by a lack of change in anterior ground reaction force impulse (p=0.11). The knee of the limb contralateral to the AFO did not display differences in any TJCF variable between conditions (all p>0.07).

SIGNIFICANCE

These results suggest a unilateral deficit in ankle propulsion will not increase contralateral knee joint forces in individuals who are able to use other joints of the limb to compensate for the loss of ankle function. However, further research should investigate this relationship in those who display pathologies that may prevent more proximal compensations.

The Biomechanics of Musculoskeletal Tissues during Activities of Daily Living: Dynamic Assessment Using Quantitative Transmission-Mode Ultrasound Techniques

The measurement of musculoskeletal tissue properties and loading patterns during physical activity is important for understanding the adaptation mechanisms of tissues such as bone, tendon, and muscle tissues, particularly with injury and repair. Although the properties and loading of these connective tissues have been quantified using direct measurement techniques, these methods are highly invasive and often prevent or interfere with normal activity patterns. Indirect biomechanical methods, such as estimates based on electromyography, ultrasound, and inverse dynamics, are used more widely but are known to yield different parameter values than direct measurements. Through a series of literature searches of electronic databases, including Pubmed, Embase, Web of Science, and IEEE Explore, this paper reviews current methods used for the in vivo measurement of human musculoskeletal tissue and describes the operating principals, application, and emerging research findings gained from the use of quantitative transmission-mode ultrasound measurement techniques to non-invasively characterize human bone, tendon, and muscle properties at rest and during activities of daily living. In contrast to standard ultrasound imaging approaches, these techniques assess the interaction between ultrasound compression waves and connective tissues to provide quantifiable parameters associated with the structure, instantaneous elastic modulus, and density of tissues. By taking advantage of the physical relationship between the axial velocity of ultrasound compression waves and the instantaneous modulus of the propagation material, these techniques can also be used to estimate the in vivo loading environment of relatively superficial soft connective tissues during sports and activities of daily living. This paper highlights key findings from clinical studies in which quantitative transmission-mode ultrasound has been used to measure the properties and loading of bone, tendon, and muscle tissue during common physical activities in healthy and pathological populations.

Prediction of Achilles Tendon Force During Common Motor Tasks From Markerless Video

Remodeling of the Achilles tendon (AT) is partly driven by its mechanical environment. AT force can be estimated with neuromusculoskeletal (NMSK) modeling; however, the complex experimental setup required to perform the analyses confines use to the laboratory. We developed task-specific long short-term memory (LSTM) neural networks that employ markerless video data to predict the AT force during walking, running, countermovement jump, single-leg landing, and single-leg heel rise. The task-specific LSTM models were trained on pose estimation keypoints and corresponding AT force data from 16 subjects, calculated via an established NMSK modeling pipeline, and cross-validated using a leave-one-subject-out approach. As proof-of-concept, new motion data of one participant was collected with two smartphones and used to predict AT forces. The task-specific LSTM models predicted the time-series AT force using synthesized pose estimation data with root mean square error (RMSE) ≤ 526 N, normalized RMSE (nRMSE) ≤ 0.21 , R 2 ≥ 0.81 . Walking task resulted the most accurate with RMSE = 189±62 N; nRMSE = 0.11±0.03 , R 2 = 0.92±0.04 . AT force predicted with smartphones video data was physiologically plausible, agreeing in timing and magnitude with established force profiles. This study demonstrated the feasibility of using low-cost solutions to deploy complex biomechanical analyses outside the laboratory.

Cost function criteria using muscle synergies: Exploring the potential of muscle synergy hypothesis

BACKGROUND AND OBJECTIVE

Solving the redundant optimization problem for human muscles depends on the cost function. Choosing the appropriate cost function helps to address a specific problem. Muscle synergies are currently limited to those obtained by electromyography. Furthermore, debate continues regarding whether muscle synergy is derived or real. This study proposes new cost functions based on the muscle synergy hypothesis for solving the optimal muscle force output problem through musculoskeletal modeling.

METHODS

We propose two new computational cost functions involving muscle synergies, which are extracted from muscle activations predicted by musculoskeletal modelling rather than electromyography. In this study, we constructed a musculoskeletal model for simulation using the "Grand Challenge Competition to Predict In Vivo Knee Loads" dataset. Muscle synergies were obtained using non-negative matrix factorization. Two cost functions with muscle synergies were constructed by integrating the polynomial and min/max criterion. Two new functions were verified and validated in normal, smooth, and bouncy gaits.

RESULTS

The muscle synergies based on normal gaits were classified into four modules. The cosine similarities of the first three modules were all >0.9. In the normal and smooth gaits, the forces in most muscles predicted using the two new functions were within three standard deviations of the root mean square error for electromyographic comparisons. Predicted muscle force curves using the four methods as well as characteristic points (i.e., time points in the gait cycle when the significant difference was observed between normal and bouncy gaits) were obtained to validate their predictive capabilities.

CONCLUSIONS

This study constructed two new cost functions involving muscle synergies, verified and validated the ability, and explored the potential of muscle synergy hypothesis.

AST: An OpenSim-based tool for the automatic scaling of generic musculoskeletal models

BACKGROUND AND OBJECTIVES

The paper introduces a tool called Automatic Scaling Tool (AST) designed for improving and expediting musculoskeletal (MSK) simulations based on generic models in OpenSim. Scaling is a crucial initial step in MSK analyses, involving the correction of virtual marker locations on a model to align with actual experimental markers.

METHODS

The AST automates this process by iteratively adjusting virtual markers using scaling and inverse kinematics on a static trial. It evaluates the root mean square error (RMSE) and maximum marker error, implementing corrective actions until achieving the desired accuracy level. The tool determines whether to scale a segment with a marker-based or constant scaling factor based on checks on RMSE and segment scaling factors.

RESULTS

Testing on three generic MSK models demonstrated that the AST significantly outperformed manual scaling by an expert operator. The RMSE for static trials was one order of magnitude lower, and for gait tasks, it was five times lower (8.5 ± 0.76 mm vs. 44.5 ± 7.5 mm). The AST consistently achieved the desired level of accuracy in less than 100 iterations, providing reliable scaled MSK models within a relatively brief timeframe, ranging from minutes to hours depending on model complexity.

CONCLUSIONS

The paper concludes that AST can greatly benefit the biomechanical community by quickly and accurately scaling generic models, a critical first step in MSK analyses. Further validation through additional experimental datasets and generic models is proposed for future tests.

Changes in walking function and neural control following pelvic cancer surgery with reconstruction

Introduction: Surgical planning and custom prosthesis design for pelvic cancer patients are challenging due to the unique clinical characteristics of each patient and the significant amount of pelvic bone and hip musculature often removed. Limb-sparing internal hemipelvectomy surgery with custom prosthesis reconstruction has become a viable option for this patient population. However, little is known about how post-surgery walking function and neural control change from pre-surgery conditions. Methods: This case study combined comprehensive walking data (video motion capture, ground reaction, and electromyography) with personalized neuromusculoskeletal computer models to provide a thorough assessment of pre- to post-surgery changes in walking function (ground reactions, joint motions, and joint moments) and neural control (muscle synergies) for a single pelvic sarcoma patient who received internal hemipelvectomy surgery with custom prosthesis reconstruction. Pre- and post-surgery walking function and neural control were quantified using pre- and post-surgery neuromusculoskeletal models, respectively, whose pelvic anatomy, joint functional axes, muscle-tendon properties, and muscle synergy controls were personalized using the participant's pre-and post-surgery walking and imaging data. For the post-surgery model, virtual surgery was performed to emulate the implemented surgical decisions, including removal of hip muscles and implantation of a custom prosthesis with total hip replacement. Results: The participant's post-surgery walking function was marked by a slower self-selected walking speed coupled with several compensatory mechanisms necessitated by lost or impaired hip muscle function, while the participant's post-surgery neural control demonstrated a dramatic change in coordination strategy (as evidenced by modified time-invariant synergy vectors) with little change in recruitment timing (as evidenced by conserved time-varying synergy activations). Furthermore, the participant's post-surgery muscle activations were fitted accurately using his pre-surgery synergy activations but fitted poorly using his pre-surgery synergy vectors. Discussion: These results provide valuable information about which aspects of post-surgery walking function could potentially be improved through modifications to surgical decisions, custom prosthesis design, or rehabilitation protocol, as well as how computational simulations could be formulated to predict post-surgery walking function reliably given a patient's pre-surgery walking data and the planned surgical decisions and custom prosthesis design.

A Sensorized 3D-Printed Knee Test Rig for Preliminary Experimental Validation of Patellar Tracking and Contact Simulation

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.

Prediction of medial knee contact force using multisource fusion recurrent neural network and transfer learning

Estimation of knee contact force (KCF) during gait provides essential information to evaluate knee joint function. Machine learning has been employed to estimate KCF because of the advantages of low computational cost and real-time. However, the existing machine learning models do not adequately consider gait-related data's temporal-dependent, multidimensional, and highly heterogeneous nature. This study is aimed at developing a multisource fusion recurrent neural network to predict the medial condyle KCF. First, a multisource fusion long short-term memory (MF-LSTM) model was established. Then, we developed a transfer learning strategy based on the MF-LSTM model for subject-specific medial KCF prediction. Four subjects with instrumented tibial prostheses were obtained from the literature. The results showed that the MF-LSTM model could predict medial KCF to a certain high level of accuracy (the mean of ρ = 0.970). The transfer learning model improved the prediction accuracy (the mean of ρ = 0.987). This study shows that the MF-LSTM model is a powerful and accurate computational tool for medial KCF prediction. Introducing transfer learning techniques could further improve the prediction performance for the target subject. This coupling strategy can help clinicians accurately estimate and track joint contact forces in real time.

Joint contact forces during semi-recumbent seated cycling

Semi-recumbent cycling performed from a wheelchair is a popular rehabilitation exercise following spinal cord injury (SCI) and is often paired with functional electrical stimulation. However, biomechanical assessment of this cycling modality is lacking, even in unimpaired populations, hindering the development of personalised and safe rehabilitation programs for those with SCI. This study developed a computational pipeline to determine lower limb kinematics, kinetics, and joint contact forces (JCF) in 11 unimpaired participants during voluntary semi-recumbent cycling using a rehabilitation ergometer. Two cadences (40 and 60 revolutions per minute) and three crank powers (15 W, 30 W, and 45 W) were assessed. A rigid body model of a rehabilitation ergometer was combined with a calibrated electromyogram-informed neuromusculoskeletal model to determine JCF at the hip, knee, and ankle. Joint excursions remained consistent across all cadence and powers, but joint moments and JCF differed between 40 and 60 revolutions per minute, with peak JCF force significantly greater at 40 compared to 60 revolutions per minute for all crank powers. Poor correlations were found between mean crank power and peak JCF across all joints. This study provides foundation data and computational methods to enable further evaluation and optimisation of semi-recumbent cycling for application in rehabilitation after SCI and other neurological disorders.

Computational modelling of articular joints with biphasic cartilage: recent advances, challenges and opportunities

Biphasic models have been widely used to simulate the time-dependent biomechanical response of soft tissues. Modelling techniques of joints with biphasic weight-bearing soft tissues have been markedly improved over the last decade, enhancing our understanding of the function, degenerative mechanism and outcomes of interventions of joints. This paper reviews the recent advances, challenges and opportunities in computational models of joints with biphasic weight-bearing soft tissues. The review begins with an introduction of the function and degeneration of joints from a biomechanical aspect. Different constitutive models of articular cartilage, in particular biphasic materials, are illustrated in the context of the study of contact mechanics in joints. Approaches, advances and major findings of biphasic models of the hip and knee are presented, followed by a discussion of the challenges awaiting to be addressed, including the convergence issue, high computational cost and inadequate validation. Finally, opportunities and clinical insights in the areas of subject-specific modeling and tissue engineering are provided and discussed.

Influence of posterior cruciate ligament tension on tibiofemoral and patellofemoral joint contact mechanics in cruciate-retaining total knee replacement: a combined musculoskeletal multibody and finite-element simulation

The influence of posterior cruciate ligament (PCL) tension on the clinical outcome of cruciate-retaining total knee replacement (CR-TKR) remains controversial. Various numerical approaches have been used to study this influence systematically, but the models used are limited by certain assumptions and simplifications. Therefore, the objective of this computational study was to develop a combined musculoskeletal multibody and finite-element simulation during a squat motion to 90° knee flexion with a CR-TKR design to overcome previous limitations regarding model inputs. In addition, different PCL tensions (tight, lax, resected) were modeled and the influence on tibiofemoral and resurfaced patellofemoral joint dynamics and contact stresses was evaluated. The effect of the PCL on knee joint dynamics and contact stresses was more pronounced at higher flexion angles. Tibiofemoral joint dynamics were influenced and a tight PCL induced increased posterior femoral translation during flexion. The maximum contact stress in the tibial insert increased from 20.6 MPa to 22.5 MPa for the resected and tightest PCL at 90° knee flexion. Patellofemoral joint dynamics were only slightly affected by PCL tension. However, the maximum contact stress in the patellar component decreased from 58.0 MPa to 53.7 MPa for the resected and tightest PCL at 90° knee flexion. The combination of musculoskeletal multibody and finite-element simulation is a sufficient method to comprehensively investigate knee joint dynamics and contact stresses in CR-TKR. The PCL tension after CR-TKR affects joint dynamics and contact stresses at the articulating implant surfaces.

Musculoskeletal model degrees of Freedom: Frontal plane constraints are hindering our understanding of human movement

Induced acceleration analyses have expanded our understanding on the contributions of muscle forces to center of mass and segmental kinematics during a myriad of tasks. While these techniques have identified a subset of major muscle that contribute to locomotion, most analyses have included models with only one frontal plane degree of freedom (dof) actuated by the hip joint. The purpose of this study was to define the impact of including knee and subtalar joint frontal plane dof on model superposition accuracy and muscle specific contributions to mediolateral accelerations. Induced acceleration analyses were performed using OpenSim with the Lai model on a freely available dataset of one subject running at 4 m/s. Analyses were performed on four models (standard, with subtalar joint, with frontal plane knee, and combined frontal plane knee with subtalar) with the kinematic constraint and perturbation analyses. Root mean square error and correlations were computed against experimental kinematics. Adding frontal plane dofs improved mediolateral acceleration correlations on average by > 0.25 while only minimally impacting errors. The constraints method performed better than the perturbation method for mediolateral accelerations. Including frontal plane knee dof resulted in muscle and method specific responses. All muscles presented with a complete flip of polarity for constraint method, imparted by allowing the medial/lateral muscles to contribute according to their anatomical function. Only the gluteus medius flipped for the perturbation method. This study provides significant support for the inclusion of frontal plane knee and subtalar dof and the need for reevaluation of muscle contributions via induced acceleration.

Transfemoral prosthetic simulators versus amputees: ground reaction forces and spatio-temporal parameters in gait

This study aimed to compare the ground reaction forces (GRFs) and spatio-temporal parameters as well as their asymmetry ratios in gait between individuals wearing a transfemoral prosthetic simulator (TFSim) and individuals with unilateral transfemoral amputation (TFAmp) across a range of walking speeds (2.0-5.5 km h-1). The study recruited 10 non-disabled individuals using TFSim and 10 individuals with unilateral TFAmp using a transfemoral prosthesis. Data were collected using an instrumented treadmill with built-in force plates, and subsequently, the GRFs and spatio-temporal parameters, as well as their asymmetry ratios, were analysed. When comparing the TFSim and TFAmp groups, no significant differences were found among the gait parameters and asymmetry ratios of all tested metrics except the vertical GRFs. The TFSim may not realistically reproduce the vertical GRFs during the weight acceptance and push-off phases. The structural and functional variations in prosthetic limbs and components between the TFSim and TFAmp groups may be primary contributors to the difference in the vertical GRFs. These results suggest that TFSim might be able to emulate the gait of individuals with TFAmp regarding the majority of spatio-temporal and GRF parameters. However, the vertical GRFs of TFSim should be interpreted with caution.

Use of patellofemoral digital twins for patellar tracking and treatment prediction: comparison of 3D models and contact detection algorithms

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.

NSF DARE-transforming modeling in neurorehabilitation: a patient-in-the-loop framework

In 2023, the National Science Foundation (NSF) and the National Institute of Health (NIH) brought together engineers, scientists, and clinicians by sponsoring a conference on computational modelling in neurorehabiilitation. To facilitate multidisciplinary collaborations and improve patient care, in this perspective piece we identify where and how computational modelling can support neurorehabilitation. To address the where, we developed a patient-in-the-loop framework that uses multiple and/or continual measurements to update diagnostic and treatment model parameters, treatment type, and treatment prescription, with the goal of maximizing clinically-relevant functional outcomes. This patient-in-the-loop framework has several key features: (i) it includes diagnostic and treatment models, (ii) it is clinically-grounded with the International Classification of Functioning, Disability and Health (ICF) and patient involvement, (iii) it uses multiple or continual data measurements over time, and (iv) it is applicable to a range of neurological and neurodevelopmental conditions. To address the how, we identify state-of-the-art and highlight promising avenues of future research across the realms of sensorimotor adaptation, neuroplasticity, musculoskeletal, and sensory & pain computational modelling. We also discuss both the importance of and how to perform model validation, as well as challenges to overcome when implementing computational models within a clinical setting. The patient-in-the-loop approach offers a unifying framework to guide multidisciplinary collaboration between computational and clinical stakeholders in the field of neurorehabilitation.

A novel computational workflow to holistically assess total knee arthroplasty biomechanics identifies subject-specific effects of joint mechanics on implant fixation

Computational studies of total knee arthroplasty (TKA) often focus on either joint mechanics (kinematics and forces) or implant fixation mechanics. However, such disconnect between joint and fixation mechanics hinders our understanding of overall TKA biomechanical function by preventing identification of key relationships between these two levels of TKA mechanics. We developed a computational workflow to holistically assess TKA biomechanics by integrating musculoskeletal and finite element (FE) models. For our initial study using the workflow, we investigated how tibiofemoral contact mechanics affected the risk of failure due to debonding at the implant-cement interface using the four available subjects from the Grand Challenge Competitions to Predict In Vivo Knee Loads. We used a musculoskeletal model with a 12 degrees-of-freedom knee joint to simulate the stance phase of gait for each subject. The computed tibiofemoral joint forces at each node in contact were direct inputs to FE simulations of the same subjects. We found that the peak risk of failure did not coincide with the peak joint forces or the extreme tibiofemoral contact positions. Moreover, despite the consistency of joint forces across subjects, we observed important variability in the profile of the risk of failure during gait. Thus, by a combined evaluation of the joint and implant fixation mechanics of TKA, we could identify subject-specific effects of joint kinematics and forces on implant fixation that would otherwise have gone unnoticed. We intend to apply our workflow to evaluate the impact of implant alignment and design on TKA biomechanics.

Effects of gait modifications on tissue-level knee mechanics in individuals with medial tibiofemoral osteoarthritis: A proof-of-concept study towards personalized interventions

Gait modification is a common nonsurgical approach to alter the mediolateral distribution of knee contact forces, intending to decelerate or postpone the progression of mechanically induced knee osteoarthritis (KOA). Nevertheless, the success rate of these approaches is controversial, with no studies conducted to assess alterations in tissue-level knee mechanics governing cartilage degradation response in KOA patients undertaking gait modifications. Thus, here we investigated the effect of different conventional gait conditions and modifications on tissue-level knee mechanics previously suggested as indicators of collagen network damage, cell death, and loss of proteoglycans in knee cartilage. Five participants with medial KOA were recruited and musculoskeletal finite element analyses were conducted to estimate subject-specific tissue mechanics of knee cartilages during two gait conditions (i.e., barefoot and shod) and six gait modifications (i.e., 0°, 5°, and 10° lateral wedge insoles, toe-in, toe-out, and wide stance). Based on our results, the optimal gait modification varied across the participants. Overall, toe-in, toe-out, and wide stance showed the greatest reduction in tissue mechanics within medial tibial and femoral cartilages. Gait modifications could effectually alter maximum principal stress (~20 ± 7%) and shear strain (~9 ± 4%) within the medial tibial cartilage. Nevertheless, lateral wedge insoles did not reduce joint- and tissue-level mechanics considerably. Significance: This proof-of-concept study emphasizes the importance of the personalized design of gait modifications to account for biomechanical risk factors associated with cartilage degradation.

Prediction of knee biomechanics with different tibial component malrotations after total knee arthroplasty: conventional machine learning vs. deep learning

The precise alignment of tibiofemoral components in total knee arthroplasty is a crucial factor in enhancing the longevity and functionality of the knee. However, it is a substantial challenge to quickly predict the biomechanical response to malrotation of tibiofemoral components after total knee arthroplasty using musculoskeletal multibody dynamics models. The objective of the present study was to conduct a comparative analysis between a deep learning method and four conventional machine learning methods for predicting knee biomechanics with different tibial component malrotation during a walking gait after total knee arthroplasty. First, the knee contact forces and kinematics with different tibial component malrotation in the range of ±5° in the three directions of anterior/posterior slope, internal/external rotation, and varus/valgus rotation during a walking gait after total knee arthroplasty were calculated based on the developed musculoskeletal multibody dynamics model. Subsequently, deep learning and four conventional machine learning methods were developed using the above 343 sets of biomechanical data as the dataset. Finally, the results predicted by the deep learning method were compared to the results predicted by four conventional machine learning methods. The findings indicated that the deep learning method was more accurate than four conventional machine learning methods in predicting knee contact forces and kinematics with different tibial component malrotation during a walking gait after total knee arthroplasty. The deep learning method developed in this study enabled quickly determine the biomechanical response with different tibial component malrotation during a walking gait after total knee arthroplasty. The proposed method offered surgeons and surgical robots the ability to establish a calibration safety zone, which was essential for achieving precise alignment in both preoperative surgical planning and intraoperative robotic-assisted surgical navigation.

Muscle contributions to reduced ankle joint contact force during drop vertical jumps in patients with chronic ankle instability

Chronic ankle instability is a condition linked to progressive early ankle joint degeneration. Patients with chronic ankle instability exhibit altered biomechanics during gait and jump landings and these alterations are believed to contribute to aberrant joint loading and subsequent joint degeneration. Musculoskeletal modeling has the capacity to estimate joint loads from individual muscle forces. However, the influence of chronic ankle instability on joint contact forces remains largely unknown. The objective of this study was to compare tri-axial (i.e., compressive, anterior-posterior, and medial-lateral) ankle joint contact forces between those with and without chronic ankle instability during the ground contact phase of a drop vertical jump. Fifteen individuals with and 15 individuals without chronic ankle instability completed drop vertical jump maneuvers in a research laboratory. We used those data to drive three-dimensional musculoskeletal simulations and estimate muscle forces and tri-axial joint contact force variables (i.e., peak and impulse). Compared to those without chronic ankle instability, the ankles of patients with chronic ankle instability underwent lower compressive ankle joint contact forces as well as lower anterior-posterior and medial-lateral shearing forces during the weight acceptance phase of landing (p <.05). These findings suggest that patients with chronic ankle instability exhibit lower ankle joint loading patterns than uninjured individuals during a drop vertical jump, which may be considered in rehabilitation to potentially reduce the risk of early onset of ankle joint degeneration.

Loading of the Hip and Knee During Swimming: An in Vivo Load Study

BACKGROUND

Swimming is commonly recommended as postoperative rehabilitation following total hip arthroplasty (THA) and total knee arthroplasty (TKA). So far, in vivo hip and knee joint loads during swimming remain undescribed.

METHODS

In vivo hip and knee joint loads were measured in 6 patients who underwent THA and 5 patients who underwent TKA with instrumented joint implants. Joint loads, including the resultant joint contact force (F Res ), torsional moment around the femoral shaft axis or the tibial axis (M Tors ), bending moment at the middle of the femoral neck (M Bend ), torsional moment around the femoral neck axis (M Tne ), and medial force ratio (MFR) in the knee, were measured during breaststroke swimming at 0.5, 0.6, and 0.7 m/s and the breaststroke and crawl kicks at 0.5 and 1.0 m/s.

RESULTS

The ranges of the median maximal F Res were 157% to 193% of body weight for the hip and 93% to 145% of body weight for the knee during breaststroke swimming. Greater maxima of F Res (hip and knee), M Tors (hip and knee), M Bend (hip), and M Tne (hip) were observed with higher breaststroke swimming velocities, but significance was only identified between 0.5 and 0.6 m/s in F Res (p = 0.028), M Tors (p = 0.028), and M Bend (p = 0.028) and between 0.5 and 0.7 m/s in F Res (p = 0.045) in hips. No difference was found in maximal MFR between different breaststroke swimming velocities. The maximal F Res was significantly positively correlated with the breaststroke swimming velocity (hip: r = 0.541; p < 0.05; and knee: r = 0.414; p < 0.001). The maximal F Res (hip and knee) and moments (hip) were higher in the crawl kick than in the breaststroke kick, and a significant difference was recognized in F Res Max for the hip: median, 179% versus 118% of body weight (p = 0.028) for 0.5 m/s and 166% versus 133% of body weight (p = 0.028) for 1.0 m/s.

CONCLUSIONS

Swimming is a safe and low-impact activity, particularly recommended for patients who undergo THA or TKA. Hip and knee joint loads are greater with higher swimming velocities and can be influenced by swimming styles. Nevertheless, concrete suggestions to patients who undergo arthroplasty on swimming should involve individual considerations.

LEVEL OF EVIDENCE

Therapeutic Level IV . See Instructions for Authors for a complete description of levels of evidence.

Knee kinematics are primarily determined by implant alignment but knee kinetics are mainly influenced by muscle coordination strategy

Implant malalignment has been reported to be a primary reason for revision total knee arthroplasty (TKA). In addition, altered muscle coordination patterns are commonly observed in TKA patients, which is thought to alter knee contact loads. A comprehensive understanding of the influence of surgical implantation and muscle recruitment strategies on joint contact mechanics is crucial to improve surgical techniques, increase implant longevity, and inform rehabilitation protocols. In this study, a detailed musculoskeletal model with a 12 degrees of freedom knee was developed to represent a TKA subject from the CAMS-Knee datasets. Using motion capture and ground reaction force data, a level walking cycle was simulated and the joint movement and loading patterns were estimated using a novel technique for concurrent optimization of muscle activations and joint kinematics. In addition, over 12'000 Monte Carlo simulations were performed to predict knee contact mechanics during walking, considering numerous combinations of implant alignment and muscle activation scenarios. Validation of our baseline simulation showed good agreement between the model kinematics and loading patterns against the in vivo data. Our analyses reveal a considerable impact of implant alignment on the joint kinematics, while variation in muscle activation strategies mainly affects knee contact loading. Moreover, our results indicate that high knee compressive forces do not necessarily originate from extreme kinematics and vice versa. This study provides an improved understanding of the complex inter-relationships between loading and movement patterns resulting from different surgical implantation and muscle coordination strategies and presents a validated framework towards population-based modelling in TKA.

Tibiofemoral Load Magnitude and Distribution During Load Carriage

Chronic exposure to high tibiofemoral joint (TFJ) contact forces can be detrimental to knee joint health. Load carriage increases TFJ contact forces, but it is unclear whether medial and lateral tibiofemoral compartments respond similarly to incremental load carriage. The purpose of our study was to compare TFJ contact forces when walking with 15% and 30% added body weight. Young healthy adults (n = 24) walked for 5 minutes with no load, 15% load, and 30% load on an instrumented treadmill. Total, medial, and lateral TFJ contact peak forces and impulses were calculated via an inverse dynamics informed musculoskeletal model. Results of 1-way repeated measures analyses of variance (α = .05) demonstrated total, medial, and lateral TFJ first peak contact forces and impulses increased significantly with increasing load. Orthogonal polynomial trends demonstrated that the 30% loading condition led to a curvilinear increase in total and lateral TFJ impulses, whereas medial first peak TFJ contact forces and impulses responded linearly to increasing load. The total and lateral compartment impulse increased disproportionally with load carriage, while the medial did not. The medial and lateral compartments responded differently to increasing load during walking, warranting further investigation because it may relate to risk of osteoarthritis.

Reproducibility in modeling and simulation of the knee: Academic, industry, and regulatory perspectives

Stakeholders in the modeling and simulation (M&S) community organized a workshop at the 2019 Annual Meeting of the Orthopaedic Research Society (ORS) entitled "Reproducibility in Modeling and Simulation of the Knee: Academic, Industry, and Regulatory Perspectives." The goal was to discuss efforts among these stakeholders to address irreproducibility in M&S focusing on the knee joint. An academic representative from a leading orthopedic hospital in the United States described a multi-institutional, open effort funded by the National Institutes of Health to assess model reproducibility in computational knee biomechanics. A regulatory representative from the United States Food and Drug Administration indicated the necessity of standards for reproducibility to increase utility of M&S in the regulatory setting. An industry representative from a major orthopedic implant company emphasized improving reproducibility by addressing indeterminacy in personalized modeling through sensitivity analyses, thereby enhancing preclinical evaluation of joint replacement technology. Thought leaders in the M&S community stressed the importance of data sharing to minimize duplication of efforts. A survey comprised 103 attendees revealed strong support for the workshop and for increasing emphasis on computational modeling at future ORS meetings. Nearly all survey respondents (97%) considered reproducibility to be an important issue. Almost half of respondents (45%) tried and failed to reproduce the work of others. Two-thirds of respondents (67%) declared that individual laboratories are most responsible for ensuring reproducible research whereas 44% thought that journals are most responsible. Thought leaders and survey respondents emphasized that computational models must be reproducible and credible to advance knee M&S.

AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization

Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subject's skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2 cm, and residual force magnitudes smaller than 2% of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at AddBiomechanics.org, which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.

Prediction of Knee Joint Compartmental Loading Maxima Utilizing Simple Subject Characteristics and Neural Networks

Joint loading may affect the development of osteoarthritis, but patient-specific load estimation requires cumbersome motion laboratory equipment. This reliance could be eliminated using artificial neural networks (ANNs) to predict loading from simple input predictors. We used subject-specific musculoskeletal simulations to estimate knee joint contact forces for 290 subjects during over 5000 stance phases of walking and then extracted compartmental and total joint loading maxima from the first and second peaks of the stance phase. We then trained ANN models to predict the loading maxima from predictors that can be measured without motion laboratory equipment (subject mass, height, age, gender, knee abduction-adduction angle, and walking speed). When compared to the target data, our trained models had NRMSEs (RMSEs normalized to the mean of the response variable) between 0.14 and 0.42 and Pearson correlation coefficients between 0.42 and 0.84. The loading maxima were predicted most accurately using the models trained with all predictors. We demonstrated that prediction of knee joint loading maxima may be possible without laboratory-measured motion capture data. This is a promising step in facilitating knee joint loading predictions in simple environments, such as a physician's appointment. In future, the rapid measurement and analysis setup could be utilized to guide patients in rehabilitation to slow development of joint disorders, such as osteoarthritis.

Bilateral Asymmetry in Knee and Hip Musculoskeletal Loading During Stair Ascending/Descending in Individuals with Unilateral Mild-to-Moderate Medial Knee Osteoarthritis

Most cases of unilateral knee osteoarthritis (OA) progress to bilateral OA within 10 years. Biomechanical asymmetries have been implicated in contralateral OA development; however, gait analysis alone does not consistently detect asymmetries in OA patient gait. Stair ambulation is a more demanding activity that may be more suited to reveal between-leg asymmetries in OA patients. The objective of this study was to investigate the between-leg biomechanical differences in patients with unilateral mild-to-moderate knee OA. Sixteen unilateral mild-to-moderate medial knee OA patients and 16 healthy individuals underwent kinematic and kinetic analysis of stair ascent and descent. Stair ascent produced higher loading and muscle forces in the unaffected limb compared to the OA limb, and stair descent produced lower loading on the OA limb compared to healthy subjects. These biomechanical differences were apparent in the ankle, knee, and hip joints. The implications of these findings are that OA patients rely more heavily on their unaffected sides than the affected side in stair ascent, a strategy that may be detrimental to the unaffected joint health. The reduction in affected limb loading in stair descent is thought to be related to minimizing pain.

EMG-Driven Musculoskeletal Model Calibration With Wrapping Surface Personalization

Muscle forces and joint moments estimated by electromyography (EMG)-driven musculoskeletal models are sensitive to the wrapping surface geometry defining muscle-tendon lengths and moment arms. Despite this sensitivity, wrapping surface properties are typically not personalized to subject movement data. This study developed a novel method for personalizing OpenSim cylindrical wrapping surfaces during EMG-driven model calibration. To avoid the high computational cost of repeated OpenSim muscle analyses, the method uses two-level polynomial surrogate models. Outer-level models specify time-varying muscle-tendon lengths and moment arms as functions of joint angles, while inner-level models specify time-invariant outer-level polynomial coefficients as functions of wrapping surface parameters. To evaluate the method, we used walking data collected from two individuals post-stroke and performed four variations of EMG-driven lower extremity model calibration: 1) no calibration of scaled generic wrapping surfaces (NGA), 2) calibration of outer-level polynomial coefficients for all muscles (SGA), 3) calibration of outer-level polynomial coefficients only for muscles with wrapping surfaces (LSGA), and 4) calibration of cylindrical wrapping surface parameters for muscles with wrapping surfaces (PGA). On average compared to NGA, SGA reduced lower extremity joint moment matching errors by 31%, LSGA by 24%, and PGA by 12%, with the largest reductions occurring at the hip. Furthermore, PGA reduced peak hip joint contact force by 47% bodyweight, which was the most consistent with published in vivo measurements. The proposed method for EMG-driven model calibration with wrapping surface personalization produces physically realistic OpenSim models that reduce joint moment matching errors while improving prediction of hip joint contact force.

Hamstring harvest results in significantly reduced knee muscular protection during side-step cutting two years after anterior cruciate ligament reconstruction

The purpose of this study was to determine the effect of donor muscle morphology following tendon harvest in anterior cruciate ligament (ACL) reconstruction on muscular support of the tibiofemoral joint during sidestep cutting. Magnetic resonance imaging (MRI) was used to measure peak cross-sectional area (CSA) and volume of the semitendinosus (ST) and gracilis (GR) muscles and tendons (bilaterally) in 18 individuals following ACL reconstruction. Participants performed sidestep cutting tasks in a biomechanics laboratory during which lower-limb electromyography, ground reaction loads, whole-body motions were recorded. An EMG driven neuro-musculoskeletal model was subsequently used to determine force from 34 musculotendinous units of the lower limb and the contribution of the ST and GR to muscular support of the tibiofemoral joint based on a normal muscle-tendon model (Standard model). Then, differences in peak CSA and volume between the ipsilateral/contralateral ST and GR were used to adjust their muscle-tendon parameters in the model followed by a recalibration to determine muscle force for 34 musculotendinous units (Adjusted model). The combined contribution of the donor muscles to muscular support about the medial and lateral compartments were reduced by 52% and 42%, respectively, in the adjusted compared to standard model. While the semimembranosus (SM) increased its contribution to muscular stabilisation about the medial and lateral compartment by 23% and 30%, respectively. This computer simulation study demonstrated the muscles harvested for ACL reconstruction reduced their support of the tibiofemoral joint during sidestep cutting, while the SM may have the potential to partially offset these reductions. This suggests donor muscle impairment could be a factor that contributes to ipsilateral re-injury rates to the ACL following return to sport.

Sensitivity of Model-Based Predictions of Post-TKA Kinematic Behavior to Residual Errors in Ultrasound-Based Knee Collateral Ligament Strain Assessment

Ultrasound-based ligament strain estimation shows promise in non-invasively assessing knee joint collateral ligament behavior and improving ligament balancing procedures. However, the impact of ultrasound-based strain estimation residual errors on in-silico arthroplasty predictions remains unexplored. We investigated the sensitivity of post-arthroplasty kinematic predictions to ultrasound-based strain estimation errors compared to clinical inaccuracies in implant positioning.Two cadaveric legs were submitted to active squatting, and specimen-specific rigid computer models were formulated. Mechanical properties of the ligament model were optimized to reproduce experimentally obtained tibiofemoral kinematics and loads with minimal error. Resulting remaining errors were comparable to the current state-of-the-art. Ultrasound-derived strain residual errors were then introduced by perturbing lateral collateral ligament (LCL) and medial collateral ligament (MCL) stiffness. Afterwards, the implant position was perturbed to match with the current clinical inaccuracies reported in the literature. Finally, the impact on simulated post-arthroplasty tibiofemoral kinematics was compared for both perturbation scenarios. Ultrasound-based errors minimally affected kinematic outcomes (mean differences < 0.73° in rotations, 0.1 mm in translations). Greatest differences occurred in external tibial rotations (-0.61° to 0.73° for MCL, -0.28° to 0.27° for LCL). Comparatively, changes in implant position had larger effects, with mean differences up to 1.95° in external tibial rotation and 0.7 mm in mediolateral translation. In conclusion, our study demonstrated that the ultrasound-based assessment of collateral ligament strains has the potential to enhance current computer-based pre-operative knee arthroplasty planning.

Comparative validation of two patient-specific modelling pipelines for predicting knee joint forces during level walking

Over the past few years, the use of computer models and simulations tailored to the patient's physiology to assist clinical decision-making has increased enormously.While several pipelines to develop personalized models exist, their adoption on a large scale is still limited due to the required niche computational skillset and the lengthy operations required. Novel toolboxes, such as STAPLE, promise to streamline and expedite the development of image-based skeletal lower limb models. STAPLE-generated models can be rapidly generated, with minimal user input, and present similar joint kinematics and kinetics compared to models developed employing the established INSIGNEO pipeline. Yet, it is unclear how much the observed discrepancies scale up and affect joint contact force predictions. In this study, we compared image-based musculoskeletal models developed (i) with the INSIGNEO pipeline and (ii) with a semi-automated pipeline that combines STAPLE and nmsBuilder, and assessed their accuracy against experimental implant data.Our results showed that both pipelines predicted similar total knee joint contact forces between one another in terms of profiles and average values, characterized by a moderately high level of agreement with the experimental data. Nonetheless, the Student t-test revealed statistically significant differences between both pipelines. Of note, the STAPLE-based pipeline required considerably less time than the INSIGNEO pipeline to generate a musculoskeletal model (i.e., 60 vs 160 min). This is likely to open up opportunities for the use of personalized musculoskeletal models in clinical practice, where time is of the essence.

The History and Future of Neuromusculoskeletal Biomechanics

The Executive Council of the International Society of Biomechanics has initiated and overseen the commemorations of the Society's 50th Anniversary in 2023. This included multiple series of lectures at the ninth World Congress of Biomechanics in 2022 and XXIXth Congress of the International Society of Biomechanics in 2023, all linked to special issues of International Society of Biomechanics' affiliated journals. This special issue of the Journal of Applied Biomechanics is dedicated to the biomechanics of the neuromusculoskeletal system. The reader is encouraged to explore this special issue which comprises 6 papers exploring the current state-of the-art, and future directions and roles for neuromusculoskeletal biomechanics. This editorial presents a very brief history of the science of the neuromusculoskeletal system's 4 main components: the central nervous system, musculotendon units, the musculoskeletal system, and joints, and how they biomechanically integrate to enable an understanding of the generation and control of human movement. This also entails a quick exploration of contemporary neuromusculoskeletal biomechanics and its future with new fields of application.

A Narrative Review of Personalized Musculoskeletal Modeling Using the Physiome and Musculoskeletal Atlas Projects

In this narrative review, we explore developments in the field of computational musculoskeletal model personalization using the Physiome and Musculoskeletal Atlas Projects. Model geometry personalization; statistical shape modeling; and its impact on segmentation, classification, and model creation are explored. Examples include the trapeziometacarpal and tibiofemoral joints, Achilles tendon, gastrocnemius muscle, and pediatric lower limb bones. Finally, a more general approach to model personalization is discussed based on the idea of multiscale personalization called scaffolds.

AddBiomechanics: Automating model scaling, inverse kinematics, and inverse dynamics from human motion data through sequential optimization

Creating large-scale public datasets of human motion biomechanics could unlock data-driven breakthroughs in our understanding of human motion, neuromuscular diseases, and assistive devices. However, the manual effort currently required to process motion capture data and quantify the kinematics and dynamics of movement is costly and limits the collection and sharing of large-scale biomechanical datasets. We present a method, called AddBiomechanics, to automate and standardize the quantification of human movement dynamics from motion capture data. We use linear methods followed by a non-convex bilevel optimization to scale the body segments of a musculoskeletal model, register the locations of optical markers placed on an experimental subject to the markers on a musculoskeletal model, and compute body segment kinematics given trajectories of experimental markers during a motion. We then apply a linear method followed by another non-convex optimization to find body segment masses and fine tune kinematics to minimize residual forces given corresponding trajectories of ground reaction forces. The optimization approach requires approximately 3-5 minutes to determine a subjecťs skeleton dimensions and motion kinematics, and less than 30 minutes of computation to also determine dynamically consistent skeleton inertia properties and fine-tuned kinematics and kinetics, compared with about one day of manual work for a human expert. We used AddBiomechanics to automatically reconstruct joint angle and torque trajectories from previously published multi-activity datasets, achieving close correspondence to expert-calculated values, marker root-mean-square errors less than 2 c m , and residual force magnitudes smaller than 2 % of peak external force. Finally, we confirmed that AddBiomechanics accurately reproduced joint kinematics and kinetics from synthetic walking data with low marker error and residual loads. We have published the algorithm as an open source cloud service at AddBiomechanics.org, which is available at no cost and asks that users agree to share processed and de-identified data with the community. As of this writing, hundreds of researchers have used the prototype tool to process and share about ten thousand motion files from about one thousand experimental subjects. Reducing the barriers to processing and sharing high-quality human motion biomechanics data will enable more people to use state-of-the-art biomechanical analysis, do so at lower cost, and share larger and more accurate datasets.

Medial and lateral knee contact forces during walking, stair ascent and stair descent are more affected by contact locations than tibiofemoral alignment in knee osteoarthritis patients with varus malalignment

Introduction: Knee OA progression is related to medial knee contact forces, which can be altered by anatomical parameters of tibiofemoral alignment and contact point locations. There is limited and controversial literature on medial-lateral force distribution and the effect of anatomical parameters, especially in motor activities different from walking. We analyzed the effect of tibiofemoral alignment and contact point locations on knee contact forces, and the medial-lateral force distribution in knee OA subjects with varus malalignment during walking, stair ascending and stair descending. Methods: Fifty-one knee OA subjects with varus malalignment underwent weight-bearing radiographs and motion capture during walking, stair ascending and stair descending. We created a set of four musculoskeletal models per subject with increasing level of personalization, and calculated medial and lateral knee contact forces. To analyze the effect of the anatomical parameters, statistically-significant differences in knee contact forces among models were evaluated. Then, to analyze the force distribution, the medial-to-total contact force ratios were calculated from the fully-informed models. In addition, a multiple regression analysis was performed to evaluate correlations between forces and anatomical parameters. Results: The anatomical parameters significantly affected the knee contact forces. However, the contact points decreased medial forces and increased lateral forces and led to more marked variations compared to tibiofemoral alignment, which produced an opposite effect. The forces were less medially-distributed during stair negotiation, with medial-to-total ratios below 50% at force peaks. The anatomical parameters explained 30%-67% of the variability in the knee forces, where the medial contact points were the best predictors of medial contact forces. Discussion: Including personalized locations of contact points is crucial when analyzing knee contact forces in subjects with varus malalignment, and especially the medial contact points have a major effect on the forces rather than tibiofemoral alignment. Remarkably, the medial-lateral force distribution depends on the motor activity, where stair ascending and descending show increased lateral forces that lead to less medially-distributed loads compared to walking.

Validation of a Biomechanical Injury and Disease Assessment Platform Applying an Inertial-Based Biosensor and Axis Vector Computation

Inertial kinetics and kinematics have substantial influences on human biomechanical function. A new algorithm for Inertial Measurement Unit (IMU)-based motion tracking is presented in this work. The primary aims of this paper are to combine recent developments in improved biosensor technology with mainstream motion-tracking hardware to measure the overall performance of human movement based on joint axis-angle representations of limb rotation. This work describes an alternative approach to representing three-dimensional rotations using a normalized vector around which an identified joint angle defines the overall rotation, rather than a traditional Euler angle approach. Furthermore, IMUs allow for the direct measurement of joint angular velocities, offering the opportunity to increase the accuracy of instantaneous axis of rotation estimations. Although the axis-angle representation requires vector quotient algebra (quaternions) to define rotation, this approach may be preferred for many graphics, vision, and virtual reality software applications. The analytical method was validated with laboratory data gathered from an infant dummy leg's flexion and extension knee movements and applied to a living subject's upper limb movement. The results showed that the novel approach could reasonably handle a simple case and provide a detailed analysis of axis-angle migration. The described algorithm could play a notable role in the biomechanical analysis of human joints and offers a harbinger of IMU-based biosensors that may detect pathological patterns of joint disease and injury.

Validation of a Robotic Testbench for Evaluating Biomechanical Effects of Implant Rotation in Total Knee Arthroplasty on a Cadaveric Specimen

In this study, we developed and validated a robotic testbench to investigate the biomechanical compatibility of three total knee arthroplasty (TKA) configurations under different loading conditions, including varus-valgus and internal-external loading across defined flexion angles. The testbench captured force-torque data, position, and quaternion information of the knee joint. A cadaver study was conducted, encompassing a native knee joint assessment and successive TKA testing, featuring femoral component rotations at -5°, 0°, and +5° relative to the transepicondylar axis of the femur. The native knee showed enhanced stability in varus-valgus loading, with the +5° external rotation TKA displaying the smallest deviation, indicating biomechanical compatibility. The robotic testbench consistently demonstrated high precision across all loading conditions. The findings demonstrated that the TKA configuration with a +5° external rotation displayed the minimal mean deviation under internal-external loading, indicating superior joint stability. These results contribute meaningful understanding regarding the influence of different TKA configurations on knee joint biomechanics, potentially influencing surgical planning and implant positioning. We are making the collected dataset available for further biomechanical model development and plan to explore the 6 Degrees of Freedom (DOF) robotic platform for additional biomechanical analysis. This study highlights the versatility and usefulness of the robotic testbench as an instrumental tool for expanding our understanding of knee joint biomechanics.

Smooth and accurate predictions of joint contact force time-series in gait using over parameterised deep neural networks

Alterations in joint contact forces (JCFs) are thought to be important mechanisms for the onset and progression of many musculoskeletal and orthopaedic pain disorders. Computational approaches to JCFs assessment represent the only non-invasive means of estimating in-vivo forces; but this cannot be undertaken in free-living environments. Here, we used deep neural networks to train models to predict JCFs, using only joint angles as predictors. Our neural network models were generally able to predict JCFs with errors within published minimal detectable change values. The errors ranged from the lowest value of 0.03 bodyweight (BW) (ankle medial-lateral JCF in walking) to a maximum of 0.65BW (knee VT JCF in running). Interestingly, we also found that over parametrised neural networks by training on longer epochs (>100) resulted in better and smoother waveform predictions. Our methods for predicting JCFs using only joint kinematics hold a lot of promise in allowing clinicians and coaches to continuously monitor tissue loading in free-living environments.

[Innovative image-based planning in musculoskeletal surgery]

BACKGROUND

For the preparation of surgical procedures in orthopedics and trauma surgery, precise knowledge of imaging and the three-dimensional imagination of the surgeon are of outstanding importance. Image-based, preoperative two-dimensional planning is the gold standard in arthroplasty today. In complex cases, further imaging such as computed tomography (CT) or magnetic resonance imaging is also performed, generating a three-dimensional model of the body region and helping the surgeon in the planning of the surgical treatment. Four-dimensional, dynamic CT studies have also been reported and are available as a complementary tool.

DIGITAL AIDS

Furthermore, digital aids should generate an improved representation of the pathology to be treated and optimize the surgeon's imagination. The finite element method allows patient-specific and implant-specific parameters to be taken into account in preoperative surgical planning. Intraoperatively, relevant information can be provided by augmented reality without significantly influencing the surgical workflow.

Musculotendon Parameters in Lower Limb Models: Simplifications, Uncertainties, and Muscle Force Estimation Sensitivity

Musculotendon parameters are key factors in the Hill-type muscle contraction dynamics, determining the muscle force estimation accuracy of a musculoskeletal model. Their values are mostly derived from muscle architecture datasets, whose emergence has been a major impetus for model development. However, it is often not clear if such parameter update indeed improves simulation accuracy. Our goal is to explain to model users how these parameters are derived and how accurate they are, as well as to what extent errors in parameter values might influence force estimation. We examine in detail the derivation of musculotendon parameters in six muscle architecture datasets and four prominent OpenSim models of the lower limb, and then identify simplifications which could add uncertainties to the derived parameter values. Finally, we analyze the sensitivity of muscle force estimation to these parameters both numerically and analytically. Nine typical simplifications in parameter derivation are identified. Partial derivatives of the Hill-type contraction dynamics are derived. Tendon slack length is determined as the musculotendon parameter that muscle force estimation is most sensitive to, whereas pennation angle is the least impactful. Anatomical measurements alone are not enough to calibrate musculotendon parameters, and the improvement on muscle force estimation accuracy will be limited if the source muscle architecture datasets are the only main update. Model users may check if a dataset or model is free of concerning factors for their research or application requirements. The derived partial derivatives may be used as the gradient for musculotendon parameter calibration. For model development, we demonstrate that it is more promising to focus on other model parameters or components and seek alternative strategies to further increase simulation accuracy.

Finite element analysis of a healthy knee joint at deep squatting for the study of tibiofemoral and patellofemoral contact

Background

In non-western countries, deep squatting is a daily activity, and prolonged deep squatting is common among occupational squatters. Household tasks, taking a bath, socializing, using toilets, and performing religious acts are among the activities frequently carried out while squatting by the Asian population. High knee loading is responsible for a knee injury and osteoarthritis. Finite element analysis is an effective tool to determine stresses on the knee joint.

Methods

Magnetic Resonance Imaging (MRI) and Computed Tomographic (CT) images were acquired of one adult without knee injuries. The CT images were acquired at the fully extended knee and one more set of images was acquired with the knee at a deeply flexed knee position. The MRI was acquired with the fully extended knee. 3-Dimensional models of bones were created using CT and soft tissue using MRI with the help of 3D Slicer software. Kinematics and finite element analysis of the knee was performed for standing and deep squatting posture using Ansys Workbench 2022.

Results

High peak stresses were observed at deep squatting compared to standing along with the reduction in the contact area. Peak von Mises stresses on femoral cartilage, tibial cartilage, patellar cartilage, and meniscus were increased from 3.3 MPa to 19.9 MPa, 2.9 MPa to 12.4 MPa, 1.5 MPa to 16.7 MPa and 15.8 MPa to 32.8 MPa respectively during deep squatting. Posterior translation of 7.01 mm, and 12.58 mm was observed for medial and lateral femoral condyle respectively from full extension to 153° knee flexion.

Conclusions

Increased stresses in the knee joint at deep squat posture may cause cartilage damage. A sustained deep squat posture should be avoided for healthy knee joints. More posterior translations of the medial femoral condyle at higher knee flexion angle warrant further investigation.

Ten steps to becoming a musculoskeletal simulation expert: A half-century of progress and outlook for the future

Over the past half-century, musculoskeletal simulations have deepened our knowledge of human and animal movement. This article outlines ten steps to becoming a musculoskeletal simulation expert so you can contribute to the next half-century of technical innovation and scientific discovery. We advocate looking to the past, present, and future to harness the power of simulations that seek to understand and improve mobility. Instead of presenting a comprehensive literature review, we articulate a set of ideas intended to help researchers use simulations effectively and responsibly by understanding the work on which today's musculoskeletal simulations are built, following established modeling and simulation principles, and branching out in new directions.

The Use of Wearable Inertial Sensors and Workplace-Based Exercises to Reduce Lateral Epicondylitis in the Workstation of a Textile Logistics Center

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.

Subject-specific tribo-contact conditions in total knee replacements: a simulation framework across scales

Fundamental knowledge about in vivo kinematics and contact conditions at the articulating interfaces of total knee replacements are essential for predicting and optimizing their behavior and durability. However, the prevailing motions and contact stresses in total knee replacements cannot be precisely determined using conventional in vivo measurement methods. In silico modeling, in turn, allows for a prediction of the loads, velocities, deformations, stress, and lubrication conditions across the scales during gait. Within the scope of this paper, we therefore combine musculoskeletal modeling with tribo-contact modeling. In the first step, we compute contact forces and sliding velocities by means of inverse dynamics approach and force-dependent kinematic solver based upon experimental gait data, revealing contact forces during healthy/physiological gait of young subjects. In a second step, the derived data are employed as input data for an elastohydrodynamic model based upon the finite element method full-system approach taking into account elastic deformation, the synovial fluid's hydrodynamics as well as mixed lubrication to predict and discuss the subject-specific pressure and lubrication conditions.

Overcoming Challenges and Innovations in Orthopedic Prosthesis Design: An Interdisciplinary Perspective

Recent advances in the orthopedic prostheses design have significantly improved the quality of life for individuals with orthopedic disabilities. However, there are still critical challenges that need to be addressed to further enhance the functionality of orthopedic prostheses improving biocompatibility to promote better integration with natural tissues, enhancing durability to withstand the demands of daily use, and improving sensory feedback for better control of movement are the most pressing issues. To address these challenges, promising emerging solutions such as smart prosthetics, 3D printing, regenerative medicine, and artificial intelligence have been developed. These innovative technologies hold the potential to significantly enhance the functionality of orthopedic prostheses. Realizing the full potential of these next-generation orthopedic prostheses requires addressing several critical factors. These include interdisciplinary collaboration between experts in orthopedics, materials science, biology, and engineering, increased investment in research and development, standardization of components to ensure quality and reliability, and improved access to prosthetics. A comprehensive review of these challenges and considerations for future orthopedic prosthesis design is s provided in this paper addressing the further advances to the field. By addressing these issues, we can continue to improve the lives of individuals with orthopedic disabilities and further enhance the field of orthopedic prosthetics.

Conformity design can change the effect of tibial component malrotation on knee biomechanics after total knee arthroplasty

BACKGROUND

Component alignment is essential to improve knee function and survival in total knee arthroplasty. However, it is still unclear whether the conformity design of tibiofemoral component can mitigate abnormal knee biomechanics caused by component malrotation. The purpose of this study was to investigate whether the sagittal/coronal conformity design of the tibial component could change the effect of the tibial component malrotation on knee biomechanics in total knee arthroplasty.

METHODS

A developed patient-specific musculoskeletal multi-body dynamics model of total knee arthroplasty was used to investigate the effects of the sagittal/coronal conformity of the tibial component on knee contact forces and kinematics caused by tibial component malrotation during the walking gait.

FINDINGS

Medial and lateral contact forces, internal-external rotation, and anterior-posterior translation were significantly affected by tibial component malrotation after total knee arthroplasty during the walking gait. The lower sagittal conformity of the tibial component can mitigate the abnormal internal-external rotation caused by tibial component malrotation in total knee arthroplasty, the higher coronal conformity of the tibial component can mitigate the abnormal medial-lateral translation caused by tibial component malrotation in total knee arthroplasty.

INTERPRETATION

This study highlights the importance of the tibiofemoral conformity designs on knee biomechanics caused by component malrotation in total knee arthroplasty. The optimization of the tibiofemoral conformity designs should be thoroughly considered in the design of new implants and in the planning of surgical procedures.

Peak Tibiofemoral Contact Forces Estimated Using IMU-Based Approaches Are Not Significantly Different from Motion Capture-Based Estimations in Patients with Knee Osteoarthritis

Altered tibiofemoral contact forces represent a risk factor for osteoarthritis onset and progression, making optimization of the knee force distribution a target of treatment strategies. Musculoskeletal model-based simulations are a state-of-the-art method to estimate joint contact forces, but they typically require laboratory-based input and skilled operators. To overcome these limitations, ambulatory methods, relying on inertial measurement units, have been proposed to estimated ground reaction forces and, consequently, knee contact forces out-of-the-lab. This study proposes the use of a full inertial-capture-based musculoskeletal modelling workflow with an underlying probabilistic principal component analysis model trained on 1787 gait cycles in patients with knee osteoarthritis. As validation, five patients with knee osteoarthritis were instrumented with 17 inertial measurement units and 76 opto-reflective markers. Participants performed multiple overground walking trials while motion and inertial capture methods were synchronously recorded. Moderate to strong correlations were found for the inertial capture-based knee contact forces compared to motion capture with root mean square error between 0.15 and 0.40 of body weight. The results show that our workflow can inform and potentially assist clinical practitioners to monitor knee joint loading in physical therapy sessions and eventually assess long-term therapeutic effects in a clinical context.

The influence of induced gait asymmetry on joint reaction forces

Chronic injury- or disease-induced joint impairments result in asymmetric gait deviations that may precipitate changes in joint loading associated with pain and osteoarthritis. Understanding the impact of gait deviations on joint reaction forces (JRFs) is challenging because of concurrent neurological and/or anatomical changes and because measuring JRFs requires medically invasive instrumented implants. Instead, we investigated the impact of joint motion limitations and induced asymmetry on JRFs by simulating data recorded as 8 unimpaired participants walked with bracing to unilaterally and bilaterally restrict ankle, knee, and simultaneous ankle + knee motion. Personalized models, calculated kinematics, and ground reaction forces (GRFs) were input into a computed muscle control tool to determine lower limb JRFs and simulated muscle activations guided by electromyography-driven timing constraints. Unilateral knee restriction increased GRF peak and loading rate ipsilaterally but peak values decreased contralaterally when compared to walking without joint restriction. GRF peak and loading rate increased with bilateral restriction compared to the contralateral limb of unilaterally restricted conditions. Despite changes in GRFs, JRFs were relatively unchanged due to reduced muscle forces during loading response. Thus, while joint restriction results in increased limb loading, reductions in muscle forces counteract changes in limb loading such that JRFs were relatively unchanged.