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Modenese, L.

Publications and source records attributed to Modenese, L..

12 recordsLinked to original sources

Joint loading in the presence of torsional deformities is overestimated unless gait adaptations are considered: a predictive simulation approach

Lower-limb torsional deformities have been shown to alter joint loading, although the findings vary between studies perhaps due to the simulation approaches applied. This study used predictive simulations to investigate how femoral neck anteversion (FNA) and external tibial torsion (ETT) influence hip and knee joint loading. Musculoskeletal models with altered FNA (1{degrees}-48{degrees}) and ETT (12{degrees}-53{degrees}), in isolation and combination, were created from a scaled adult model, and predictive walking (speed: 1.33 m/s) simulations were generated. Predictive simulations reproduced adaptations in hip rotation and foot-progression angle reported in individuals with torsional deformities. Hip and knee compressive, shear, and resultant contact forces were estimated, and multiple linear regressions quantified the independent associations of FNA and ETT with each outcome. Regressions accounted for 23%-86% of the variance in hip loading and 5%-87% in knee loading. FNA generally made the largest relative contribution to the variance explained in joint loading across regression models, although its associations varied in direction. Each 10{degrees} increase in FNA reduced the first hip compressive and resultant peaks by 0.076 and 0.035 BW, respectively, while hip shear force showed the largest increase, averaging 0.055 BW across both peaks. Most knee loads also increased, by up to 0.131 BW for the second resultant peak. Associations with ETT were primarily observed at the second peak. Our findings suggest that considering gait adaptations due to torsional alterations is crucial for estimating lower-limb joint loading, and prescribing joint kinematics and external forces while altering lower-limb torsion might lead to overestimation.

bioengineering↗

Validation of a multiscale Hill-type actuator against comprehensive benchmarks of motor unit and muscle force measurements

Computational Hill-type muscle models are widely used to simulate muscle force production because of their efficiency and physiological interpretability. However, their formulation relies on limiting assumptions, including debated multiscale simplifications, a simplified excitation-activation dynamics and an inability to capture slow and fast fibres. Moreover, existing Hill-type models remain insufficiently validated across physiological scales, fibre types, and contraction modes. We addressed these limitations by developing a multiscale fibre-type specific Hill-type neuromuscular actuator with mechanistic excitation-activation dynamics and systematically validated it against comprehensive experimental benchmarks. The model built upon a previously proposed motoneuron-driven actuator incorporating calcium-kinetics-based activation dynamics. The excitation-activation formulation was further refined to strengthen its physiological basis, while the contraction dynamics was extended by including an activation- and length-dependent force-velocity relationship, elastic tendon, passive elastic element, and the fibre-type-specific effects of yielding and sag. Validation was performed against four benchmark datasets spanning motor-unit and whole-muscle scales, including slow and fast fibres under both isometric and dynamic conditions. Experimental force traces were obtained from six muscles of rats and cats using a broad range of stimulation frequencies, muscle lengths, and imposed length changes, combining previous literature datasets with experiments performed ad hoc for this study. Overall, the model reproduced forces across all benchmark conditions, with mean absolute errors typically below 15% of the maximum isometric force, although larger errors were observed in specific submaximal and dynamic trials. The inclusion of physiologically based excitation-activation dynamics, together with yielding and sag, improved model performance under submaximal activation conditions. This study presents the first systematic validation of a single multiscale Hill-type neuromuscular actuator against comprehensive experimental motor unit and muscle force data, providing a benchmark framework for the development and assessment of future models. Author summarySkeletal muscles generate force through a complex sequence of events that links neural signals to muscle contraction. Because direct measurements are difficult to obtain, researchers often rely on computer models to investigate neuromuscular function and estimate muscle forces. However, most modelling approaches rely on simplifying assumptions about how force is generated across different biological scales, how muscles are activated, and how slow and fast muscle fibres behave. Moreover, they have not been validated against comprehensive experimental data. As a result, it remains unclear how accurately these models can reproduce muscle force across different physiological conditions. In this study, we established the first comprehensive set of experimental benchmarks spanning both motor-unit and whole-muscle scales, including slow and fast muscles under isometric and dynamic conditions. We used these benchmarks to validate a newly developed multiscale muscle model that explicitly represents the physiological pathway from neural stimulation to force production. The model incorporates experimentally based descriptions of calcium dynamics, activation, tendon elasticity, and fibre-type-specific contractile properties. We then compared simulated and experimental force responses across a wide range of stimulation frequencies, muscle lengths, and length-change conditions.

bioengineering↗

A reduced order multibody model of the foot and ankle complex based on kinematic synergies

Background and ObjectiveThe foot-ankle complex is a highly articulated and mechanically constrained system, often simplified as a chain of few rigid segments, neglecting many bone-to-bone motions and raising questions about the accurate representation of interaction with ground. This study proposes a new reduced-order multibody formulation that captures intrinsic kinematic constraints of the foot through motion synergies. MethodsBones kinematic coupling, or motion synergies, were experimentally derived from weight-bearing CT scans using principal component analysis. These couplings were embedded in a synergy-based multibody kinematic optimization framework describing the foot-ankle with five degrees of freedom: ankle flexion; foot adduction, pronation, and arching; and toe flexion. Model accuracy was evaluated against bone-level experimental kinematics. The model was applied to gait data from healthy, flat, and diabetic feet and compared with a standard multi-segment foot model, assessing robustness by progressively reducing the number of skin markers. ResultsAverage errors were about 1{degrees} and 0.5 mm when using subject-specific synergies and below 7{degrees} and 4 mm when scaling the generic model, matching or exceeding the accuracy of existing models. Reliable reconstruction was obtained using only four foot markers. In clinical gait analysis, the model showed superior discrimination between populations and enabled assessment of transverse arch deformation, not accessible with conventional models. ConclusionThe proposed synergy-based model provides an accurate, low-complexity framework for reconstructing bone-level foot and ankle kinematics, substantially simplifying gait analysis while improving biomechanical interpretability. This framework supports future integration with dynamic models aimed at studying load transmission in the foot.

bioengineering↗

OpenGRF: Predicting Ground Reaction Forces and Moments During Daily Living Activities in OpenSim

Background and ObjectivesGround reaction forces and moments (GRF&Ms), typically measured using force plates, are key inputs for musculoskeletal simulations. OpenSim currently lacks a tool to predict GRF&Ms directly from kinematics. This study presents OpenGRF, an OpenSim-based tool designed to estimate GRF&Ms and the centre of pressure (CoP) from joint kinematic data and validates its performance against force plate recordings. MethodsThe proposed methodology integrates calibrated foot-ground contact probes with an optimization framework based on computed muscle control, while CoP is computed accounting for both kinematic and dynamic contributions. For validation, a scaled FullBodyModel (37-DoF without muscles) was created for seven healthy adults performing six trials each of level walking, stair ascent, and stair descent, for a total of 126 marker-based trials. GRF&Ms predictions were compared to reference force plate data using normalized RMSE (nRMSE), Pearson correlation coefficients ({rho}), CoP error, and Statistical Parametric Mapping (SPM) ResultsResults showed high accuracy for vertical GRF (nRMSE [≤]1.5%, {rho} [≥]0.94), particularly during level walking, and good accuracy for anterior-posterior GRF (nRMSE 4.4-6.1%, {rho} = 0.81-0.91). Medio-lateral GRF was less reliable, especially in stair tasks (nRMSE up to 11.2%, {rho} down to 0.48). Free moments were the most challenging quantity to predict across all tasks (nRMSE up to 28%). In contrast, ankle moments were predicted with high fidelity (nRMSE {approx}1.7%, {rho} {approx}0.98). Median CoP errors were 21-23 mm, with largest discrepancies during double support. ConclusionsOpenGRF enables physics-consistent estimation of GRF&Ms and CoP directly from kinematics, achieving the highest accuracy for vertical GRFs and predicting ankle moments that closely match those obtained from force plate measurements.

bioengineering↗

NeuroMechanics: Electrophysiological and Computational Methods to Accurately Estimate the Neural Drive to Muscles in Humans In Vivo

The ultimate neural signal for muscle control is the neural drive sent from the spinal cord to muscles. This neural signal comprises the ensemble of action potentials discharged by the active spinal motoneurons, which is transmitted to the innervated muscle fibres to generate forces. Accurately estimating the neural drive to muscles in humans in vivo is challenging since it requires the identification of the activity of a sample of motor units (MUs) that is representative of the active MU population. Current electrophysiological recordings usually fail in this task by identifying small MU samples with over-representation of higher-threshold with respect to lower-threshold MUs. Here, we describe recent advances in electrophysiological methods that allow the identification of more representative samples of greater numbers of MUs than previously possible. This is obtained with large and very dense arrays of electromyographic electrodes. Moreover, recently developed computational methods of data augmentation further extend experimental MU samples to infer the activity of the full MU pool. In conclusion, the combination of new electrode technologies and computational modelling allows for an accurate estimate of the neural drive to muscles and opens new perspectives in the study of the neural control of movement and in neural interfacing.

neuroscience↗

A novel motoneuron-driven computational muscle model with motor unit resolution for subject-specific simulations of human voluntary muscle contraction

The computational simulation of human voluntary muscle contraction is possible with EMG-driven Hill-type models of whole muscles. Despite impactful applications in numerous fields, the neuromechanical information and the physiological accuracy such models provide remain limited because of multiscale simplifications that limit comprehensive description of muscle internal dynamics during contraction. We addressed this limitation by developing a novel motoneuron-driven neuromuscular model, that describes the force-generating dynamics of a population of individual motor units, each of which was described with a Hill-type actuator and controlled by a dedicated experimentally derived motoneuronal control. In forward simulation of human voluntary muscle contraction, the model transforms a vector of motoneuron spike trains decoded from high-density EMG signals into a vector of motor unit forces that sum into the predicted whole muscle force. The control of motoneurons provides comprehensive and separate descriptions of the dynamics of motor unit recruitment and discharge and decode the subjects intention. The neuromuscular model is subject-specific, muscle-specific, includes an advanced and physiological description of motor unit activation dynamics, and is validated against an experimental muscle force. Accurate force predictions were obtained when the vector of experimental neural controls was representative of the discharge activity of the complete motor unit pool. This was achieved with large and dense grids of EMG electrodes during medium-force contractions or with computational methods that physiologically estimate the discharge activity of the motor units that were not identified experimentally. This neuromuscular model advances the state-of-the-art of neuromuscular modelling, bringing together the fields of motor control and musculoskeletal modelling, and finding applications in neuromuscular control and human-machine interfacing research. Author SummaryNeuromuscular computational simulations of human muscle contractions are typically obtained with a mathematical model that transforms an electromyographic signal recorded from the muscle into force. This single-input single-output approach, however, limits the comprehensive description of muscle internal dynamics during contraction because of necessary multiscale simplifications. Here, we advance the state-of-the-art in neuromuscular modelling by proposing a novel mathematical model that describes the force-generating dynamics of the individual motor units that constitute the muscle. For the first time, the control to the population of modelled motor units was inferred from decomposed high-density electromyographic signals. The model was experimentally validated, and the sensitivity of its predictions to different experimental neural controls was assessed. The neuromuscular model, coupled with an image-based musculoskeletal model, includes a novel and advanced neuromechanical model of the motor unit excitation-contraction properties, and is suited for subject-specific simulations of human voluntary contraction, with applications in neurorehabilitation and the control of neuroprosthetics.

bioengineering↗

Larger and denser: an optimal design for surface grids of EMG electrodes to identify greater and more representative samples of motor units

The spinal motor neurons are the only neural cells whose individual activity can be non-invasively identified. This is usually done using grids of surface electromyographic (EMG) electrodes and source separation algorithms; an approach called EMG decomposition. In this study, we combined computational and experimental analyses to assess how the design parameters of grids of electrodes influence the number and the properties of the identified motor units. We first computed the percentage of motor units that could be theoretically discriminated within a pool of 200 simulated motor units when decomposing EMG signals recorded with grids of various sizes and interelectrode distances (IED). Increasing the density, the number of electrodes, and the size of the grids, increased the number of motor units that our decomposition algorithm could theoretically discriminate, i.e., up to 83.5% of the simulated pool (range across conditions: 30.5-83.5%). We then identified motor units from experimental EMG signals recorded in six participants with grids of various sizes (range: 2-36 cm2) and IED (range: 4-16 mm). The configuration with the largest number of electrodes and the shortest IED maximized the number of identified motor units (56{+/-}14; range: 39-79) and the percentage of early recruited motor units within these samples (29{+/-}14%). Finally, the number of identified motor units further increased with a prototyped grid of 256 electrodes and an IED of 2 mm. Taken together, our results showed that larger and denser surface grids of electrodes allow to identify a more representative pool of motor units than currently reported in experimental studies. Significance statementThe application of source separation methods to multi-channel EMG signals recorded with grids of electrodes enables users to accurately identify the activity of individual motor units. However, the design parameters of these grids have never been discussed. They are usually arbitrarily fixed, often based on commercial availability. Here, we showed that using larger and denser grids of electrodes than conventionally proposed drastically increases the number of identified motor units. The samples of identified units are more balanced between early- and late-recruited motor units. Thus, these grids provide a more representative sampling of the active motor unit population. Gathering large datasets of motor units using large and dense grids will impact the study of motor control, neuromuscular modelling, and human-machine interfacing.

neuroscience↗

Hill-type computational models of muscle-tendon actuators: a systematic review

Backed by a century of research and development, Hill-type models of skeletal muscle, often including a muscle-tendon complex and neuromechanical interface, are widely used for countless applications. Lacking recent comprehensive reviews, the field of Hill-type modelling is, however, dense and hard-to-explore, with detrimental consequences on innovation. Here we present the first systematic review of Hill-type muscle modelling. It aims to clarify the literature by detailing its contents and critically discussing the state-of-the-art by identifying the latest advances, current gaps, and potential future directions in Hill-type modelling. For this purpose, fifty-seven criteria-abiding Hill-type models were assessed according to a completeness evaluation, which identified the modelled muscle properties, and a modelling evaluation, which considered the level of validation and reusability of the models, as well as their modelling strategy and calibration. It is concluded that most models (1) do not significantly advance beyond historical gold standards, (2) neglect the importance of parameter identification, (3) lack robust validation, and (4) are not reusable in other studies. Besides providing a convenient tool supported by extensive supplementary material for navigating the literature, the results of this review highlight the need for global recommendations in Hill-type modelling to optimize inter-study consistency, knowledge transfer, and model reusability.

bioinformatics↗

Estimation of the firing behaviour of a complete motoneuron pool by combining EMG signal decomposition and realistic motoneuron modelling

Our understanding of the firing behaviour of motoneuron (MN) pools during human voluntary muscle contractions is currently limited to electrophysiological findings from animal experiments extrapolated to humans, mathematical models of MN pools not validated for human data, and experimental results obtained from decomposition of electromyographical (EMG) signals. These approaches are limited in accuracy or provide information on only small partitions of the MN population. Here, we propose a method based on the combination of high-density EMG (HDEMG) data and realistic modelling for predicting the behaviour of entire pools of motoneurons in humans. The method builds on a physiologically realistic model of a MN pool which predicts, from the experimental spike trains of a smaller number of individual MNs identified from decomposed HDEMG signals, the unknown recruitment and firing activity of the remaining unidentified MNs in the complete MN pool. The MN pool model is described as a cohort of single-compartment leaky fire- and-integrate (LIF) models of MNs scaled by a physiologically realistic distribution of MN electrophysiological properties and driven by a spinal synaptic input, both derived from decomposed HDEMG data. The MN spike trains and effective neural drive to muscle, predicted with this method, have been successfully validated experimentally. A representative application of the method in MN-driven neuromuscular modelling is also presented. The proposed approach provides a validated tool for neuroscientists, experimentalists, and modelers to infer the firing activity of MNs that cannot be observed experimentally, investigate the neuromechanics of human MN pools, support future experimental investigations, and advance neuromuscular modelling for investigating the neural strategies controlling human voluntary contractions. Author SummaryOur experimental understanding of the firing behaviour of motoneuron (MN) pools during human voluntary muscle contractions is currently limited to the observation of small samples of active MNs obtained from EMG decomposition. EMG decomposition therefore provides an important but incomplete description of the role of individual MNs in the firing activity of the complete MN pool, which limits our understanding of the neural strategies of the whole MN pool and of how the firing activity of each MN contributes to the neural drive to muscle. Here, we combine decomposed high-density EMG (HDEMG) data and a physiologically realistic model of MN population to predict the unknown recruitment and firing activity of the remaining unidentified MNs in the complete MN pool. In brief, an experimental estimation of the synaptic current is input to a cohort of MN models, which are calibrated using the available decomposed HDEMG data, and predict the MN spike trains fired by the entire MN population. This novel approach is experimentally validated and applied to muscle force prediction from neuromuscular modelling.

neuroscience↗

Mathematical Relationships between Motoneuron Properties Derived by Empirical Data Analysis: Size Determines All Motoneuron Properties

Our understanding of the behaviour of spinal alpha-motoneurons (MNs) in mammals partly relies on our knowledge of the relationships between MN membrane properties, such as MN size, resistance, rheobase, capacitance, time constant, axonal conduction velocity and afterhyperpolarization period. We reprocessed the data from 40 experimental studies in adult cat, rat and mouse MN preparations, to empirically derive a set of quantitative mathematical relationships between these MN electrophysiological and anatomical properties. This validated mathematical framework, which supports past findings that the MN membrane properties are all related to each other and clarifies the nature of their associations, is besides consistent with the Hennemans size principle and Ralls cable theory. The derived mathematical relationships provide a convenient tool for neuroscientists and experimenters to complete experimental datasets, to explore relationships between pairs of MN properties never concurrently observed in previous experiments, or to investigate inter-mammalian-species variations in MN membrane properties. Using this mathematical framework, modelers can build profiles of inter-consistent MN-specific properties to scale pools of MN models, with consequences on the accuracy and the interpretability of the simulations.

neuroscience↗

Dependency of Lower Limb Joint Reaction Forces on Femoral Anteversion

BackgroundMusculoskeletal (MSK) models based on literature data are meant to represent a generic anatomy and are a popular tool employed by biomechanists to estimate the internal loads occurring in the lower limb joints, such as joint reaction forces (JRFs). However, since these models are normally just linearly scaled to an individuals anthropometry, it is unclear how their estimations would be affected by the personalization of key features of the MSK anatomy, one of which is the femoral anteversion angle. Research QuestionHow are the lower limb JRF magnitudes computed through a generic MSK model affected by changes in the femoral anteversion? MethodsWe developed a bone-deformation tool in MATLAB (https://simtk.org/projects/bone_deformity) and used it to create a set of seven OpenSim models spanning from 2{degrees} femoral retroversion to 40{degrees} anteversion. We used these models to simulate the gait of an elderly individual with an instrumented prosthesis implanted at their knee joint (5th Grand Challenge dataset) and quantified both the changes in JRFs magnitude due to varying the skeletal anatomy and their accuracy against the correspondent in vivo measurements at the knee joint. ResultsHip and knee JRF magnitudes were affected by the femoral anteversion with variations from the unmodified generic model up to 11.7{+/-}5.5% at the hip and 42.6{+/-}31.0% at the knee joint. The ankle joint was unaffected by the femoral geometry. The MSK models providing the most accurate knee JRFs (root mean squared error: 0.370{+/-}0.069 body weight, coefficient of determination: 0.764{+/-}0.104, largest peak error: 0.36{+/-}0.16 body weight) were those with the femoral anteversion angle closer to that measured on the segmented bone of the individual. SignificanceFemoral anteversion substantially affects hip and knee JRFs estimated with generic MSK models, suggesting that personalizing key MSK anatomical features might be necessary for accurate estimation of JRFs with these models.

bioengineering↗

Automatic Generation of Personalised Models of the Lower Limb from Three-Dimensional Bone Geometries: A Validation Study

The generation of personalised and patient-specific musculoskeletal models is currently a cumbersome and time-consuming task that normally requires several processing hours and trained operators. We believe that this aspect discourages the use of computational models even when appropriate data are available and personalised biomechanical analysis would be beneficial. In this paper we present a computational tool that enables the fully automatic generation of skeletal models of the lower limb from three-dimensional bone geometries, normally obtained by segmentation of medical images. This tool was evaluated against four manually created lower limb models finding remarkable agreement in the computed joint parameters, well within human operator repeatability. The coordinate systems origins were identified with maximum differences between 0.5 mm (hip joint) and 5.9 mm (subtalar joint), while the joint axes presented discrepancies between 1{degrees} (knee joint) to 11{degrees} (subtalar joint). To prove the robustness of the methodology, the models were built from four datasets including both genders, anatomies ranging from juvenile to elderly and bone geometries reconstructed from high-quality computed tomography as well as lower-quality magnetic resonance imaging scans. The entire workflow, implemented in MATLAB scripting language, executed in seconds and required no operator intervention, creating lower extremity models ready to use for kinematic and kinetic analysis or as baselines for more advanced musculoskeletal modelling approaches, of which we provide some practical examples. We auspicate that this technical advancement, together with upcoming progress in medical image segmentation techniques, will promote the use of personalised models in larger-scale studies than those hitherto undertaken.

bioengineering↗