Biomechanical modeling for the estimation of muscle forces: toward a common language in biomechanics, medical engineering, and neurosciences

Different research fields, such as biomechanics, medical engineering or neurosciences take part in the development of biomechanical models allowing for the estimation of individual muscle forces involved in motor action. The heterogeneity of the terminology used to describe these models according to the research field is a source of confusion and can hamper collaboration between the different fields. This paper proposes a common language based on lexical disambiguation and a synthesis of the terms used in the literature in order to facilitate the understanding of the different elements of biomechanical modeling for force estimation, without questioning the relevance of the terms used in each field or the different model components or their interest. We suggest that the description should start with an indication of whether the muscle force estimation problem is solved following the physiological movement control (from the nervous drive to the muscle force production) or in the opposite direction. Next, the suitability of the model for force production estimation at a given time or for monitoring over time should be specified. Authors should pay particular attention to the method description used to find solutions, specifying whether this is done during or after data collection, with possible method adaptations during processing. Finally, the presence of additional data must be specified by indicating whether they are used to drive, assist, or calibrate the model. Describing and classifying models in this way will facilitate the use and application in all fields where the estimation of muscle forces is of real, direct, and concrete interest.

The effects of anatomical errors on shoulder kinematics computed using multi-body models

Joint motion calculated using multi-body models and inverse kinematics presents many advantages over direct marker-based calculations. However, the sensitivity of the computed kinematics is known to be partly caused by the model and could also be influenced by the participants' anthropometry and sex. This study aimed to compare kinematics computed from an anatomical shoulder model based on medical images against a scaled-generic model and quantify the effects of anatomical errors and participants' anthropometry on the calculated joint angles. Twelve participants have had planar shoulder movements experimentally captured in a motion lab, and their shoulder anatomy imaged using an MRI scanner. A shoulder multi-body dynamics model was developed for each participant, using both an image-based approach and a scaled-generic approach. Inverse kinematics have been performed using the two different modelling procedures and the three different experimental motions. Results have been compared using Bland-Altman analysis of agreement and further analysed using multi-linear regressions. Kinematics computed via an anatomical and a scaled-generic shoulder models differed in average from 3.2 to 5.4 degrees depending on the task. The MRI-based model presented smaller limits of agreement to direct kinematics than the scaled-generic model. Finally, the regression model predictors, including anatomical errors, sex, and BMI of the participant, explained from 41 to 80% of the kinematic variability between model types with respect to the task. This study highlighted the consequences of modelling precision, quantified the effects of anatomical errors on the shoulder kinematics, and showed that participants' anthropometry and sex could indirectly affect kinematic outcomes.

Statistical Quantification of the Effects of Marker Misplacement and Soft-Tissue Artifact on Shoulder Kinematics and Kinetics

The assessment of shoulder kinematics and kinetics are commonly undertaken biomechanically and clinically by using rigid-body models and experimental skin-marker trajectories. However, the accuracy of these trajectories is plagued by inherent skin-based marker errors due to marker misplacements (offset) and soft-tissue artifacts (STA). This paper aimed to assess the individual contribution of each of these errors to kinematic and kinetic shoulder outcomes computed using a shoulder rigid-body model. Baseline experimental data of three shoulder planar motions in a young healthy adult were collected. The baseline marker trajectories were then perturbed by simulating typically observed population-based offset and/or STA using a probabilistic Monte-Carlo approach. The perturbed trajectories were then used together with a shoulder rigid-body model to compute shoulder angles and moments and study their accuracy and variability against baseline. Each type of error was studied individually, as well as in combination. On average, shoulder kinematics varied by 3%, 6% and 7% due to offset, STA or combined errors, respectively. Shoulder kinetics varied by 11%, 27% and 28% due to offset, STA or combined errors, respectively. In conclusion, to reduce shoulder kinematic and kinetic errors, one should prioritise reducing STA as they have the largest error contribution compared to marker misplacements.

Review of musculoskeletal modelling in a clinical setting: Current use in rehabilitation design, surgical decision making and healthcare interventions

BACKGROUND

Musculoskeletal modelling is a common means by which to non-invasively analyse movement. Such models have largely been used to observe function in both healthy and patient populations. However, utility in a clinical environment is largely unknown. The aim of this review was to explore existing uses of musculoskeletal models as a clinical intervention, or decision-making, tool.

METHODS

A literature search was performed using PubMed and Scopus to find articles published since 2010 and relating to musculoskeletal modelling and joint and muscle forces.

FINDINGS

4662 abstracts were found, of which 39 relevant articles were reviewed. Journal articles were categorised into 5 distinct groups: non-surgical treatment, orthoses assessment, surgical decision making, surgical intervention assessment and rehabilitation regime assessment. All reviewed articles were authored by collaborations between clinicians and engineers/modellers. Current uses included insight into the development of osteoarthritis, identifying candidates for hamstring lengthening surgery, and the assessment of exercise programmes to reduce joint damage.

INTERPRETATION

There is little evidence showing the use of musculoskeletal modelling as a tool for patient care, despite the ability to assess long-term joint loading and muscle overuse during functional activities, as well as clinical decision making to avoid unfavourable treatment outcomes. Continued collaboration between model developers should aim to create clinically-friendly models which can be used with minimal input and experience by healthcare professionals to determine surgical necessity and suitability for rehabilitation regimes, and in the assessment of orthotic devices.

Strain Assessment of Deep Fascia of the Thigh During Leg Movement: An in situ Study

Fascia is a fibrous connective tissue present all over the body. At the lower limb level, the deep fascia that is overlying muscles of the outer thigh and sheathing them (fascia lata) is involved in various pathologies. However, the understanding and quantification of the mechanisms involved in these sheathing effects are still unclear. The aim of this study is to observe and quantify the strain field of the fascia lata, including the iliotibial tract (ITT), during a passive movement of the knee. Three fresh postmortem human subjects were studied. To measure hip and knee angles during knee flexion-extension, passive movements from 0° to around 120° were recorded with a motion analysis system and strain fields of the fascia were acquired using digital image correlation. Strains were computed for three areas of the fascia lata: anterior fascia, lateral fascia, and ITT. Mean principal strains showed different strain mechanisms depending on location on the fascia and knee angle. For the ITT, two strain mechanisms were observed depending on knee movement: compression is observed when the knee is extended relative to the reference position of 47°, however, tension and pure shear can be observed when the knee is flexed. For the anterior and lateral fascia, in most cases, minor strain is higher than major strain in absolute value, suggesting high tissue compression probably due to microstructural fiber rearrangements. This in situ study is the first attempt to quantify the superficial strain field of fascia lata during passive leg movement. The study presents some limitations but provides a step in understanding strain mechanism of the fascia lata during passive knee movement.

An Optimization Method Tracking EMG, Ground Reactions Forces, and Marker Trajectories for Musculo-Tendon Forces Estimation in Equinus Gait

In the context of neuro-orthopedic pathologies affecting walking and thus patients' quality of life, understanding the mechanisms of gait deviations and identifying the causal motor impairments is of primary importance. Beside other approaches, neuromusculoskeletal simulations may be used to provide insight into this matter. To the best of our knowledge, no computational framework exists in the literature that allows for predictive simulations featuring muscle co-contractions, and the introduction of various types of perturbations during both healthy and pathological gait types. The aim of this preliminary study was to adapt a recently proposed EMG-marker tracking optimization process to a lower limb musculoskeletal model during equinus gait, a multiphase problem with contact forces. The resulting optimization method tracking EMG, ground reactions forces, and marker trajectories allowed an accurate reproduction of joint kinematics (average error of 5.4 ± 3.3 mm for pelvis translations, and 1.9 ± 1.3° for pelvis rotation and joint angles) and ensured good temporal agreement in muscle activity (the concordance between estimated and measured excitations was 76.8 ± 5.3 %) in a relatively fast process (3.88 ± 1.04 h). We have also highlighted that the tracking of ground reaction forces was possible and accurate (average error of 17.3 ± 5.5 N), even without the use of a complex foot-ground contact model.

Influence of the Level of Muscular Redundancy on the Validity of a Musculoskeletal Model

While recent literature has clearly demonstrated that an extensive personalization of the musculoskeletal models was necessary to reach high accuracy, several components of the generic models may be further investigated before defining subject-specific parameters. Among others, the choice in muscular geometry and thus the level of muscular redundancy in the model may have a noticeable influence on the predicted musculotendon and joint contact forces. In this context, the aim of this study was to investigate if the level of muscular redundancy can contribute or not to reduce inaccuracies in tibiofemoral contact forces predictions. For that, the dataset disseminated through the Sixth Grand Challenge Competition to Predict In Vivo Knee Loads was applied to a versatile 3D lower limb musculoskeletal model in which two muscular geometries (i.e., two different levels of muscular redundancy) were implemented. This dataset provides tibiofemoral implant measurements for both medial and lateral compartments and thus allows evaluation of the validity of the model predictions. The results suggest that an increase of the level of muscular redundancy corresponds to a better accuracy of total tibiofemoral contact force whatever the gait pattern investigated. However, the medial and lateral contact forces ratio and accuracy were not necessarily improved when increasing the level of muscular redundancy and may thus be attributed to other parameters such as the location of contact points. To conclude, the muscular geometry, among other components of the generic model, has a noticeable impact on joint contact forces predictions and may thus be correctly chosen even before trying to personalize the model.