A calibrated EMG-informed neuromusculoskeletal model can appropriately account for muscle co-contraction in the estimation of hip joint contact forces in people with hip osteoarthritis

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.

Hip biomechanics in early recovery following fixation of intertrochanteric fractures: Results from a randomised controlled trial

Single and dual integrated screw femoral nails are both commonly used to treat intertrochanteric fractures. This study investigated if using single or dual integrated screw femoral nails result in different post-operative hip joint loading. In the presence of differences, we investigated potential contributing factors. Patients were randomised for treatment via single screw (Stryker, Gamma3) or dual-integrated screw nail (Smith and Nephew, Intertan). Pre-injury mobility levels were collected at enrolment. Hip radiographs and gait data were collected at six weeks (Gamma: 16; Intertan: 15) and six months (Gamma: 14; Intertan: 13) follow-up. The resultant hip joint reaction forces and abductor muscle forces were estimated using electromyography-assisted neuromusculoskeletal modelling during level walking gait. Our primary analysis focused on the resultant hip joint reaction force and abductor muscle forces. We compared between groups, across stance phase of walking gait, using statistical parametric mapping. At six weeks, the Intertan group showed a short (∼5% of stance phase) but substantial (33 % [0.3 × body weight] greater magnitude) resultant hip joint reaction force when compared to the Gamma group (P = 0.022). Higher gluteus medius forces (P = 0.009) were demonstrated in the Intertan group at six weeks. Harris Hip Scores followed the trend seen for the biomechanical outcomes with superior scores for the Intertan group at six weeks postoperative (P = 0.044). The use of dual-integrated screw femoral nails over single screw devices may allow for hip biomechanics more closely resembling normal hip function at earlier post-operative timepoints, but these appear to resolve by six months postoperative.

Hip contact forces can be predicted with a neural network using only synthesised key points and electromyography in people with hip osteoarthritis

OBJECTIVE

To develop and validate a neural network to estimate hip contact forces (HCF), and lower body kinematics and kinetics during walking in individuals with hip osteoarthritis (OA) using synthesised anatomical key points and electromyography. To assess the capability of the neural network to detect directional changes in HCF resulting from prescribed gait modifications.

DESIGN

A calibrated electromyography-informed neuromusculoskeletal model was used to compute lower body joint angles, moments, and HCF for 17 participants with mild-to-moderate hip OA. Anatomical key points (e.g., joint centres) were synthesised from marker trajectories and augmented with bias and noise expected from computer vision-based pose estimation systems. Temporal convolutional and long short-term memory neural networks (NN) were trained using leave-one-subject-out validation to predict neuromusculoskeletal modelling outputs from the synthesised key points and measured electromyography data from 5 hip-spanning muscles.

RESULTS

HCF was predicted with an average error of 13.4 ± 7.1% of peak force. Joint angles and moments were predicted with an average root-mean-square-error of 5.3 degrees and 0.10 Nm/kg, respectively. The NN could detect changes in peak HCF that occur due to gait modifications with good agreement with neuromusculoskeletal modelling (r2 = 0.72) and a minimum detectable change of 9.5%.

CONCLUSION

The developed neural network predicted HCF and lower body joint angles and moments in individuals with hip OA using noisy synthesised key point locations with acceptable errors. Changes in HCF magnitude due to gait modifications were predicted with high accuracy. These findings have important implications for implementation of load-modification based gait retraining interventions for people with hip OA in a natural environment (i.e., home, clinic).

Muscle synergy-informed neuromusculoskeletal modelling to estimate knee contact forces in children with cerebral palsy

Cerebral palsy (CP) includes a group of neurological conditions caused by damage to the developing brain, resulting in maladaptive alterations of muscle coordination and movement. Estimates of joint moments and contact forces during locomotion are important to establish the trajectory of disease progression and plan appropriate surgical interventions in children with CP. Joint moments and contact forces can be estimated using electromyogram (EMG)-informed neuromusculoskeletal models, but a reduced number of EMG sensors would facilitate translation of these computational methods to clinics. This study developed and evaluated a muscle synergy-informed neuromusculoskeletal modelling approach using EMG recordings from three to four muscles to estimate joint moments and knee contact forces of children with CP and typically developing (TD) children during walking. Using only three to four experimental EMG sensors attached to a single leg and leveraging an EMG database of walking data of TD children, the synergy-informed approach estimated total knee contact forces comparable to those estimated by EMG-assisted approaches that used 13 EMG sensors (children with CP, n = 3, R2 = 0.95 ± 0.01, RMSE = 0.40 ± 0.14 BW; TD controls, n = 3, R2 = 0.93 ± 0.07, RMSE = 0.19 ± 0.05 BW). The proposed synergy-informed neuromusculoskeletal modelling approach could enable rapid evaluation of joint biomechanics in children with unimpaired and impaired motor control within a clinical environment.

Effect of different constraining boundary conditions on simulated femoral stresses and strains during gait

Finite element analysis (FEA) is commonly used in orthopaedic research to estimate localised tissue stresses and strains. A variety of boundary conditions have been proposed for isolated femur analysis, but it remains unclear how these assumed constraints influence FEA predictions of bone biomechanics. This study compared the femoral head deflection (FHD), stresses, and strains elicited under four commonly used boundary conditions (fixed knee, mid-shaft constraint, springs, and isostatic methods) and benchmarked these mechanics against the gold standard inertia relief method for normal and pathological femurs (extreme anteversion and retroversion, coxa vara, and coxa valga). Simulations were performed for the stance phase of walking with the applied femoral loading determined from patient-specific neuromusculoskeletal models. Due to unrealistic biomechanics observed for the commonly used boundary conditions, we propose a novel biomechanical constraint method to generate physiological femur biomechanics. The biomechanical method yielded FHD (< 1 mm), strains (approaching 1000 µε), and stresses (< 60 MPa), which were consistent with physiological observations and similar to predictions from the inertia relief method (average coefficient of determination = 0.97, average normalized root mean square error = 0.17). Our results highlight the superior performance of the biomechanical method compared to current methods of constraint for  both healthy and pathological femurs.

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.

Comparison of Synergy Extrapolation and Static Optimization for Estimating Multiple Unmeasured Muscle Activations during Walking

Background

Calibrated electromyography (EMG)-driven musculoskeletal models can provide great insight into internal quantities (e.g., muscle forces) that are difficult or impossible to measure experimentally. However, the need for EMG data from all involved muscles presents a significant barrier to the widespread application of EMG-driven modeling methods. Synergy extrapolation (SynX) is a computational method that can estimate a single missing EMG signal with reasonable accuracy during the EMG-driven model calibration process, yet its performance in estimating a larger number of missing EMG signals remains unclear.

Methods

This study assessed the accuracy with which SynX can use eight measured EMG signals to estimate muscle activations and forces associated with eight missing EMG signals in the same leg during walking while simultaneously performing EMG-driven model calibration. Experimental gait data collected from two individuals post-stroke, including 16 channels of EMG data per leg, were used to calibrate an EMG-driven musculoskeletal model, providing "gold standard" muscle activations and forces for evaluation purposes. SynX was then used to predict the muscle activations and forces associated with the eight missing EMG signals while simultaneously calibrating EMG-driven model parameter values. Due to its widespread use, static optimization (SO) was also utilized to estimate the same muscle activations and forces. Estimation accuracy for SynX and SO was evaluated using root mean square errors (RMSE) to quantify amplitude errors and correlation coefficient r values to quantify shape similarity, each calculated with respect to "gold standard" muscle activations and forces.

Results

On average, SynX produced significantly more accurate amplitude and shape estimates for unmeasured muscle activations (RMSE 0.08 vs. 0.15 , r value 0.55 vs. 0.12) and forces (RMSE 101.3 N vs. 174.4   N , r value 0.53 vs. 0.07) compared to SO. SynX yielded calibrated Hill-type muscle-tendon model parameter values for all muscles and activation dynamics model parameter values for measured muscles that were similar to "gold standard" calibrated model parameter values.

Conclusions

These findings suggest that SynX could make it possible to calibrate EMG-driven musculoskeletal models for all important lower-extremity muscles with as few as eight carefully chosen EMG signals and eventually contribute to the design of personalized rehabilitation and surgical interventions for mobility impairments.

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.

A framework based on subject-specific musculoskeletal models and Monte Carlo simulations to personalize muscle coordination retraining

Excessive loads at lower limb joints can lead to pain and degenerative diseases. Altering joint loads with muscle coordination retraining might help to treat or prevent clinical symptoms in a non-invasive way. Knowing how much muscle coordination retraining can reduce joint loads and which muscles have the biggest impact on joint loads is crucial for personalized gait retraining. We introduced a simulation framework to quantify the potential of muscle coordination retraining to reduce joint loads for an individuum. Furthermore, the proposed framework enables to pinpoint muscles, which alterations have the highest likelihood to reduce joint loads. Simulations were performed based on three-dimensional motion capture data of five healthy adolescents (femoral torsion 10°-29°, tibial torsion 19°-38°) and five patients with idiopathic torsional deformities at the femur and/or tibia (femoral torsion 18°-52°, tibial torsion 3°-50°). For each participant, a musculoskeletal model was modified to match the femoral and tibial geometry obtained from magnetic resonance images. Each participant's model and the corresponding motion capture data were used as input for a Monte Carlo analysis to investigate how different muscle coordination strategies influence joint loads. OpenSim was used to run 10,000 simulations for each participant. Root-mean-square of muscle forces and peak joint contact forces were compared between simulations. Depending on the participant, altering muscle coordination led to a maximum reduction in hip, knee, patellofemoral and ankle joint loads between 5 and 18%, 4% and 45%, 16% and 36%, and 2% and 6%, respectively. In some but not all participants reducing joint loads at one joint increased joint loads at other joints. The required alteration in muscle forces to achieve a reduction in joint loads showed a large variability between participants. The potential of muscle coordination retraining to reduce joint loads depends on the person's musculoskeletal geometry and gait pattern and therefore showed a large variability between participants, which highlights the usefulness and importance of the proposed framework to personalize gait retraining.

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.

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.

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.

Influence of different calibration methods on surface electromyography-informed musculoskeletal models with few input signals

BACKGROUND

Although model personalization is critical when assessing individuals with morphological or neurological abnormalities, or even non-disabled subjects, its translation into routine clinical settings is hampered by the cumbersomeness of experimental data acquisition and lack of resources, which are linked to high costs and long processing pipelines. Quantifying the impact of neglecting subject-specific information in simulations that aim to estimate muscle forces with surface electromyography informed modeling approaches, can address their potential in relevant clinical questions. The present study investigates how different methods to fine-tune subject-specific neuromuscular parameters, reducing the number of electromyography input data, could affect the estimation of the unmeasured excitations and the musculotendon forces.

METHODS

Three-dimensional motion analysis was performed on 8 non-disabled adult subjects and 13 electromyographic signals captured. Four neuromusculoskeletal models were created for 8 participants: a reference model driven by a large set of sEMG signals; two models informed by four electromyographic signals but calibrated in different fashions; a model based on static optimization.

FINDINGS

The electromyography-informed models better predicted experimental excitations, including the unmeasured ones. The model based on static optimization obtained less reliable predictions of the experimental data. When comparing the different reduced models, no major differences were observed, suggesting that the less complex model may suffice for predicting muscle forces with a small set of input in clinical gait analysis tasks.

INTERPRETATION

Quantitative model performance evaluation in different conditions provides an objective indication of which method yields the most accurate prediction when a small set of electromyographic recordings is available.

Electromyography-informed musculoskeletal modeling provides new insight into hand tendon forces during tennis forehand

Lateral epicondylitis, also known as tennis elbow, is a major health issue among tennis players. This musculo-skeletal disorder affects hand extensor tendons, results in substantial pain and impairments for sporting and everyday activities and requires several weeks of recovery. Unfortunately, prevention remains limited by the lack of data regarding biomechanical risk factors, especially because in vivo evaluation of hand tendon forces remains challenging. Electromyography-informed musculo-skeletal modeling is a noninvasive approach to provide physiological estimation of tendon forces based on motion capture and electromyography but was never applied to study hand tendon loading during tennis playing. The objective of this study was to develop such electromyography-informed musculo-skeletal model to provide new insight into hand tendon loading in tennis players. The model was tested with three-dimensional kinematics and electromyography data of two players performing forehand drives at two-shot speeds and with three rackets. Muscle forces increased with shot speed but were moderately affected by racket properties. Wrist prime extensors withstood the highest forces, but their relative implication compared to flexors depended on the player-specific grip force and racket motion strategy. When normalizing wrist extensor forces by shot speed and grip strength, up to threefold differences were observed between players, suggesting that gesture technique, for example, grip position or joint motion coordination, could play a role in the overloading of wrist extensor tendons. This study provided a new methodology for in situ analysis of hand biomechanical loadings during tennis gesture and shed a new light on lateral epicondylitis risk factors.

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.

Individual muscle contributions to the acceleration of the centre of mass during gait in people with mild-to-moderate hip osteoarthritis

BACKGROUND

People with mild-to-moderate hip osteoarthritis (OA) exhibit hip muscle weakness, alterations in hip kinematics and kinetics and hip contact forces during gait compared to healthy controls. However, it is unclear if those with hip OA use different motor control strategies to coordinate the motion of the centre of mass (COM) during gait. Such information could provide further critical assessment of conservative management strategies implemented for people with hip OA.

RESEARCH QUESTION

Do muscle contributions to the acceleration of the COM during walking differ between individuals with mild-to-moderate hip OA and controls?

METHODS

Eleven individuals with mild-to-moderate hip OA and 10 healthy controls walked at a self-selected speed while whole-body motion and ground reaction forces were measured. Muscle forces during gait were obtained using static optimisation and an induced acceleration analysis was performed to determine individual muscle contributions to the acceleration of the COM during single-leg stance (SLS). Between-group comparisons were made using independent t-tests via Statistical Parametric Modelling.

RESULTS

There were no between-group differences in spatial-temporal gait parameters or three-dimensional whole-body COM acceleration. The rectus femoris, biceps femoris, iliopsoas and gastrocnemius muscles in the hip OA group contributed less to the fore-aft accelerations of the COM (p < 0.05), and more to the vertical COM acceleration with the gluteus maximus (p < 0.05), during SLS, compared to the control group.

SIGNIFICANCE

Subtle differences exist in the way people with mild-to-moderate hip OA use their muscles to accelerate the whole-body centre of mass during the SLS phase of walking relative to healthy controls. These findings improve understanding of the complex functional consequences of hip OA and enhance our understanding of how to monitor the effectiveness of an intervention on biomechanical changes in gait in people with hip OA.

Maintaining soldier musculoskeletal health using personalised digital humans, wearables and/or computer vision

OBJECTIVES

The physical demands of military service place soldiers at risk of musculoskeletal injuries and are major concerns for military capability. This paper outlines the development new training technologies to prevent and manage these injuries.

DESIGN

Narrative review.

METHODS

Technologies suitable for integration into next-generation training devices were examined. We considered the capability of technologies to target tissue level mechanics, provide appropriate real-time feedback, and their useability in-the-field.

RESULTS

Musculoskeletal tissues' health depends on their functional mechanical environment experienced in military activities, training and rehabilitation. These environments result from the interactions between tissue motion, loading, biology, and morphology. Maintaining health of and/or repairing joint tissues requires targeting the "ideal" in vivo tissue mechanics (i.e., loading and strain), which may be enabled by real-time biofeedback. Recent research has shown that these biofeedback technologies are possible by integrating a patient's personalised digital twin and wireless wearable devices. Personalised digital twins are personalised neuromusculoskeletal rigid body and finite element models that work in real-time by code optimisation and artificial intelligence. Model personalisation is crucial in obtaining physically and physiologically valid predictions.

CONCLUSIONS

Recent work has shown that laboratory-quality biomechanical measurements and modelling can be performed outside the laboratory with a small number of wearable sensors or computer vision methods. The next stage is to combine these technologies into well-designed easy to use products.

Gluteal Muscle Forces during Hip-Focused Injury Prevention and Rehabilitation Exercises

PURPOSE

This study aimed to compare and rank gluteal muscle forces in eight hip-focused exercises performed with and without external resistance and describe the underlying fiber lengths, velocities, and muscle activations.

METHODS

Motion capture, ground reaction forces, and electromyography (EMG) were used as input to an EMG-informed neuromusculoskeletal model to estimate gluteus maximus, medius, and minimus muscle forces. Participants were 14 female footballers (18-32 yr old) with at least 3 months of lower limb strength training experience. Each participant performed eight hip-focused exercises (single-leg squat, split squat, single-leg Romanian deadlift [RDL], single-leg hip thrust, banded side step, hip hike, side plank, and side-lying leg raise) with and without 12 repetition maximum (RM) resistance. For each muscle, exercises were ranked by peak muscle force, and k-means clustering separated exercises into four tiers.

RESULTS

The tier 1 exercises for gluteus maximus were loaded split squat (95% confidence interval [CI] = 495-688 N), loaded single-leg RDL (95% CI = 500-655 N), and loaded single-leg hip thrust (95% CI = 505-640 N). The tier 1 exercises for gluteus medius were body weight side plank (95% CI = 338-483 N), loaded single-leg squat (95% CI = 278-422 N), and loaded single-leg RDL (95% CI = 283-405 N). The tier 1 exercises for gluteus minimus were loaded single-leg RDL (95% CI = 267-389 N) and body weight side plank (95% CI = 272-382 N). Peak gluteal muscle forces increased by 28-150 N when exercises were performed with 12RM external resistance compared with body weight only. Peak muscle force coincided with maximum fiber length for most exercises.

CONCLUSIONS

Gluteal muscle forces were exercise specific, and peak muscle forces increased by varying amounts when adding a 12RM external resistance. These findings may inform exercise selection by facilitating the targeting of individual gluteal muscles and optimization of mechanical loads to match performance, injury prevention, or rehabilitation training goals.

EMG-informed neuromuscular model assesses the effects of varied bodyweight support on muscles during overground walking

Bodyweight supported walking is a common gait rehabilitation method that can be used as an experimental approach to better understand walking biomechanics. Neuromuscular modeling can provide an analytical means to gain insight into how muscles coordinate to produce walking and other movements. To better understand how muscle length and velocity affect muscle force during overground walking with bodyweight support, we used an electromyography (EMG)-informed neuromuscular model to investigate changes in muscle parameters (muscle force, activation and fiber length) at varying bodyweight support levels: 0%, 24%, 45% and 69% bodyweight. Coupled constant force springs provided a vertical support force while we collected biomechanical data (EMG, motion capture and ground reaction forces) from healthy, neurologically intact participants walking at 1.20 ± 0.06 m/s. The lateral and medial gastrocnemius demonstrated a significant decrease in muscle force (lateral: p = 0.002 and medial: p < 0.001) and activation (lateral: p = 0.007 and medial: p < 0.001) through push-off at higher levels of support. The soleus, in contrast, had no significant change in muscle activation through push-off (p = 0.652) regardless of bodyweight support level even though soleus muscle force decreased with increasing support (p < 0.001). During push-off, the soleus had shorter muscle fiber lengths and faster shortening velocities as bodyweight support levels increased. These results provide insight into how muscle force can be decoupled from effective bodyweight during bodyweight supported walking due to changes in muscle fiber dynamics. The findings contribute evidence that clinicians and biomechanists should not expect a reduction in muscle activation and force when using bodyweight support to assist gait during rehabilitation.

Neuromusculoskeletal model calibration accounts for differences in electromechanical delay and maximum isometric muscle force

Electromechanical delay (EMD) and maximum isometric muscle force (F_o^M) are important parameters for joint contact force calculation with EMG-informed neuromusculoskeletal (NMS) models. These parameters can vary between tasks (EMD) and individuals (EMD and F_o^M), making it challenging to establish representative values. One promising approach is to personalise candidate parameters to the participant (e.g., F_o^M by regression equation) and then adjust all parameters within a calibration (i.e., numerical optimisation) to minimise error between corresponding pairs of experimental measures and model-predicted values. The purpose of this study was to determine whether calibration of an NMS model resulted in consistent joint contact forces, regardless of EMD value or personalisation of F_o^M. Hip, knee, and ankle contact forces were predicted for 28 participants using EMG-informed NMS models. Differences in joint contact forces with EMD were examined in six models, calibrated with EMD from 15 to 110 ms. Differences in joint contact forces with personalisation of F_o^M were examined in two models, both calibrated with the same initial EMD (50 ms), one with generic and one with personalised values for F_o^M. For all models, joint contact force peaks during the first and second halves of stance were extracted and compared using a repeated-measures analysis of variance. Calibrated models with EMD set between 35 and 70 ms produced similar magnitude and timing of peak joint contact forces. Compared with generic values, personalising and then calibrating F_o^M resulted in comparable peak contact forces at hip, but not knee or ankle, while also producing muscle-specific tensions similar to reported literature. Overall, EMD between 35 and 70 ms and personalised initial values of F_o^M before calibration are advised for EMG-informed NMS modelling.

Evaluating the risk of knee osteoarthritis following unilateral ACL reconstruction based on an EMG-assisted method

Objective: Anterior cruciate ligament reconstruction (ACLR) cannot decrease the risk of knee osteoarthritis after anterior cruciate ligament rupture, and tibial contact force is associated with the development of knee osteoarthritis. The purpose of this study was to compare the difference in bilateral tibial contact force for patients with unilateral ACLR during walking and jogging based on an EMG-assisted method in order to evaluate the risk of knee osteoarthritis following unilateral ACLR. Methods: Seven unilateral ACLR patients participated in experiments. The 14-camera motion capture system, 3-Dimension force plate, and wireless EMG test system were used to collect the participants' kinematics, kinetics, and EMG data during walking and jogging. A personalized neuromusculoskeletal model was established by combining scaling and calibration optimization. The inverse kinematics and inverse dynamics algorithms were used to calculate the joint angle and joint net moment. The EMG-assisted model was used to calculate the muscle force. On this basis, the contact force of the knee joint was analyzed, and the tibial contact force was obtained. The paired sample t-test was used to analyze the difference between the participants' healthy and surgical sides of the participants. Results: During jogging, the peak tibial compression force on the healthy side was higher than on the surgical side (p = 0.039). At the peak moment of tibial compression force, the muscle force of the rectus femoris (p = 0.035) and vastus medialis (p = 0.036) on the healthy side was significantly higher than that on the surgical side; the knee flexion (p = 0.042) and ankle dorsiflexion (p = 0.046) angle on the healthy side was higher than that on the surgical side. There was no significant difference in the first (p = 0.122) and second (p = 0.445) peak tibial compression forces during walking between the healthy and surgical sides. Conclusion: Patients with unilateral ACLR showed smaller tibial compression force on the surgical side than on the healthy side during jogging. The main reason for this may be the insufficient exertion of the rectus femoris and vastus medialis.

On the estimation of hip joint loads through musculoskeletal modeling

Noninvasive estimation of joint loads is still an open challenge in biomechanics. Although musculoskeletal modeling represents a solid resource, multiple improvements are still necessary to obtain accurate predictions of joint loads and to translate such potential into practical utility. The present study, focused on the hip joint, is aimed at reviewing the state-of-the-art literature on the estimation of hip joint reaction forces through musculoskeletal modeling. Our literature inspection, based on well-defined selection criteria, returned seventeen works, which were compared in terms of methods and results. Deviations between predicted and in vivo measured hip joint loads, taken from the OrthoLoad database, were assessed through quantitative deviation indices. Despite the numerous modeling and computational improvements made over the last two decades, predicted hip joint loads still deviate from their experimental counterparts and typically overestimate them. Several critical aspects have emerged that affect muscle force estimation, hence joint loads. Among them, the physical fidelity of the musculoskeletal model, with its parameters and geometry, plays a crucial role. Also, predicted joint loads are markedly affected by the selected muscle recruitment strategy, which reflects the underlying motor control policy. Practical guidelines for researchers interested in noninvasive estimation of hip joint loads are also provided.

EMG-driven musculoskeletal model calibration with estimation of unmeasured muscle excitations via synergy extrapolation

Subject-specific electromyography (EMG)-driven musculoskeletal models that predict muscle forces have the potential to enhance our knowledge of internal biomechanics and neural control of normal and pathological movements. However, technical gaps in experimental EMG measurement, such as inaccessibility of deep muscles using surface electrodes or an insufficient number of EMG channels, can cause difficulties in collecting EMG data from muscles that contribute substantially to joint moments, thereby hindering the ability of EMG-driven models to predict muscle forces and joint moments reliably. This study presents a novel computational approach to address the problem of a small number of missing EMG signals during EMG-driven model calibration. The approach (henceforth called "synergy extrapolation" or SynX) linearly combines time-varying synergy excitations extracted from measured muscle excitations to estimate 1) unmeasured muscle excitations and 2) residual muscle excitations added to measured muscle excitations. Time-invariant synergy vector weights defining the contribution of each measured synergy excitation to all unmeasured and residual muscle excitations were calibrated simultaneously with EMG-driven model parameters through a multi-objective optimization. The cost function was formulated as a trade-off between minimizing joint moment tracking errors and minimizing unmeasured and residual muscle activation magnitudes. We developed and evaluated the approach by treating a measured fine wire EMG signal (iliopsoas) as though it were "unmeasured" for walking datasets collected from two individuals post-stroke-one high functioning and one low functioning. How well unmeasured muscle excitations and activations could be predicted with SynX was assessed quantitatively for different combinations of SynX methodological choices, including the number of synergies and categories of variability in unmeasured and residual synergy vector weights across trials. The two best methodological combinations were identified, one for analyzing experimental walking trials used for calibration and another for analyzing experimental walking trials not used for calibration or for predicting new walking motions computationally. Both methodological combinations consistently provided reliable and efficient estimates of unmeasured muscle excitations and activations, muscle forces, and joint moments across both subjects. This approach broadens the possibilities for EMG-driven calibration of muscle-tendon properties in personalized neuromusculoskeletal models and may eventually contribute to the design of personalized treatments for mobility impairments.

Sensitivity analysis guided improvement of an electromyogram-driven lumped parameter musculoskeletal hand model

EMG-driven neuromusculoskeletal models have been used to study many impairments and hold great potential to facilitate human-machine interactions for rehabilitation. A challenge to successful clinical application is the need to optimize the model parameters to produce accurate kinematic predictions. In order to identify the key parameters, we used Monte-Carlo simulations to evaluate the sensitivities of wrist and metacarpophalangeal (MCP) flexion/extension prediction accuracies for an EMG-driven, lumped-parameter musculoskeletal model. Four muscles were modeled with 22 total optimizable parameters. Model predictions from EMG were compared with measured joint angles from 11 able-bodied subjects. While sensitivities varied by muscle, we determined muscle moment arms, maximum isometric force, and tendon slack length were highly influential, while passive stiffness and optimal fiber length were less influential. Removing the two least influential parameters from each muscle reduced the optimization search space from 22 to 14 parameters without significantly impacting prediction correlation (wrist: 0.90 ± 0.05 vs 0.90 ± 0.05, p = 0.96; MCP: 0.74 ± 0.20 vs 0.70 ± 0.23, p = 0.51) and normalized root mean square error (wrist: 0.18 ± 0.03 vs 0.19 ± 0.03, p = 0.16; MCP: 0.18 ± 0.06 vs 0.19 ± 0.06, p = 0.60). Additionally, we showed that wrist kinematic predictions were insensitive to parameters of the modeled MCP muscles. This allowed us to develop a novel optimization strategy that more reliably identified the optimal set of parameters for each subject (27.3 ± 19.5%) compared to the baseline optimization strategy (6.4 ± 8.1%; p = 0.004). This study demonstrated how sensitivity analyses can be used to guide model refinement and inform novel and improved optimization strategies, facilitating implementation of musculoskeletal models for clinical applications.

Assigning trabecular bone material properties in finite element models simulating the pelvis before and after the development of peri-prosthetic osteolytic lesions

Estimating strain distribution in the acetabulum before and after the development of peri-prosthetic osteolytic lesions secondary to total hip arthroplasty may assist with understanding the pathogenesis of this condition. This could be achieved by performing patient-specific finite element analysis of (1) total hip arthroplasty recipients with developed acetabular osteolytic lesions, and (2) models simulating the patient's pelvis and implant immediately after primary surgery. State of the art patient-specific total hip arthroplasty finite element analysis simulations obtain trabecular bone material properties from Hounsfield units within computed tomography (CT) scans of patients. However, this is not feasible when an implant is already in situ due to metal artefact disruption and, in turn, incorrectly reproduced Hounsfield units. Therefore, alternative methods of assigning trabecular bone material properties within such models were tested and strain results compared. It was found that assigning set material properties throughout the trabecular bone geometry was sufficient for the desired application. Simulating the primary implant and pelvis requires geometric and material based assumptions. Therefore, comparisons were made between strain values obtained from simulated primary models, from state of the art methods using material properties obtained from intact bone within a CT scan, and from models with osteolytic lesions. Strain values found using the finite element models simulating the pelvis before osteolytic lesion developed were considerably closer to those found using state of the art methods than those found for the bone loss models. These models could be used to determine relationships between strain distribution and factors such as bone loss.

Uncertainty in Muscle–Tendon Parameters can Greatly Influence the Accuracy of Knee Contact Force Estimates of Musculoskeletal Models

Understanding the sources of error is critical before models of the musculoskeletal system can be usefully translated. Using in vivo measured tibiofemoral forces, the impact of uncertainty in muscle–tendon parameters on the accuracy of knee contact force estimates of a generic musculoskeletal model was investigated following a probabilistic approach. Population variability was introduced to the routine musculoskeletal modeling framework by perturbing input parameters of the lower limb muscles around their baseline values. Using ground reaction force and skin marker trajectory data collected from six subjects performing body-weight squat, the knee contact force was calculated for the perturbed models. The combined impact of input uncertainties resulted in a considerable variation in the knee contact force estimates (up to 2.1 BW change in the predicted force), especially at larger knee flexion angles, hence explaining up to 70% of the simulation error. Although individual muscle groups exhibited different contributions to the overall error, variation in the maximum isometric force and pathway of the muscles showed the highest impacts on the model outcomes. Importantly, this study highlights parameters that should be personalized in order to achieve the best possible predictions when using generic musculoskeletal models for activities involving deep knee flexion.

Medial and Lateral Tibiofemoral Compressive Forces in Patients Following Unilateral Total Knee Arthroplasty During Stationary Cycling

Patients following unilateral total knee arthroplasty (TKA) display interlimb differences in knee joint kinetics during gait and more recently, stationary cycling. The purpose of this study was to use musculoskeletal modeling to estimate total, medial, and lateral tibiofemoral compressive forces for patients following TKA during stationary cycling. Fifteen patients of unilateral TKA, from the same surgeon, participated in cycling at 2 workrates (80 and 100 W). A knee model (OpenSim 3.2) was used to estimate total, medial, and lateral tibiofemoral compressive forces for replaced and nonreplaced limbs. A 2 × 2 (limb × workrate) and a 2 × 2 × 2 (compartment × limb × workrate) analysis of variance were run on the selected variables. Peak medial tibiofemoral compressive force was 23.5% lower for replaced compared to nonreplaced limbs (P = .004, G = 0.80). Peak medial tibiofemoral compressive force was 48.0% greater than peak lateral tibiofemoral compressive force in nonreplaced limbs (MD = 344.5 N, P < .001, G = 1.6) with no difference in replaced limbs (P = .274). Following TKA, patients have greater medial compartment loading on their nonreplaced compared to their replaced limbs and ipsilateral lateral compartment loading. This disproportionate loading may be cause for concern regarding exacerbating contralateral knee osteoarthritis.

Towards Tailored Rehabilitation by Implementation of a Novel Musculoskeletal Finite Element Analysis Pipeline

Tissue-level mechanics (e.g., stress and strain) are important factors governing tissue remodeling and development of knee osteoarthritis (KOA), and hence, the success of physical rehabilitation. To date, no clinically feasible analysis toolbox has been introduced and used to inform clinical decision making with subject-specific in-depth joint mechanics of different activities. Herein, we utilized a rapid state-of-the-art electromyography-assisted musculoskeletal finite element analysis toolbox with fibril-reinforced poro(visco)elastic cartilages and menisci to investigate knee mechanics in different activities. Tissue mechanical responses, believed to govern collagen damage, cell death, and fixed charge density loss of proteoglycans, were characterized within 15 patients with KOA while various daily activities and rehabilitation exercises were performed. Results showed more inter-participant variation in joint mechanics during rehabilitation exercises compared to daily activities. Accordingly, the devised workflow may be used for designing subject-specific rehabilitation protocols. Further, results showed the potential to tailor rehabilitation exercises, or assess capacity for daily activity modifications, to optimally load knee tissue, especially when mechanically-induced cartilage degeneration and adaptation are of interest.

An EMG-assisted muscle-force driven finite element analysis pipeline to investigate joint- and tissue-level mechanical responses in functional activities: towards a rapid assessment toolbox

Joint tissue mechanics (e.g., stress and strain) are believed to have a major involvement in the onset and progression of musculoskeletal disorders, e.g., knee osteoarthritis (KOA). Accordingly, considerable efforts have been made to develop musculoskeletal finite element (MS-FE) models to estimate highly detailed tissue mechanics that predict cartilage degeneration. However, creating such models is time-consuming and requires advanced expertise. This limits these complex, yet promising MS-FE models to research applications with few participants and makes the models impractical for clinical assessments. Also, these previously developed MS-FE models have not been used to assess activities other than gait. This study introduces and verifies a semi-automated rapid state-of-the-art MS-FE modeling and simulation toolbox incorporating an electromyography- (EMG) assisted MS model and a muscle-force driven FE model of the knee with fibril-reinforced poro(visco)elastic cartilages and menisci. To showcase the usability of the pipeline, we estimated joint- and tissue-level knee mechanics in 15 KOA individuals performing different daily activities. The pipeline was verified by comparing the estimated muscle activations and joint mechanics to existing experimental data. To determine the importance of EMG-assisted MS approach, results were compared to those from the same FE models but driven by static-optimization-based MS models. The EMG-assisted MS-FE pipeline bore a closer resemblance to experiments compared to the static-optimization-based MS-FE pipeline. Importantly, the developed pipeline showed great potential as a rapid MS-FE analysis toolbox to investigate multiscale knee mechanics during different activities of individuals with KOA. The template FE model of the study is freely available here.

Electromyography-Assisted Neuromusculoskeletal Models Can Estimate Physiological Muscle Activations and Joint Moments Across the Neck Before Impacts

Knowledge of neck muscle activation strategies before sporting impacts is crucial for investigating mechanisms of severe spinal injuries. However, measurement of muscle activations during impacts is experimentally challenging and computational estimations are not often guided by experimental measurements. We investigated neck muscle activations before impacts with the use of electromyography (EMG)-assisted neuromusculoskeletal models. Kinematics and EMG recordings from four major neck muscles of a rugby player were experimentally measured during rugby activities. A subject-specific musculoskeletal model was created with muscle parameters informed from MRI measurements. The model was used in the calibrated EMG-informed neuromusculoskeletal modeling toolbox and three neural solutions were compared: (i) static optimization (SO), (ii) EMG-assisted (EMGa), and (iii) MRI-informed EMG-assisted (EMGaMRI). EMGaMRI and EMGa significantly (p < 0.01) outperformed SO when tracking cervical spine net joint moments from inverse dynamics in flexion/extension (RMSE = 0.95, 1.14, and 2.32 N·m) but not in lateral bending (RMSE = 1.07, 2.07, and 0.84 N·m). EMG-assisted solutions generated physiological muscle activation patterns and maintained experimental cocontractions significantly (p < 0.01) outperforming SO, which was characterized by saturation and nonphysiological "on-off" patterns. This study showed for the first time that physiological neck muscle activations and cervical spine net joint moments can be estimated without assumed a priori objective criteria before impacts. Future studies could use this technique to provide detailed initial loading conditions for theoretical simulations of neck injury during impacts.

Hamstrings Contraction Regulates the Magnitude and Timing of the Peak ACL Loading During the Drop Vertical Jump in Female Athletes

Background

Anterior cruciate ligament (ACL) injury reduction training has focused on lower body strengthening and landing stabilization. In vitro studies have shown that quadriceps forces increase ACL strain, and hamstring forces decrease ACL strain. However, the magnitude of the effect of the quadriceps and hamstrings forces on ACL loading and its timing during in vivo landings remains unclear.

Purpose

To investigate the effect and timing of knee muscle forces on ACL loading during landing.

Study Design

Descriptive laboratory study.

Methods

A total of 13 young female athletes performed drop vertical jump trials, and their movements were recorded with 3-dimensional motion capture. Lower limb joint motion and muscle forces were estimated with OpenSim and applied to a musculoskeletal finite element (FE) model to estimate ACL loading during landings. The FE simulations were performed with 5 different conditions that included/excluded kinematics, ground-reaction force (GRF), and muscle forces.

Results

Simulation of landing kinematics without GRF or muscle forces yielded an estimated median ACL strain and force of 5.1% and 282.6 N. Addition of GRF to kinematic simulations increased ACL strain and force to 6.8% and 418.4 N (P < .05). Addition of quadriceps force to kinematics + GRF simulations nonsignificantly increased ACL strain and force to 7.2% and 478.5 N. Addition of hamstrings force to kinematics + GRF simulations decreased ACL strain and force to 2.6% and 171.4 N (P < .001). Addition of all muscles to kinematics + GRF simulations decreased ACL strain and force to 3.3% and 195.1 N (P < .001). With hamstrings force, ACL loading decreased from initial contact (time of peak: 1-18 milliseconds) while ACL loading without hamstrings force peaked at 47 to 98 milliseconds after initial contact (P = .024-.001). The knee flexion angle increased from 20.9° to 73.1° within 100 milliseconds after initial contact.

Conclusion

Hamstrings activation had greater effect relative to GRF and quadriceps activation on ACL loading, which significantly decreased and regulated the magnitude and timing of ACL loading during in vivo landings.

Clinical Relevance

Clinical training should focus on strategies that influence increased hamstrings activation during landing to reduce ACL loads.

The deep hip muscles are unlikely to contribute to hip stability in the sagittal plane during walking: a stiffness approach

- Objective: This study determined whether the deep hip muscles could contribute to hip stability.

METHODS

Hip stability was defined as rotational hip stiffness in the sagittal plane, which was calculated for walking trials for 12 participants via electromyography (EMG)informed neuromusculoskeletal modelling that included all 22 hip spanning muscles. Three model configurations were compared that differed in the excitations of the deep hip muscles, but were identical in the excitations of all other muscles: (1) deep hip muscles informed by intramuscular EMG measurements (assisted activation); (2) deep hip muscles with simulated zero activation (no activation); (3) deep hip muscles with simulated maximal activation (maximal activation). Sagittal plane rotational hip stiffness over the gait cycle was compared between model configurations using a within-participant analysis of variance via statistical parametric mapping (p<0.05).

RESULTS

Compared to the assisted activation configuration, hip stiffness (mean (95% confidence interval)) was 0.8% (0.7 to 0.9) lower in the no activation configuration, and 3.2% (2.9 to 3.5) higher in the maximal activation configuration over the gait cycle.

CONCLUSION

Regardless of activation level, the deep hip muscles made little contribution to sagittal plane rotational hip stiffness, which casts uncertainty around their assumed function as hip stabilizers.

SIGNIFICANCE

The merit of targeted deep hip muscle strengthening to improve hip stability in rehabilitation programs for remains unclear.

Quantifying Muscle Forces and Joint Loading During Hip Exercises Performed With and Without an Elastic Resistance Band

An increase in hip joint contact forces (HJCFs) is one of the main contributing mechanical causes of hip joint pathologies, such as hip osteoarthritis, and its progression. The strengthening of the surrounding muscles of the joint is a way to increase joint stability, which results in the reduction of HJCF. Most of the exercise recommendations are based on expert opinions instead of evidence-based facts. This study aimed to quantify muscle forces and joint loading during rehabilitative exercises using an elastic resistance band (ERB). Hip exercise movements of 16 healthy volunteers were recorded using a three-dimensional motion capture system and two force plates. All exercises were performed without and with an ERB and two execution velocities. Hip joint kinematics, kinetics, muscle forces, and HJCF were calculated based on the musculoskeletal simulations in OpenSim. Time-normalized waveforms of the different exercise modalities were compared with each other and with reference values found during walking. The results showed that training with an ERB increases both target muscle forces and HJCF. Furthermore, the ERB reduced the hip joint range of motion during the exercises. The type of ERB used (soft vs. stiff ERB) and the execution velocity of the exercise had a minor impact on the peak muscle forces and HJCF. The velocity of exercise execution, however, had an influence on the total required muscle force. Performing hip exercises without an ERB resulted in similar or lower peak HJCF and lower muscle forces than those found during walking. Adding an ERB during hip exercises increased the peak muscle and HJCF but the values remained below those found during walking. Our workflow and findings can be used in conjunction with future studies to support exercise design.

How Well Do Commonly Used Co-contraction Indices Approximate Lower Limb Joint Stiffness Trends During Gait for Individuals Post-stroke?

Muscle co-contraction generates joint stiffness to improve stability and accuracy during limb movement but at the expense of higher energetic cost. However, quantification of joint stiffness is difficult using either experimental or computational means. In contrast, quantification of muscle co-contraction using an EMG-based Co-Contraction Index (CCI) is easier and may offer an alternative for estimating joint stiffness. This study investigated the feasibility of using two common CCIs to approximate lower limb joint stiffness trends during gait. Calibrated EMG-driven lower extremity musculoskeletal models constructed for two individuals post-stroke were used to generate the quantities required for CCI calculations and model-based estimation of joint stiffness. CCIs were calculated for various combinations of antagonist muscle pairs based on two common CCI formulations: Rudolph et al. (2000) (CCI₁) and Falconer and Winter (1985) (CCI₂). CCI₁ measures antagonist muscle activation relative to not only total activation of agonist plus antagonist muscles but also agonist muscle activation, while CCI₂ measures antagonist muscle activation relative to only total muscle activation. We computed the correlation between these two CCIs and model-based estimates of sagittal plane joint stiffness for the hip, knee, and ankle of both legs. Although we observed moderate to strong correlations between some CCI formulations and corresponding joint stiffness, these associations were highly dependent on the methodological choices made for CCI computation. Specifically, we found that: (1) CCI₁ was generally more correlated with joint stiffness than was CCI₂, (2) CCI calculation using EMG signals with calibrated electromechanical delay generally yielded the best correlations with joint stiffness, and (3) choice of antagonist muscle pairs significantly influenced CCI correlation with joint stiffness. By providing guidance on how methodological choices influence CCI correlation with joint stiffness trends, this study may facilitate a simpler alternate approach for studying joint stiffness during human movement.

Trunk, pelvis and lower limb walking biomechanics are similarly altered in those with femoroacetabular impingement syndrome regardless of cam morphology size

BACKGROUND

Studies of walking in those with femoroacetabular impingement syndrome have found altered pelvis and hip biomechanics. But a whole body, time-contiuous, assessment of biomechanical parameters has not been reported. Additionally, larger cam morphology has been associated with more pain, faster progression to end-stage osteoarthritis and increased cartilage damage but differences in walking biomechanics between large compared to small cam morphologies have not been assessed.

RESEARCH QUESTION

Are trunk, pelvis and lower limb biomechanics different between healthy pain-free controls and individuals with FAI syndrome and are those biomechanics different between those with larger, compared to smaller, cam morphologies?

METHODS

Twenty four pain-free controls were compared against 41 participants with FAI syndrome who were stratified into two groups according to their maximum alpha angle. Participants underwent three-dimensional motion capture during walking. Trunk, pelvis, and lower limb biomechanics were compared between groups using statistical parametric mapping corrected for walking speed and pain.

RESULTS

Compared to pain-free controls, participants with FAI syndrome walked with more trunk anterior tilt (mean difference 7.6°, p < 0.001) as well as less pelvic rise (3°, p < 0.001), hip abduction (-4.6°, p < 0.05) and external rotation (-6.5°, p < 0.05). They also had lower hip flexion (-0.06Nm⋅kg^-1, p < 0.05), abduction (-0.07Nm⋅kg^-1, p < 0.05) and ankle plantarflexion moments (-0.19Nm⋅kg^-1, p < 0.001). These biomechanical differences occurred throughout the gait cycle. There were no differences in walking biomechanics according to cam morphology size.

SIGNIFICANCE

Results do not support the hypothesis that larger cam morphology is associated with larger differences in walking biomechanics but did demonstrate general differences in trunk, pelvis and lower limb biomechanics between those with FAI sydrome and pain-free controls. Altered external biomechanics are likely the result of complex sensory-motor strategy resulting from pain inhibition or impingement avoidance. Future studies should examine internal loading in those with FAI sydnrome.

In Silico-Enhanced Treatment and Rehabilitation Planning for Patients with Musculoskeletal Disorders: Can Musculoskeletal Modelling and Dynamic Simulations Really Impact Current Clinical Practice?

Over the past decades, the use of computational physics-based models representative of the musculoskeletal (MSK) system has become increasingly popular in many fields of clinically driven research, locomotor rehabilitation in particular. These models have been applied to various functional impairments given their ability to estimate parameters which cannot be readily measured in vivo but are of interest to clinicians. The use of MSK modelling and simulations allows analysis of relevant MSK biomarkers such as muscle and joint contact loading at a number of different stages in the clinical treatment pathway in order to benefit patient functional outcome. Applications of these methods include optimisation of rehabilitation programs, patient stratification, disease characterisation, surgical pre-planning, and assistive device and exoskeleton design and optimisation. This review provides an overview of current approaches, the components of standard MSK models, applications, limitations, and assumptions of these modelling and simulation methods, and finally proposes a future direction.

Knee Load Distribution in Hip Osteoarthritis Patients After Total Hip Replacement

Reduced external knee adduction moments in the second half of stance after total hip replacement have been reported in hip osteoarthritis patients. This reduction is thought to shift the load from the medial to the lateral knee compartment and as such increase the risk for knee osteoarthritis. The knee adduction moment is a surrogate for the load distribution between the medial and lateral compartments of the knee and not a valid measure for the tibiofemoral contact forces which are the result of externally applied forces and muscle forces. The purpose of this study was to investigate whether the distribution of the tibiofemoral contact forces over the knee compartments in unilateral hip osteoarthritis patients 1 year after receiving a primary total hip replacement differs from healthy controls. Musculoskeletal modeling on gait was performed in OpenSim using the detailed knee model of Lerner et al. (2015) for 19 patients as well as for 15 healthy controls of similar age. Knee adduction moments were calculated by the inverse dynamics analysis, medial and lateral tibiofemoral contact forces with the joint reaction force analysis. Moments and contact forces of patients and controls were compared using Statistical Parametric Mapping two-sample t-tests. Knee adduction moments and medial tibiofemoral contact forces of both the ipsi- and contralateral leg were not significantly different compared to healthy controls. The contralateral leg showed 14% higher medial tibiofemoral contact forces compared to the ipsilateral (operated) leg during the second half of stance. During the first half of stance, the lateral tibiofemoral contact force of the contralateral leg was 39% lower and the ratio 32% lower compared to healthy controls. In contrast, during the second half of stance the forces were significantly higher (39 and 26%, respectively) compared to healthy controls. The higher ratio indicates a changed distribution whereas the increased lateral tibiofemoral contact forces indicate a higher lateral knee joint loading in the contralateral leg in OA patients after total hip replacement (THR). Musculoskeletal modeling using a detailed knee model can be useful to detect differences in the load distribution between the medial and lateral knee compartment which cannot be verified with the knee adduction moment.

Individuals with mild-to-moderate hip osteoarthritis walk with lower hip joint contact forces despite higher levels of muscle co-contraction compared to healthy individuals

OBJECTIVE

To compare hip joint contact forces (HJCF), hip muscle forces, and hip muscle co-contraction levels between individuals with mild-to-moderate hip osteoarthritis (OA) and healthy controls during walking.

DESIGN

Eighteen participants with mild-to-moderate hip OA and 23 healthy controls walked at a self-selected speed while motion capture and electromyographic data were synchronously collected. HJCF were computed using a calibrated electromyography-informed neuromusculoskeletal model. Hip joint contact forces, muscle forces, and co-contraction indices for flexor/extensor and adductor/abductor muscle groups were compared between groups using independent sample t-tests (P < 0.05).

RESULTS

There was no between-group difference in self-selected walking speed. On average, participants with hip OA walked with 11% lower first peak (mean difference 235 [95% confidence interval (CI) 57-413] N) and 22% lower second peak (mean difference 574 [95%CI 304-844] N) HJCF compared to controls. Hip muscle forces were also significantly lower in the hip OA compared to control group at first (mean difference 224 [95%CI 66-382] N) and second (mean difference 782 [95%CI 399-1164] N) peak HJCF. Participants with hip OA exhibited higher levels of hip muscle co-contraction in both flexor/extensor and adductor/abductor muscle groups. Consistent with existing literature, hip joint angles (extension, adduction) and external moments (flexion, extension, adduction) were lower in hip OA compared to controls.

CONCLUSION

Lower HJCF were detected in mild-to-moderate hip OA, primarily due to lower hip muscle force production, and despite higher levels of hip muscle co-contraction. Findings suggest that lower loading of the hip joint during walking is a feature of mild-to-moderate hip OA, which could have implications for the pathogenesis of hip OA and/or disease progression.

Immediate Effects of Medially Posted Insoles on Lower Limb Joint Contact Forces in Adult Acquired Flatfoot: A Pilot Study

Flatfoot is linked to secondary lower limb joint problems, such as patellofemoral pain. This study aimed to investigate the influence of medial posting insoles on the joint mechanics of the lower extremity in adults with flatfoot. Gait analysis was performed on fifteen young adults with flatfoot under two conditions: walking with shoes and foot orthoses (WSFO), and walking with shoes (WS) in random order. The data collected by a vicon system were used to drive the musculoskeletal model to estimate the hip, patellofemoral, ankle, medial and lateral tibiofemoral joint contact forces. The joint contact forces in WSFO and WS conditions were compared. Compared to the WS group, the second peak patellofemoral contact force (p < 0.05) and the peak ankle contact force (p < 0.05) were significantly lower in the WSFO group by 10.2% and 6.8%, respectively. The foot orthosis significantly reduced the peak ankle eversion angle (p < 0.05) and ankle eversion moment (p < 0.05); however, the peak knee adduction moment increased (p < 0.05). The reduction in the patellofemoral joint force and ankle contact force could potentially inhibit flatfoot-induced lower limb joint problems, despite a greater knee adduction moment.

Osteoarthritis year in review 2019: mechanics

Mechanics play a critical - but not sole - role in the pathogenesis of osteoarthritis, and recent research has highlighted how mechanical constructs are relevant at the cellular, joint, and whole-body level related to osteoarthritis outcomes. This review examined papers from April 2018 to April 2019 that reported on the role of mechanics in osteoarthritis etiology, with a particular emphasis on studies that focused on the interaction between movement and tissue biomechanics with other clinical outcomes relevant to the pathophysiology of osteoarthritis. Studies were grouped by themes that were particularly prevalent from the past year. Results of the search highlighted the large exposure of knee-related research relative to other body areas, as well as studies utilizing laboratory-based motion capture technology. New research from this past year highlighted the important role that rate of exerted loads and rate of muscle force development - rather than simply force capacity (strength) - have in OA etiology and treatment. Further, the role of muscle activation patterns in functional and structural aspects of joint health has received much interest, though findings remain equivocal. Finally, new research has identified potential mechanical outcome measures that may be related to osteoarthritis disease progression. Future research should continue to combine knowledge of mechanics with other relevant research techniques, and to identify mechanical markers of joint health and structural and functional disease progression that are needed to best inform disease prevention, monitoring, and treatment.

EMG-Assisted Muscle Force Driven Finite Element Model of the Knee Joint with Fibril-Reinforced Poroelastic Cartilages and Menisci

Abnormal mechanical loading is essential in the onset and progression of knee osteoarthritis. Combined musculoskeletal (MS) and finite element (FE) modeling is a typical method to estimate load distribution and tissue responses in the knee joint. However, earlier combined models mostly utilize static-optimization based MS models and muscle force driven FE models typically use elastic materials for soft tissues or analyze specific time points of gait. Therefore, here we develop an electromyography-assisted muscle force driven FE model with fibril-reinforced poro(visco)elastic cartilages and menisci to analyze knee joint loading during the stance phase of gait. Moreover, since ligament pre-strains are one of the important uncertainties in joint modeling, we conducted a sensitivity analysis on the pre-strains of anterior and posterior cruciate ligaments (ACL and PCL) as well as medial and lateral collateral ligaments (MCL and LCL). The model produced kinematics and kinetics consistent with previous experimental data. Joint contact forces and contact areas were highly sensitive to ACL and PCL pre-strains, while those changed less cartilage stresses, fibril strains, and fluid pressures. The presented workflow could be used in a wide range of applications related to the aetiology of cartilage degeneration, optimization of rehabilitation exercises, and simulation of knee surgeries.

EMG-Assisted Muscle Force Driven Finite Element Model of the Knee Joint with Fibril-Reinforced Poroelastic Cartilages and Menisci

Abnormal mechanical loading is essential in the onset and progression of knee osteoarthritis. Combined musculoskeletal (MS) and finite element (FE) modeling is a typical method to estimate load distribution and tissue responses in the knee joint. However, earlier combined models mostly utilize static-optimization based MS models and muscle force driven FE models typically use elastic materials for soft tissues or analyze specific time points of gait. Therefore, here we develop an electromyography-assisted muscle force driven FE model with fibril-reinforced poro(visco)elastic cartilages and menisci to analyze knee joint loading during the stance phase of gait. Moreover, since ligament pre-strains are one of the important uncertainties in joint modeling, we conducted a sensitivity analysis on the pre-strains of anterior and posterior cruciate ligaments (ACL and PCL) as well as medial and lateral collateral ligaments (MCL and LCL). The model produced kinematics and kinetics consistent with previous experimental data. Joint contact forces and contact areas were highly sensitive to ACL and PCL pre-strains, while those changed less cartilage stresses, fibril strains, and fluid pressures. The presented workflow could be used in a wide range of applications related to the aetiology of cartilage degeneration, optimization of rehabilitation exercises, and simulation of knee surgeries.

The Capacity of Generic Musculoskeletal Simulations to Predict Knee Joint Loading Using the CAMS-Knee Datasets

Musculoskeletal models enable non-invasive estimation of knee contact forces (KCFs) during functional movements. However, the redundant nature of the musculoskeletal system and uncertainty in model parameters necessitates that model predictions are critically evaluated. This study compared KCF and muscle activation patterns predicted using a scaled generic model and OpenSim static optimization tool against in vivo measurements from six patients in the CAMS-knee datasets during level walking and squatting. Generally, the total KCFs were under-predicted (RMS: 47.55%BW, R²: 0.92) throughout the gait cycle, but substiantially over-predicted (RMS: 105.7%BW, R²: 0.81) during squatting. To understand the underlying etiology of the errors, muscle activations were compared to electromyography (EMG) signals, and showed good agreement during level walking. For squatting, however, the muscle activations showed large descrepancies especially for the biceps femoris long head. Errors in the predicted KCF and muscle activation patterns were greatest during deep squat. Hence suggesting that the errors mainly originate from muscle represented at the hip and an associated muscle co-contraction at the knee. Furthermore, there were substaintial differences in the ranking of subjects and activities based on peak KCFs in the simulations versus measurements. Thus, future simulation study designs must account for subject-specific uncertainties in musculoskeletal predictions.

Effect of walking with a modified gait on activation patterns of the knee spanning muscles in people with medial knee osteoarthritis

OBJECTIVE

To evaluate muscle activation patterns and co-contraction around the knee in response to walking with modified gait patterns in patients with medial compartment knee-osteoarthritis (KOA).

DESIGN

40 medial KOA patients walked on an instrumented treadmill. Surface EMG activity from seven knee-spanning muscles (gastrocnemius, hamstrings, quadriceps), kinematics, and ground reaction forces were recorded. Patients received real-time visual feedback on target kinematics to modify their gait pattern towards three different gait modifications: Toe-in, Wider steps, Medial Thrust. The individualized feedback aimed to reduce their first peak knee adduction moment (KAM) by ≥10%. Changes in muscle activations and medial/lateral co-contraction index during the loading response phase (10-35% of the gait cycle) were evaluated, for the steps in which ≥10% KAM reduction was achieved.

RESULTS

Data from 30 patients were included in the analyses; i.e. all who could successfully reduce their KAM in a sufficient number of steps by ≥10%. When walking with ≥10% KAM reduction, Medial Thrust gait (KAM -31%) showed increased flexor activation (24%), co-contraction (17%) and knee flexion moment (35%). Isolated wider-step gait also reduced the KAM (-26%), but to a smaller extent, but without increasing muscle activation amplitudes and co-contraction. Toe-in gait showed the greatest reduction in the KAM (-35%), but was accompanied by an increased flexor activation of 42% and hence an increased co-contraction index.

CONCLUSION

Gait modifications that are most effective in reducing the KAM also yield an increase in co-contraction, thereby compromising at least part of the effects on net knee load.

Neuromusculoskeletal Modeling-Based Prostheses for Recovery After Spinal Cord Injury

Concurrent stimulation and reinforcement of motor and sensory pathways has been proposed as an effective approach to restoring function after developmental or acquired neurotrauma. This can be achieved by applying multimodal rehabilitation regimens, such as thought-controlled exoskeletons or epidural electrical stimulation to recover motor pattern generation in individuals with spinal cord injury (SCI). However, the human neuromusculoskeletal (NMS) system has often been oversimplified in designing rehabilitative and assistive devices. As a result, the neuromechanics of the muscles is seldom considered when modeling the relationship between electrical stimulation, mechanical assistance from exoskeletons, and final joint movement. A powerful way to enhance current neurorehabilitation is to develop the next generation prostheses incorporating personalized NMS models of patients. This strategy will enable an individual voluntary interfacing with multiple electromechanical rehabilitation devices targeting key afferent and efferent systems for functional improvement. This narrative review discusses how real-time NMS models can be integrated with finite element (FE) of musculoskeletal tissues and interface multiple assistive and robotic devices with individuals with SCI to promote neural restoration. In particular, the utility of NMS models for optimizing muscle stimulation patterns, tracking functional improvement, monitoring safety, and providing augmented feedback during exercise-based rehabilitation are discussed.