A Systematic Review of the Associations Between Inverse Dynamics and Musculoskeletal Modeling to Investigate Joint Loading in a Clinical Environment

Impact of model geometry and joint center locations on inverse kinematic/dynamic predictions: A comparative study of sexually dimorphic models

This work illustrates the sensitivity of demographically characteristic body segment inertial properties and subject-specific customization on model performance. One characteristic demographic, gender, and one subject-specific characteristic, hip joint center location, were represented with musculoskeletal modeling to evaluate how design decisions may alter model outputs. Generic sexually dimorphic musculoskeletal models were developed from the commonly used Rajagopal model using male and female data adapted by Dumas et al. Hip joint centers of these models were adjusted based on functional joint center testing. The kinematics and dynamics of 40 gait cycles from four subjects are predicted using these models. Two-way analysis of variance (ANOVA) was performed on the continuous time series data using statistical parametric mapping (SPM) to assess changes in kinematics/dynamics due to either choice in model (Rajagopal vs Dumas) or whether joint center adjustment was performed. The SPM based two-way ANOVA of the inverse dynamics found that differences in the Rajagopal and Dumas models resulted in significant differences in sagittal plane moments during swing (0.115 ± 0.032 Nm/kg difference in mean hip flexion moment during initial swing and a 0.077 ± 0.041 Nm/kg difference in mean hip extension moment during terminal swing), and differences between the models with and without hip joint center adjustment resulted in significant differences in hip flexion and abduction moments during stance (0.217 ± 0.055 Nm/kg increased mean hip abductive moment). By comparing the outputs of these differently constructed models with each other, the study finds that dynamic predictions of stance are sensitive to positioning of joint centers, and dynamic predictions of swing are more sensitive to segment mass/inertial properties.

Comparison of kinematics and joint moments calculations for lower limbs during gait using markerless and marker-based motion capture

Objective: This study aimed at quantifying the difference in kinematic and joint moments calculation for lower limbs during gait utilizing a markerless motion system (TsingVA Technology, Beijing, China) in comparison to values estimated using a marker-based motion capture system (Nokov Motion Capture System, Beijing, China). Methods: Sixteen healthy participants were recruited for the study. The kinematic data of the lower limb during walking were acquired simultaneously based on the markerless motion capture system (120 Hz) and the marker-based motion capture system (120 Hz). The ground reaction force was recorded synchronously using a force platform (1,200 Hz). The kinematic and force data were input into Visual3D for inverse dynamics calculations. Results: The difference in the lower limb joint center position between the two systems was the least at the ankle joint in the posterior/anterior direction, with the mean absolute deviation (MAD) of 0.74 cm. The least difference in measuring lower limb angles between the two systems was found in flexion/extension movement, and the greatest difference was found in internal/external rotation movement. The coefficient of multiple correlations (CMC) of the lower limb three joint moments for both systems exceeded or equaled 0.75, except for the ad/abduction of the knee and ankle. All the Root Mean Squared Deviation (RMSD) of the lower limb joint moment are below 18 N·m. Conclusion: The markerless motion capture system and marker-based motion capture system showed a high similarity in kinematics and inverse dynamic calculation for lower limbs during gait in the sagittal plane. However, it should be noted that there is a notable deviation in ad/abduction moments at the knee and ankle.

Hip Contact Forces During Sprinting in Femoroacetabular Impingement Syndrome

PURPOSE

Sprinting often provokes hip pain in individuals with femoroacetabular impingement syndrome (FAIS). Asphericity of the femoral head-neck junction (cam morphology) characteristic of FAIS can increase the risk of anterior-superior acetabular cartilage damage. This study aimed to 1) compare hip contact forces (magnitude and direction) during sprinting between individuals with FAIS, asymptomatic cam morphology (CAM), and controls without cam morphology, and 2) identify the phases of sprinting with high levels of anteriorly directed hip contact forces.

METHODS

Forty-six recreationally active individuals with comparable levels of physical activity were divided into three groups (FAIS, 14; CAM, 15; control, 17) based on their history of hip/groin pain, results of clinical impingement tests, and presence of cam morphology (alpha angle >55°). Three-dimensional marker trajectories, ground reaction forces, and electromyograms from 12 lower-limb muscles were recorded during 10-m overground sprinting trials. A linearly scaled electromyogram-informed neuromusculoskeletal model was used to calculate hip contact force magnitude (resultant, anterior-posterior, inferior-superior, medio-lateral) and angle (sagittal and frontal planes). Between-group comparisons were made using two-sample t -tests via statistical parametric mapping ( P < 0.05).

RESULTS

No significant differences in magnitude or direction of hip contact forces were observed between FAIS and CAM or between FAIS and control groups during any phase of the sprint cycle. The highest anteriorly directed hip contact forces were observed during the initial swing phase of the sprint cycle.

CONCLUSIONS

Hip contact forces during sprinting do not differentiate recreationally active individuals with FAIS from asymptomatic individuals with and without cam morphology. Hip loading during early swing, where peak anterior loading occurs, may be a potential mechanism for cartilage damage during sprinting-related sports in individuals with FAIS and/or asymptomatic cam morphology.

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.

Machine learning-based prediction of hip joint moment in healthy subjects, patients and post-operative subjects

The application of machine learning in the field of motion capture research is growing rapidly. The purpose of the study is to implement a long-short term memory (LSTM) model able to predict sagittal plane hip joint moment (HJM) across three distinct cohorts (healthy controls, patients and post-operative patients) starting from 3D motion capture and force data. Statistical parametric mapping with paired samples t-test was performed to compare machine learning and inverse dynamics HJM predicted values, with the latter used as gold standard. The results demonstrated favorable model performance on each of the three cohorts, showcasing its ability to successfully generalize predictions across diverse cohorts.

Osteoarthritis year in review 2023: Biomechanics

Biomechanics plays a significant yet complex role in osteoarthritis (OA) onset and progression. Identifying alterations in biomechanical factors and their complex interactions is critical for gaining new insights into OA pathophysiology and identification of clearly defined and modifiable mechanical treatment targets. This review synthesized biomechanics studies from March 2022 to April 2023, from which three themes relating to human gait emerged: (1) new insights into the pathogenesis of OA using computational modeling and machine learning, (2) technology-enhanced biomechanical interventions for OA, and (3) out-of-lab biomechanical assessments of OA. We further highlighted future-focused areas which may continue to advance the field of biomechanics in OA, with a particular emphasis on exploiting technology to understand and treat biomechanical mechanisms of OA outside the laboratory. The breadth of studies included in this review highlights the complex role of biomechanics in OA and showcase numerous innovative and outstanding contributions to the field. Exciting cross-disciplinary efforts integrating computational modeling, mobile sensors, and machine learning methods show great promise for streamlining in vivo multi-scale biomechanics workflows and are expected to underpin future breakthroughs in the understanding and treatment of biomechanics in OA.

Elbow joint loads during simulated activities of daily living: implications for formulating recommendations after total elbow arthroplasty

BACKGROUND

Overloading of the elbow joint prosthesis following total elbow arthroplasty can lead to implant failure. Joint moments during daily activities are not well contextualized for a prosthesis's failure limits, and the effect of the current postoperative instruction on elbow joint loading is unclear. This study investigates the difference in elbow joint moments between simulated daily tasks and between flexion-extension, pronation-supination, and varus-valgus movement directions. Additionally, the effect of the current postoperative instruction on elbow joint load is examined.

METHODS

Nine healthy participants (age 45.8 ± 17 years, 3 males) performed 8 tasks; driving a car, opening a door, rising from a chair, lifting, sliding, combing hair, drinking, emptying cup, without and with the instruction "not lifting more than 1 kg." Upper limb kinematics and hand contact forces were measured. Elbow joint angles and net moments were analyzed using inverse dynamic analysis, where the net moments are estimated from movement data and external forces.

RESULTS

Peak elbow joint moments differed significantly between tasks (P < .01) and movement directions (P < .01). The most and least demanding tasks were, rising from a chair (13.4 Nm extension, 5.0 Nm supination, and 15.2 Nm valgus) and sliding (4.3 Nm flexion, 1.7 Nm supination, and 2.6 Nm varus). Net moments were significantly reduced after instruction only in the chair task (P < .01).

CONCLUSION

This study analyzed elbow joint moments in different directions during daily tasks. The outcomes question whether postoperative instruction can lead to decreasing elbow loads. Future research might focus on reducing elbow loads in the flexion-extension and varus-valgus directions.

Influence of femoral anteversion angle and neck-shaft angle on muscle forces and joint loading during walking

Femoral deformities, e.g. increased or decreased femoral anteversion (AVA) and neck-shaft angle (NSA), can lead to pathological gait patterns, altered joint loads, and degenerative joint diseases. The mechanism how femoral geometry influences muscle forces and joint load during walking is still not fully understood. The objective of our study was to investigate the influence of femoral AVA and NSA on muscle forces and joint loads during walking. We conducted a comprehensive musculoskeletal modelling study based on three-dimensional motion capture data of a healthy person with a typical gait pattern. We created 25 musculoskeletal models with a variety of NSA (93°-153°) and AVA (-12°-48°). For each model we calculated moment arms, muscle forces, muscle moments, co-contraction indices and joint loads using OpenSim. Multiple regression analyses were used to predict muscle activations, muscle moments, co-contraction indices, and joint contact forces based on the femoral geometry. We found a significant increase in co-contraction of hip and knee joint spanning muscles in models with increasing AVA and NSA, which led to a substantial increase in hip and knee joint contact forces. Decreased AVA and NSA had a minor impact on muscle and joint contact forces. Large AVA lead to increases in both knee and hip contact forces. Large NSA (153°) combined with large AVA (48°) led to increases in hip joint contact forces by five times body weight. Low NSA (108° and 93°) combined with large AVA (48°) led to two-fold increases in the second peak of the knee contact forces. Increased joint contact forces in models with increased AVA and NSA were linked to changes in hip muscle moment arms and compensatory increases in hip and knee muscle forces. Knowing the influence of femoral geometry on muscle forces and joint loads can help clinicians to improve treatment strategies in patients with femoral deformities.

Joint and Limb Loading during Gait in Adults with ACL Reconstruction: Comparison between Single-Step and Cumulative Load Metrics

PURPOSE

Individuals with anterior cruciate ligament reconstruction (ACLR) generally exhibit limb underloading behaviors during walking, but most research focuses on per-step comparisons. Cumulative loading metrics offer unique insight into joint loading as magnitude, duration, and total steps are considered, but few studies have evaluated if cumulative loads are altered post-ACLR. Here, we evaluated if underloading behaviors are apparent in ACLR limbs when using cumulative load metrics and how load metrics change in response to walking speed modifications.

METHODS

Treadmill walking biomechanics were evaluated in 21 participants with ACLR at three speeds (self-selected (SS); 120% SS and 80% SS). Cumulative loads per step and per kilometer were calculated using knee flexion and adduction moment (KFM and KAM) and vertical ground reaction force (GRF) impulses. Traditional magnitude metrics for KFM, KAM, and GRF were also calculated.

RESULTS

The ACLR limb displayed smaller KFM and GRF in early and late stances, but larger KFM and GRF during midstance compared with the contralateral limb ( P < 0.01). Only GRF cumulative loads (per step and per kilometer) were reduced in the ACLR limb ( P < 0.01). In response to speed modifications, load magnitudes generally increased with speed. Conversely, cumulative load metrics (per step and per kilometer) decreased at faster speeds and increased at slow speeds ( P < 0.01).

CONCLUSIONS

Patients with ACLR underload their knee in the sagittal plane per step, but cumulatively over the course of many steps/distance, this underloading phenomenon was not apparent. Furthermore, cumulative load increased at slower speeds, opposite to what is identified with traditional single-step metrics. Assessing cumulative load metrics may offer additional insight into how load outcomes may be impacted in injured populations or in response to gait modifications.

Performance of multiple neural networks in predicting lower limb joint moments using wearable sensors

Joint moment measurements represent an objective biomechemical parameter in joint health assessment. Inverse dynamics based on 3D motion capture data is the current 'gold standard' to estimate joint moments. Recently, machine learning combined with data measured by wearable technologies such electromyography (EMG), inertial measurement units (IMU), and electrogoniometers (GON) has been used to enable fast, easy, and low-cost measurements of joint moments. This study investigates the ability of various deep neural networks to predict lower limb joint moments merely from IMU sensors. The performance of five different deep neural networks (InceptionTimePlus, eXplainable convolutional neural network (XCM), XCMplus, Recurrent neural network (RNNplus), and Time Series Transformer (TSTPlus)) were tested to predict hip, knee, ankle, and subtalar moments using acceleration and gyroscope measurements of four IMU sensors at the trunk, thigh, shank, and foot. Multiple locomotion modes were considered including level-ground walking, treadmill walking, stair ascent, stair descent, ramp ascent, and ramp descent. We show that XCM can accurately predict lower limb joint moments using data of only four IMUs with RMSE of 0.046 ± 0.013 Nm/kg compared to 0.064 ± 0.003 Nm/kg on average for the other architectures. We found that hip, knee, and ankle joint moments predictions had a comparable RMSE with an average of 0.069 Nm/kg, while subtalar joint moments had the lowest RMSE of 0.033 Nm/kg. The real-time feedback that can be derived from the proposed method can be highly valuable for sports scientists and physiotherapists to gain insights into biomechanics, technique, and form to develop personalized training and rehabilitation programs.

Simulated Tibiofemoral Joint Reaction Forces for Three Previously Studied Gait Modifications in Healthy Controls

Gait modifications, such as lateral trunk lean (LTL), medial knee thrust (MKT), and toe-in gait (TIG), are frequently investigated interventions used to slow the progression of knee osteoarthritis. The Lerner knee model was developed to estimate the tibiofemoral joint reaction forces (JRF) in the medial and lateral compartments during gait. These models may be useful for estimating the effects on the JRF in the knee as a result of gait modifications. We hypothesized that all gait modifications would decrease the JRF compared to normal gait. Twenty healthy individuals volunteered for this study (26.7 ± 4.7 years, 1.75 ± 0.1 m, 73.4 ± 12.4 kg). Ten trials were collected for normal gait as well as for the three gait modifications: LTL, MKT, and TIG. The data were used to estimate the JRF in the first and second peaks for the medial and lateral compartments of the knee via opensim using the Lerner knee model. No significant difference from baseline was found for the first peak in the medial compartment. There was a decrease in JRF in the medial compartment during the loading phase of gait for TIG (6.6%) and LTL (4.9%) and an increasing JRF for MKT (2.6%). but none was statistically significant. A significant increase from baseline was found for TIG (5.8%) in the medial second peak. We found a large variation in individual responses to gait interventions, which may help explain the lack of statistically significant results. Possible factors influencing these wide ranges of responses to gait modifications include static alignment and the impacts of variation in muscle coordination strategies used, by participants, to implement gait modifications.

Prediction of ground reaction forces and moments during walking in children with cerebral palsy

Introduction

Gait analysis is increasingly used to support clinical decision-making regarding diagnosis and treatment planning for movement disorders. As a key part of gait analysis, inverse dynamics can be applied to estimate internal loading conditions during movement, which is essential for understanding pathological gait patterns. The inverse dynamics calculation uses external kinetic information, normally collected using force plates. However, collection of external ground reaction forces (GRFs) and moments (GRMs) can be challenging, especially in subjects with movement disorders. In recent years, a musculoskeletal modeling-based approach has been developed to predict external kinetics from kinematic data, but its performance has not yet been evaluated for altered locomotor patterns such as toe-walking. Therefore, the goal of this study was to investigate how well this prediction method performs for gait in children with cerebral palsy.

Methods

The method was applied to 25 subjects with various forms of hemiplegic spastic locomotor patterns. Predicted GRFs and GRMs, in addition to associated joint kinetics derived using inverse dynamics, were statistically compared against those based on force plate measurements.

Results

The results showed that the performance of the predictive method was similar for the affected and unaffected limbs, with Pearson correlation coefficients between predicted and measured GRFs of 0.71–0.96, similar to those previously reported for healthy adults, despite the motor pathology and the inclusion of toes-walkers within our cohort. However, errors were amplified when calculating the resulting joint moments to an extent that could influence clinical interpretation.

Conclusion

To conclude, the musculoskeletal modeling-based approach for estimating external kinetics is promising for pathological gait, offering the possibility of estimating GRFs and GRMs without the need for force plate data. However, further development is needed before implementation within clinical settings becomes possible.

Patellofemoral joint load and knee abduction/adduction moment are sensitive to variations in femoral version and individual muscle forces

Torsional profiles of the lower limbs, such as femoral anteversion, can dictate gait and mobility, joint biomechanics and pain, and functional impairment. It currently remains unclear how the interactions between femoral anteversion, kinematics, and muscle activity patterns contribute to joint biomechanics and thus conditions such as knee pain. This study presents a computational modeling approach to investigating the interactions between femoral anteversion, muscle forces, and knee joint loads. We employed an optimal control approach to produce actuator and muscle-driven simulations of the stance phase of gait for femoral anteversion angles ranging from -8° (retroversion) to 52° (anteversion) with a typically developing baseline of 12° of anteversion and implemented a Monte Carlo analysis for variations in lower limb muscle forces. While total patellofemoral joint load decreased with increasing femoral anteversion, patellofemoral joint load alignment worsened, and knee abduction/adduction magnitude increased with both positive and negative changes in femoral anteversion (p < 0.001). The rectus femoris muscle was found to greatly influence patellofemoral joint loads across all femoral anteversion alignments (R > 0.8, p < 0.001), and the medial gastrocnemius was found to greatly influence knee abduction/adduction moments for the extreme version cases (R > 0.74, p < 0.001). Along with the vastus lateralis, which decreased with increasing femoral anteversion (R = 0.89, p < 0.001), these muscles are prime candidates for future experimental and clinical efforts to address joint pain in individuals with extreme femoral version. These findings, along with future modeling efforts, could help clinicians better design treatment strategies for knee joint pain in populations with extreme femoral anteversion or retroversion.

Peak knee joint moments accurately predict medial and lateral knee contact forces in patients with valgus malalignment

Compressive knee joint contact force during walking is thought to be related to initiation and progression of knee osteoarthritis. However, joint loading is often evaluated with surrogate measures, like the external knee adduction moment, due to the complexity of computing joint contact forces. Statistical models have shown promising correlations between medial knee joint contact forces and knee adduction moments in particularly in individuals with knee osteoarthritis or after total knee replacements (R² = 0.44-0.60). The purpose of this study was to evaluate how accurately model-based predictions of peak medial and lateral knee joint contact forces during walking could be estimated by linear mixed-effects models including joint moments for children and adolescents with and without valgus malalignment. Peak knee joint moments were strongly correlated (R² > 0.85, p < 0.001) with both peak medial and lateral knee joint contact forces. The knee flexion and adduction moments were significant covariates in the models, strengthening the understanding of the statistical relationship between both moments and medial and lateral knee joint contact forces. In the future, these models could be used to evaluate peak knee joint contact forces from musculoskeletal simulations using peak joint moments from motion capture software, obviating the need for time-consuming musculoskeletal simulations.

Estimation of the knee joint load using plantar pressure data measured by smart socks: A feasibility study

BACKGROUND

Unsupervised sports activities could cause traumas, about 70% of them are those of the low extremities. To avoid traumas, the athlete should be aware of dangerous forces acting within low extremity joints. Research in gait analysis indicated that plantar pressure alteration rate correlates with the gait pace. Thus, the changes in plantar pressure should correlate with the accelerations of extremities, and with the forces, acting in the joints. Smart socks provide a budget solution for the measurement of plantar pressure.

OBJECTIVE

To estimate the correlation between the plantar pressure, measured using smart socks, and forces, acting in the joints of the lower extremities.

METHODS

The research is case study based. The volunteer performed a set of squats. The arbitrary plantar pressure-related data were obtained using originally developed smart socks with embedded knitted pressure sensors. Simultaneously, the lower extremity motion data were recorded using two inertial measurement units, attached to the tight and the ankle, from which the forces acted in the knee joint were estimated. The simplest possible model of knee joint mechanics was used to estimate force.

RESULTS

The estimates of the plantar pressure and knee joint forces demonstrate a strong correlation (r= 0.75, P< 0.001). The established linear regression equation enables the calculation of the knee joint force with an uncertainty of 22% using the plantar pressure estimate. The accuracy of the classification of the joint force as excessive, i.e., being more than 90% of the maximal force, was 82%.

CONCLUSION

The results demonstrate the feasibility of the smart socks for the estimation of the forces in the knee joints. Smart socks therefore could be used to develop excessive joint force alert devices, that could replace less convenient inertial sensors.

Dynamic knee stiffness during walking is increased in individuals with anterior cruciate ligament reconstruction

Individuals with anterior cruciate ligament (ACL) reconstruction often display abnormal gait mechanics reflective of a "stiff-knee" gait (i.e., reduced knee flexion angles and moments). However, dynamic knee stiffness, which is the dynamic relationship between the position of the knee and the moment acting on it, has not been directly examined during walking in individuals with ACL reconstruction. Here, we aimed to evaluate dynamic knee stiffness in the involved compared to the uninvolved limb during weight-acceptance and mid-stance phases of walking. Twenty-six individuals who underwent ACL reconstruction (Age: 20.2 ± 5.1 yrs., Time post-op: 7.2 ± 0.9 mo.) completed an overground walking assessment using a three-dimensional motion capture system and two force plates. Dynamic knee stiffness (Nm/°) was calculated as the slope of the regression line during weight-acceptance and midstance, obtained by plotting the sagittal plane knee angle versus knee moment. Paired t-tests with Bonferroni corrections were used to compare differences in dynamic stiffness, knee excursions, and moment ranges between limbs during both stance phases. Greater dynamic knee stiffness was found in the involved compared with the uninvolved limb during weight-acceptance and mid-stance (p < 0.01). Knee flexion and extension excursions were reduced in the involved limb during both weight-acceptance and mid-stance, respectively (p < 0.01). Sagittal plane knee moment ranges were not different between limbs during weight-acceptance (p = 0.1); however, the involved limb moment range was reduced relative to the uninvolved limb during mid-stance (p < 0.01). These results indicate that individuals with ACL reconstruction walk with a stiffer knee throughout stance, which may influence knee contact forces and could contribute to the high propensity for post-traumatic knee osteoarthritis development in this population.

Frontal plane knee moment in clinical gait analysis: A systematic review on the effect of kinematic gait changes

INTRODUCTION

The frontal plane knee moment (KAM1 and KAM2) derived from non-invasive three-dimensional gait analysis is a surrogate measure for knee joint load and of great interest in clinical and research settings. Many aspects can influence this measure either unintentionally or purposely in order to reduce the knee joint load to relieve symptoms and pain. All these aspects must be known when conducting a study or interpreting gait data for clinical decision-making.

METHODS

This systematic review was registered with PROSPERO (CRD42020187038). Pubmed and Web of Science were searched for peer-reviewed, original research articles in which unshod three-dimensional gait analysis was undertaken and KAM1 and KAM2 were included as an outcome variable. Two reviewers independently screened articles for inclusion, extracted data and performed a methodological quality assessment using Downs and Black checklist.

RESULTS

In total, 42 studies were included. Based on the independent variable investigated, these studies were divided into three groups: 1) gait modifications, 2) individual characteristics and 3) idiopathic orthopedic deformities. Among others, fast walking speeds (1) were found to increase KAM1; There were no sex-related differences (2) and genu valgum (3) reduces KAM1 and KAM2.

CONCLUSION

While consistent use of terminology and reporting of KAM is required for meta-analysis, this review indicates that gait modifications (speed, trunk lean, step width), individual characteristics (body weight, age) and idiopathic orthopedic deformities (femoral or tibial torsion, genu valgum/varum) influence KAM magnitudes during walking. These factors should be considered by researchers when designing studies (especially of longitudinal design) or by clinicians when interpreting data for surgical and therapeutic decision-making.

Association between foot posture and tibiofemoral contact forces during barefoot walking in patients with knee osteoarthritis

Background

Accumulating evidence indicates that abnormal foot posture are risk factors for knee osteoarthritis (OA). However, the relationship between foot posture and tibiofemoral contact force (CF) during habitual weight-bearing activities remains unclear. This study aimed to determine the association between tibiofemoral CF and foot posture while walking.

Methods

In total, 18 patients with knee OA and 18 healthy individuals participated in this cross-sectional study. Foot parameters were evaluated by Foot Posture Index (FPI), Staheli Arch Index (SAI), hallux valgus angle, calcaneus inverted angle relative to the floor as a static rearfoot posture, navicular height, and toe grip strength. In addition, all participants underwent kinetic and kinematic measurements during a self-selected speed gait. The measurement device used was the three-dimensional motion analysis system with a sampling rate of 120 Hz. The musculoskeletal model, which has 92 Hill-type muscle–tendon units with 23 degrees of freedom, was used to calculate tibiofemoral CF. Partial correlations was used to investigate the association between foot parameters and total, medial, and lateral tibiofemoral CF of the first and second peaks while controlling for gait speed.

Results

A significant negative correlation was observed between Walking SAI and first peak medial tibiofemoral CF in control participants (r = -0.505, p = 0.039). SAI was also significantly positively correlated with first peak medial tibiofemoral CF in patients with knee OA (r = 0.482, p = 0.042).

Conclusions

Our findings revealed a correlation between the medial first peak tibiofemoral CF and the SAI. This study indicates that people with knee OA and flatfoot have excessive first medial tibiofemoral CF during walking.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-05624-y.

Predicting the Internal Knee Abduction Impulse During Walking Using Deep Learning

Knee joint moments are commonly calculated to provide an indirect measure of knee joint loads. A shortcoming of inverse dynamics approaches is that the process of collecting and processing human motion data can be time-consuming. This study aimed to benchmark five different deep learning methods in using walking segment kinematics for predicting internal knee abduction impulse during walking. Three-dimensional kinematic and kinetic data used for the present analyses came from a publicly available dataset on walking (participants n = 33). The outcome for prediction was the internal knee abduction impulse over the stance phase. Three-dimensional (3D) angular and linear displacement, velocity, and acceleration of the seven lower body segment’s center of mass (COM), relative to a fixed global coordinate system were derived and formed the predictor space (126 time-series predictors). The total number of observations in the dataset was 6,737. The datasets were split into training (75%, n = 5,052) and testing (25%, n = 1685) datasets. Five deep learning models were benchmarked against inverse dynamics in quantifying knee abduction impulse. A baseline 2D convolutional network model achieved a mean absolute percentage error (MAPE) of 10.80%. Transfer learning with InceptionTime was the best performing model, achieving the best MAPE of 8.28%. Encoding the time-series as images then using a 2D convolutional model performed worse than the baseline model with a MAPE of 16.17%. Time-series based deep learning models were superior to an image-based method when predicting knee abduction moment impulse during walking. Future studies looking to develop wearable technologies will benefit from knowing the optimal network architecture, and the benefit of transfer learning for predicting joint moments.

Inertial Motion Capture-Based Wearable Systems for Estimation of Joint Kinetics: A Systematic Review

In biomechanics, joint kinetics has an important role in evaluating the mechanical load of the joint and understanding its motor function. Although an optical motion capture (OMC) system has mainly been used to evaluate joint kinetics in combination with force plates, inertial motion capture (IMC) systems have recently been emerging in joint kinetic analysis due to their wearability and ubiquitous measurement capability. In this regard, numerous studies have been conducted to estimate joint kinetics using IMC-based wearable systems. However, these have not been comprehensively addressed yet. Thus, the aim of this review is to explore the methodology of the current studies on estimating joint kinetic variables by means of an IMC system. From a systematic search of the literature, 48 studies were selected. This paper summarizes the content of the selected literature in terms of the (i) study characteristics, (ii) methodologies, and (iii) study results. The estimation methods of the selected studies are categorized into two types: the inverse dynamics-based method and the machine learning-based method. While these two methods presented different characteristics in estimating the kinetic variables, it was demonstrated in the literature that both methods could be applied with good performance for the kinetic analysis of joints in different daily activities.

Comparison of Gait Symmetry and Joint Moments in Unilateral and Bilateral Hip Osteoarthritis Patients and Healthy Controls

Patients with unilateral hip osteoarthritis show a characteristic gait pattern in which they unload the affected leg and overload the unaffected leg. Information on the gait characteristics of patients with bilateral hip osteoarthritis is very limited. The main purposes of this study were to investigate whether the gait pattern of both legs of patients with bilateral hip osteoarthritis deviates from healthy controls and whether bilateral hip osteoarthritis patients show a more symmetrical joint load compared to unilateral hip osteoarthritis patients. In this prospective study, 26 patients with bilateral hip osteoarthritis, 26 patients with unilateral hip osteoarthritis and 26 healthy controls were included. The three groups were matched for gender, age and walking speed. Patients were scheduled for a unilateral total hip arthroplasty on the more affected/more painful side. All participants underwent a three-dimensional gait analysis. Gait kinematics and gait kinetics of patients and controls were compared using Statistical Parametric Mapping. Corrected for speed, the gait kinematics and kinetics of both legs of patients with bilateral hip osteoarthritis differed from healthy controls. Bilateral patients had symmetrical knee joint loading, in contrast to the asymmetrical knee joint loading in unilateral hip osteoarthritis patients. The ipsilateral leg of the bilateral patients could be included in studies in addition to unilateral hip osteoarthritis patients as no differences were found. Although patients with bilateral hip osteoarthritis show more symmetrical frontal plane knee joint moments, a pathological external knee adduction moment in the second half of stance was present in the ipsilateral leg in patients with unilateral and bilateral hip osteoarthritis. The lateral adjustment of the knee adduction moment may initiate or accelerate progression of degenerative changes in the lateral compartment of the knee.

Impact of subject-specific step width modification on the knee and hip adduction moments during gait

BACKGROUND

Patients with hip osteoarthritis (OA) exhibit an increased step width (SW) during walking before and up to 2 years after total hip arthroplasty. Wider SW is associated with a reduction in the external knee adduction moment (KAM), but there is a lack of research regarding the effect of SW on the hip adduction moment (HAM).

RESEARCH QUESTION

Is a wider SW an effective compensatory mechanism to reduce the hip joint loading? We hypothesized that (1) an increased SW reduces, (2) a decreased SW increases the KAM/HAM, and (3) secondary kinematic gait changes have an effect on the KAM/HAM.

METHODS

Twenty healthy individuals (24.0 ± 2.5 years of age) underwent instrumented gait analyses with 4 different subject-specific SW modifications (habitual, halved, double, and triple SW). The resulting external KAMs and HAMs were compared using statistical parametric mapping (SPM).

RESULTS

Post hoc testing demonstrated significantly lower HAM for both the double (p < 0.001, 15-31 % and 61-98 % of the stance phase) and the triple SW (p < 0.001, 1-36 % and 58-98 %) compared to the habitual SW. The extent of the reduction at the first and second peak was comparable for HAM (15-25 % reduction) and less pronounced at the first peak of KAM (9-11 % reduction) compared to the second peak of KAM (19-28 % reduction). In contrast, halving the SW did not lead to a significant change in KAM or HAM compared to the habitual SW (p > 0.009).

SIGNIFICANCE

An increase in SW is an effective and simple gait mechanism to reduce the frontal plane knee and hip joint moments. However, hypothesis 2 could not be confirmed, as halving the SW did not cause a significant change in KAM or HAM. Given the results of the present study, gait retraining with regard to an increased SW may be an adequate, noninvasive option for the treatment of patients with hip OA.

[Dynamic analysis of joint loading due to leg axis deformity in the frontal plane : Relevance of instrumented gait analysis]

BACKGROUND

Instrumented 3D gait analysis (IGA) has been established for the functional evaluation of orthopedic diseases. It can provide valuable additional information beyond conventional static radiographic diagnostics and, thus, contributes to treatment decisions and a successful surgical outcome. Regarding the assessment of leg axis deformities IGA is currently only used in a few specialized centers.

PRACTICE

This article describes the methods used by IGA and shows its benefit for the treatment of leg axis deformities of the knee in the frontal plane. In particular, the calculation of dynamic joint loads provides important insights regarding the development of degenerative joint deformities in the knee joint and, thus, complements the static assessment of the leg axis. A new treatment algorithm for guided growth intervention in children and adolescents by temporary epiphysiodesis is presented. IGA can be particularly useful for clinical decision-making in borderline cases. If there is a discrepancy between the static leg axis and dynamic knee joint loading, IGA can reveal potential compensatory mechanisms during walking.