Generating dynamic simulations of movement using computed muscle control

Walking Slope and Heavy Backpacks Affect Peak and Impulsive Lumbar Joint Contact Forces

Heavy load carriage is associated with musculoskeletal overuse injury, particularly in the lumbar spine. In addition, steep walking slopes and heavy backpacks separately require adaptation of torso kinematics, but the combined effect of sloped walking and heavy backpack loads on lumbar joint contact forces is unclear. Backpacks with hip belt attachments can reduce pressure under the shoulder straps; however, it is unknown if wearing a hip belt reduces lumbar spine forces. We used a musculoskeletal modeling and simulation approach to quantify peak and impulsive L1L2 and L4L5 lumbar joint contact forces in the anterior/posterior shear and compressive directions during walking on 0 deg and ±10 deg slopes, with no backpack and with 40% body weight backpack load using two different backpack configurations (hip belt assisted and shoulder-borne). Both walking slope and backpack load significantly affected shear and compressive peak and impulsive forces. The largest peak shear and compressive forces of 1.57 and 5.23 body weights, respectively, exceed recommended limits and were observed during uphill walking with shoulder-borne loads. However, only impulsive force results revealed differences due to the backpack configuration, and this effect depended on walking slope. During downhill walking only, the hip belt-assisted configuration resulted compressive impulses lower than during shoulder borne by 0.25 body weight seconds for both L1L2 and L4L5. These results indicate that walking uphill with heavy loads causes high shear and compressive lumbar forces that may increase overuse injury risk. In addition, our results suggest it is especially important to wear a hip belt when walking downhill.

Comparison of synergy extrapolation and static optimization for estimating multiple unmeasured muscle activations during walking

BACKGROUND

Calibrated electromyography (EMG)-driven musculoskeletal models can provide 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 unknown.

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) applied to a scaled generic musculoskeletal model 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, compared to SO, SynX with simultaneous model calibration 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). 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.

Antagonistic Feedback Control of Muscle Length Changes for Efficient Involuntary Posture Stabilization

Simultaneous and cooperative muscle activation results in involuntary posture stabilization in vertebrates. However, the mechanism through which more muscles than joints contribute to this stabilization remains unclear. We developed a computational human body model with 949 muscle action lines and 22 joints and examined muscle activation patterns for stabilizing right upper or lower extremity motions at a neutral body posture (NBP) under gravity using actor-critic reinforcement learning (ACRL). Two feedback control models (FCM), muscle length change (FCM-ML) and joint angle differences, were applied to ACRL with a normalized Gaussian network (ACRL-NGN) or deep deterministic policy gradient. Our findings indicate that among the six control methods, ACRL-NGN with FCM-ML, utilizing solely antagonistic feedback control of muscle length change without relying on synergy pattern control or categorizing muscles as flexors, extensors, agonists, or synergists, achieved the most efficient involuntary NBP stabilization. This finding suggests that vertebrate muscles are fundamentally controlled without categorization of muscles for targeted joint motion and are involuntarily controlled to achieve the NBP, which is the most comfortable posture under gravity. Thus, ACRL-NGN with FCM-ML is suitable for controlling humanoid muscles and enables the development of a comfortable seat design.

A scoping review and evaluation of open-source transtibial amputation musculoskeletal models for female populations

Musculoskeletal modeling is often used to study people with transtibial amputations. Females in this population are of particular interest as they are underrepresented in research, experience unique challenges, and demonstrate gait biomechanics distinct from males. Because generic models often neglect innate variations between populations, it is important to determine whether data used to develop a model are representative of the population studied. The objective of this study was to review and analyze existing transtibial amputation musculoskeletal models, establish a database from the information compiled, and use the database to select the model most relevant for studying female populations. A scoping search was performed and a database was created based on data detailing the eligible models. Models were evaluated through a weighted decision process based on criteria of their representation of females with transtibial amputations, prosthetic functionality, development transparency, overall functionality, and experimental validation methods. The scoping review identified 3 studies, Willson et al., LaPrè et al., and Miller and Esposito. A database detailing these models was established. The Willson model scored highest on all criteria except overall functionality, where the LaPrè model outscored it. Based on the established weightings, the Willson model was classed most appropriate for the stated goals. The created database can be used by other researchers to guide their own modeling studies, irrespective of the population of focus. Of the 3, the Willson model was found most relevant for studying females with transtibial amputations. This model will be used in future work investigating and addressing challenges of females with transtibial amputations.

Beyond Inverse Dynamics: Methods for Assessment of Individual Muscle Function during Gait

Three-dimensional motion analysis performed in the modern gait analysis laboratory provides a wealth of information about the kinematics and kinetics of human locomotion, but standard gait analysis is largely restricted to joint-level measures. Three-dimensional joint rotations, joint moments, and joint powers tell us a great deal about gait mechanics, but it is often of interest to know about the roles that muscles play. This narrative review surveys work that has been done, largely over the past four decades, to augment standard gait analysis with muscle-level assessments of function. Often, these assessments have incorporated additional technology such as ultrasound imaging, or complex modeling and simulation techniques. The review discusses measurements of muscle moment arm during walking along with assessment of muscle mechanical advantage, muscle-tendon lengths, and the use of induced acceleration analysis to determine muscle roles. In each section of the review, examples are provided of how the auxiliary analyses have been used to gain potentially useful information about normal and pathological human walking. While this work highlights the potential benefits of adding various measures to gait analysis, it is acknowledged that challenges to implementation remain, such as the need for specialized knowledge and the potential for bias introduced by model choices.

Dynamic injectable tissue adhesives with strong adhesion and rapid self-healing for regeneration of large muscle injury

Wounds often necessitate the use of instructive biomaterials to facilitate effective healing. Yet, consistently filling the wound and retaining the material in place presents notable challenges. Here, we develop a new class of injectable tissue adhesives by leveraging the dynamic crosslinking chemistry of Schiff base reactions. These adhesives demonstrate outstanding mechanical properties, especially in regard to stretchability and self-healing capacity, and biodegradability. Furthermore, they also form robust adhesion to biological tissues. Their therapeutic potential was evaluated in a rodent model of volumetric muscle loss (VML). Ultrasound imaging confirmed that the adhesives remained within the wound site, effectively filled the void, and degraded at a rate comparable to the healing process. Histological analysis indicated that the adhesives facilitated muscle fiber and blood vessel formation, and induced anti-inflammatory macrophages. Notably, the injured muscles of mice treated with the adhesives displayed increased weight and higher force generation than the control groups. This approach to adhesive design paves the way for the next generation of medical adhesives in tissue repair.

Muscle short-range stiffness behaves like a maxwell element, not a spring: Implications for joint stability

INTRODUCTION

Muscles play a critical role in supporting joints during activities of daily living, owing, in part, to the phenomenon of short-range stiffness. Briefly, when an active muscle is lengthened, bound cross-bridges are stretched, yielding forces greater than what is predicted from the force length relationship. For this reason, short-range stiffness has been proposed as an attractive mechanism for providing joint stability. However, there has yet to be a forward dynamic simulation employing a cross-bridge model, that demonstrates this stabilizing role. Therefore, the purpose of this investigation was to test whether Huxley-type muscle elements, which exhibit short-range stiffness, can stabilize a joint while at constant activation.

METHODS

We analyzed the stability of an inverted pendulum (moment of inertia: 2.7 kg m2) supported by Huxley-type muscle models that reproduce the short-range stiffness phenomenon. We calculated the muscle forces that would provide sufficient short-range stiffness to stabilize the system based in minimizing the potential energy. Simulations consisted of a 50 ms long, 5 Nm square-wave perturbation, with numerical simulations carried out in ArtiSynth.

RESULTS

Despite the initial analysis predicting shared activity of antagonist and agonist muscles to maintain stable equilibrium, the inverted pendulum model was not stable, and did not maintain an upright posture even with fully activated muscles.

DISCUSSION & CONCLUSION

Our simulations suggested that short-range stiffness cannot be solely responsible for joint stability, even for modest perturbations. We argue that short-range stiffness cannot achieve stability because its dynamics do not behave like a typical spring. Instead, an alternative conceptual model for short-range stiffness is that of a Maxwell element (spring and damper in series), which can be obtained as a first-order approximation to the Huxley model. We postulate that the damping that results from short-range stiffness slows down the mechanical response and allows the central nervous system time to react and stabilize the joint. We speculate that other mechanisms, like reflexes or residual force enhancement/depression, may also play a role in joint stability. Joint stability is due to a combination of factors, and further research is needed to fully understand this complex system.

Investigation of joint contact forces during walking in the subjects with toe in gait due to increasing in femoral head anteversion angle

Toe-in gait is a pathology in which the child walks and turns the foot inward instead of pointing straight ahead. The alignment of the lower limb structure changes in this disease, increasing the incidence of knee and hip osteoarthritis. This study aimed to determine the kinematic and joint loading in subjects walking with a toe-in gait pattern. This study selected two groups of subjects: normal subjects and those with toe-in gait due to an increased femoral head anteversion angle (each group consisted of 15 subjects). A Qualisys motion analysis system and a Kistler force plate were used to record the motions and forces applied to the leg while walking. OpenSim software (version 3.3) was used to analyze the range of motion, moments, muscle forces, and joint contact forces in both groups of subjects. The mean values of stride length for normal subjects (1.1 ± 0.141 m) and those with toe-in gait (0.94 ± 0.183 m) differed significantly. The mediolateral component of the ground reaction force decreased significantly in the toe-in gait group compared to normal subjects (p-value = 0.05). The peak force of most of the hip joint muscles increased significantly in those with toe-in gait compared to normal subjects (p-value < 0.05). The results of this study showed that those with toe-in gait, due to an increase in femoral head anteversion angle, only had a change in rotation of the pelvic and hip joint. There was no significant difference between walking speed and most ground reaction force components between normal subjects and those with toe-in gait. As the peaks of most of the hip joint muscles increased significantly in the toe-in gait group, this increased joint contact forces (especially the anteroposterior component of the hip joint and the mediolateral component of the knee joint), which may ultimately increase the incidence of hip and knee joint osteoarthritis.

Bridging the sim2real gap. Investigating deviations between experimental motion measurements and musculoskeletal simulation results-a systematic review

Musculoskeletal simulations can be used to estimate biomechanical variables like muscle forces and joint torques from non-invasive experimental data using inverse and forward methods. Inverse kinematics followed by inverse dynamics (ID) uses body motion and external force measurements to compute joint movements and the corresponding joint loads, respectively. ID leads to residual forces and torques (residuals) that are not physically realistic, because of measurement noise and modeling assumptions. Forward dynamic simulations (FD) are found by tracking experimental data. They do not generate residuals but will move away from experimental data to achieve this. Therefore, there is a gap between reality (the experimental measurements) and simulations in both approaches, the sim2real gap. To answer (patho-) physiological research questions, simulation results have to be accurate and reliable; the sim2real gap needs to be handled. Therefore, we reviewed methods to handle the sim2real gap in such musculoskeletal simulations. The review identifies, classifies and analyses existing methods that bridge the sim2real gap, including their strengths and limitations. Using a systematic approach, we conducted an electronic search in the databases Scopus, PubMed and Web of Science. We selected and included 85 relevant papers that were sorted into eight different solution clusters based on three aspects: how the sim2real gap is handled, the mathematical method used, and the parameters/variables of the simulations which were adjusted. Each cluster has a distinctive way of handling the sim2real gap with accompanying strengths and limitations. Ultimately, the method choice largely depends on various factors: available model, input parameters/variables, investigated movement and of course the underlying research aim. Researchers should be aware that the sim2real gap remains for both ID and FD approaches. However, we conclude that multimodal approaches tracking kinematic and dynamic measurements may be one possible solution to handle the sim2real gap as methods tracking multimodal measurements (some combination of sensor position/orientation or EMG measurements), consistently lead to better tracking performances. Initial analyses show that motion analysis performance can be enhanced by using multimodal measurements as different sensor technologies can compensate each other's weaknesses.

Psoas force recruitment in full-body musculoskeletal movement simulations is restored with a geometrically informed cost function weighting

Simulations of musculoskeletal models are useful for estimating internal muscle and joint forces. However, predicted forces rely on optimization and modeling formulations. Geometric detail is important to predict muscle forces, and greater geometric complexity is required for muscles that have broad attachments or span many joints, as in the torso. However, the extent to which optimized muscle force recruitment is sensitive to these geometry choices is unclear. We developed level, uphill and downhill sloped walking simulations using a standard (uniformly weighted, "fatigue-like") cost function with lower limb and full-body musculoskeletal models to evaluate hip muscle recruitment with different geometric representations of the psoas muscle under walking conditions with varying hip moment demands. We also tested a novel cost function formulation where muscle activations were weighted according to the modeled geometric detail in the full-body model. Total psoas force was less and iliacus, rectus femoris, and other hip flexors' force was greater when psoas was modeled with greater geometric detail compared to other hip muscles for all slopes. The proposed weighting scheme restored hip muscle force recruitment without sacrificing detailed psoas geometry. In addition, we found that lumbar, but not hip, joint contact forces were influenced by psoas force recruitment. Our results demonstrate that static optimization dependent simulations using models comprised of muscles with different amounts of geometric detail bias force recruitment toward muscles with less geometric detail. Muscle activation weighting that accounts for differences in geometric complexity across muscles corrects for this recruitment bias.

Differences in Glenohumeral Joint Contact Forces Between Recovery Hand Patterns During Wheelchair Propulsion With and Without Shoulder Muscle Weakness: A Simulation Study

The majority of manual wheelchair users (MWCU) develop shoulder pain or injuries, which is often caused by impingement. Because propulsion mechanics are influenced by the recovery hand pattern used, the pattern may affect shoulder loading and susceptibility to injury. Shoulder muscle weakness is also correlated with shoulder pain, but how shoulder loading changes with specific muscle group weakness is unknown. Musculoskeletal modeling and simulation were used to compare glenohumeral joint contact forces (GJCFs) across hand patterns and determine how GJCFs vary when primary shoulder muscle groups are weakened. Experimental data were analyzed to classify individuals into four hand pattern groups. A representative musculoskeletal model was then developed for each group and simulations generated to portray baseline strength and six muscle weakness conditions. Three-dimensional GJCF peaks and impulses were compared across hand patterns and muscle weakness conditions. The semicircular pattern consistently had lower shear (anterior-posterior and superior-inferior) GJCFs compared to other patterns. The double-loop pattern had the highest superior GJCFs, while the single-loop pattern had the highest anterior and posterior GJCFs. These results suggest that using the semicircular pattern may be less susceptible to shoulder injuries such as subacromial impingement. Weakening the internal rotators and external rotators resulted in the greatest increases in shear GJCFs and decreases in compressive GJCF, likely due to decreased force from rotator cuff muscles. These findings suggest that strengthening specific muscle groups, especially the rotator cuff, is critical for decreasing the risk of shoulder overuse injuries.

Chronic ankle instability affects the association between knee joint angle and loading: musculoskeletal simulation study

The purpose of this study was to calculate and compare (1) knee loads, (2) muscle-specific contributions to knee loads, and (3) effects of knee flexion angle on knee loads and muscle-specific load contributions during a forward jump-landing task in people with and without chronic ankle instability (CAI). Eight CAI patients and seven healthy controls performed a forward jump-landing task. We collected 3D kinematics, ground reaction force, and muscle activation and used musculoskeletal modeling. The results showed that only healthy controls exhibited an association between knee flexion angle and knee compressive impulse (r = 0.854, p = .014). The lack of association in CAI group may lead to knee instability and increase knee injury risk in people with CAI.

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.

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

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

Estimation of Muscle Forces of Lower Limbs Based on CNN-LSTM Neural Network and Wearable Sensor System

Estimation of vivo muscle forces during human motion is important for understanding human motion control mechanisms and joint mechanics. This paper combined the advantages of the convolutional neural network (CNN) and long-short-term memory (LSTM) and proposed a novel muscle force estimation method based on CNN-LSTM. A wearable sensor system was also developed to collect the angles and angular velocities of the hip, knee, and ankle joints in the sagittal plane during walking, and the collected kinematic data were used as the input for the neural network model. In this paper, the muscle forces calculated using OpenSim based on the Static Optimization (SO) method were used as the standard value to train the neural network model. Four lower limb muscles of the left leg, including gluteus maximus (GM), rectus femoris (RF), gastrocnemius (GAST), and soleus (SOL), were selected as the studying objects in this paper. The experiment results showed that compared to the standard CNN and the standard LSTM, the CNN-LSTM performed better in muscle forces estimation under slow (1.2 m/s), medium (1.5 m/s), and fast walking speeds (1.8 m/s). The average correlation coefficients between true and estimated values of four muscle forces under slow, medium, and fast walking speeds were 0.9801, 0.9829, and 0.9809, respectively. The average correlation coefficients had smaller fluctuations under different walking speeds, which indicated that the model had good robustness. The external testing experiment showed that the CNN-LSTM also had good generalization. The model performed well when the estimated object was not included in the training sample. This article proposed a convenient method for estimating muscle forces, which could provide theoretical assistance for the quantitative analysis of human motion and muscle injury. The method has established the relationship between joint kinematic signals and muscle forces during walking based on a neural network model; compared to the SO method to calculate muscle forces in OpenSim, it is more convenient and efficient in clinical analysis or engineering applications.

Applying elastic resistance bands for gait training: A simulation-based study to determine how band configuration affects gait biomechanics and muscle activation

BACKGROUND

Wearable robotic exoskeletons and leg braces are desirable for gait rehabilitation because they can apply loads directly to an affected joint. Yet, they are not widely used in clinics because they are costly and complex to set up. Conversely, tethered devices, such as elastic resistance bands, are widely available in clinics, are low-cost, and are quick to set up. However, resistance bands will affect walking differently based on how they are configured to pull on the leg (e.g., pulling forward or backward).

RESEARCH QUESTION

How can a resistance band be configured to alter muscle activation and gait biomechanics based on the segment it is attached to and the angle with which it attaches?

METHODS

We used an open-source musculoskeletal modeling platform to emulate several configurations of an elastic band pulling on the ankle, calf, and thigh at various angles during non-pathological walking. We evaluated gait biomechanics and simulated muscle activation using computed muscle control (CMC) and identified a subset of four configurations with potential applications for gait training. Eight non-pathological participants then walked on a treadmill under these configurations to verify how these configurations altered muscle activation.

RESULTS

We found that muscle activity greatly varied based on the location where the elastic band is attached and the angle with which the elastic band pulls on the leg. Notably, specific angles can be used to pull on the legs to elicit an increase or decrease in muscle activation.

SIGNIFICANCE

This study provides insight into how tethered devices can be configured to provide assistance or resistance during gait training. This information can be applied when developing low-cost gait training solutions for addressing individuals' impairments.

Selective dorsal rhizotomy and its effect on muscle force during walking: A comprehensive study

Selective dorsal rhizotomy (SDR) is commonly used to permanently reduce spasticity in children with cerebral palsy (CP). However, studies have yielded varying results regarding muscle strength and activity after SDR. Some studies indicate weakness or no changes, while a recent study using NMSK simulations demonstrates improvements in muscle forces during walking. These findings suggest that SDR may alleviate spasticity, reducing dynamic muscle constraints and enhancing muscle force without altering muscle activity during walking in children with CP. In this study, we combined NMSK simulations with physical examinations to assess children with CP who underwent SDR, comparing them to well-matched peers who did not undergo the procedure. Each group (SDR and No-SDR) included 81 children, with pre- and post-SDR assessments. Both groups were well-matched in terms of demographics, clinical characteristics, and gait parameters. The results of the physical examination indicate that SDR significantly reduces spasticity without impacting muscle strength. Furthermore, our findings show no significant differences in gait deviation index improvements and walking speed between the two groups. Additionally, there were no statistically significant changes in muscle activity during walking before and after SDR for both groups. NMSK results demonstrate a significant increase in muscle force in the semimembranosus and calf muscles during walking, compared to children with CP who received other clinical treatments. Our findings confirm that although SDR does not significantly impact muscle strength compared to other treatments, it creates a more favorable dynamic environment for suboptimal muscle force production, which is essential for walking.

Comparison of strength profile representations using musculoskeletal models and their applications in robotics

Musculoskeletal models provide an approach towards simulating the ability of the human body in a variety of human-robot applications. A promising use for musculoskeletal models is to model the physical capabilities of the human body, for example, estimating the strength at the hand. Several methods of modelling and representing human strength with musculoskeletal models have been used in ergonomic analysis, human-robot interaction and robotic assistance. However, it is currently unclear which methods best suit modelling and representing limb strength. This paper compares existing methods for calculating and representing the strength of the upper limb using musculoskeletal models. It then details the differences and relative advantages of the existing methods, enabling the discussion on the appropriateness of each method for particular applications.

Upper Extremity Muscle Activation Pattern Prediction Through Synergy Extrapolation and Electromyography-Driven Modeling

Patients with neuromuscular disease fail to produce necessary muscle force and have trouble maintaining joint moment required to perform activities of daily living. Measuring muscle force values in patients with neuromuscular disease is important but challenging. Electromyography (EMG) can be used to obtain muscle activation values, which can be converted to muscle forces and joint torques. Surface electrodes can measure activations of superficial muscles, but fine-wire electrodes are needed for deep muscles, although it is invasive and require skilled personnel and preparation time. EMG-driven modeling with surface electrodes alone could underestimate the net torque. In this research, authors propose a methodology to predict muscle activations from deeper muscles of the upper extremity. This method finds missing muscle activation one at a time by combining an EMG-driven musculoskeletal model and muscle synergies. This method tracks inverse dynamics joint moments to determine synergy vector weights and predict muscle activation of selected shoulder and elbow muscles of a healthy subject. In addition, muscle-tendon parameter values (optimal fiber length, tendon slack length, and maximum isometric force) have been personalized to the experimental subject. The methodology is tested for a wide range of rehabilitation tasks of the upper extremity across multiple healthy subjects. Results show this methodology can determine single unmeasured muscle activation up to Pearson's correlation coefficient (R) of 0.99 (root mean squared error, RMSE = 0.001) and 0.92 (RMSE = 0.13) for the elbow and shoulder muscles, respectively, for one degree-of-freedom (DoF) tasks. For more complicated five DoF tasks, activation prediction accuracy can reach up to R = 0.71 (RMSE = 0.29).

Metabolic cost for isometric force scales nonlinearly and predicts how humans distribute forces across limbs

Muscles consume metabolic energy for force production and movement. A mathematical model of metabolic energy cost will be useful in predicting instantaneous costs during human exercise and in computing effort-minimizing movements via simulations. Previous in vivo data-derived models usually assumed either zero or linearly increasing cost with force, but a nonlinear relation could have significant metabolic or behavioural implications. Here, we show that metabolic cost scales nonlinearly with joint torque with an exponent of about 1.64, using calorimetric measurements of isometric squats. We then demonstrate that this metabolic nonlinearity is reflected in human behaviour: minimizing this nonlinear cost predicts how humans share forces between limbs in additional experiments involving arms and legs. This shows the utility of the nonlinear energy cost in predictive models and its generalizability across limbs. Finally, we show mathematical evidence that the same nonlinear metabolic objective may underlie force sharing at the muscle level.

Muscle-driven simulations and experimental data of cycling

Muscle-driven simulations have provided valuable insights in studies of walking and running, but a set of freely available simulations and corresponding experimental data for cycling do not exist. The aim of this work was to develop a set of muscle-driven simulations of cycling and to validate them by comparison with experimental data. We used direct collocation to generate simulations of 16 participants cycling over a range of powers (40-216 W) and cadences (75-99 RPM) using two optimization objectives: a baseline objective that minimized muscle effort and a second objective that additionally minimized tibiofemoral joint forces. We tested the accuracy of the simulations by comparing the timing of active muscle forces in our baseline simulation to timing in experimental electromyography data. Adding a term in the objective function to minimize tibiofemoral forces preserved cycling power and kinematics, improved similarity between active muscle force timing and experimental electromyography, and decreased tibiofemoral joint reaction forces, which better matched previously reported in vivo measurements. The musculoskeletal models, muscle-driven simulations, simulation software, and experimental data are freely shared at https://simtk.org/projects/cycling_sim for others to reproduce these results and build upon this research.

Predictions of thumb, hand, and arm muscle parameters derived using force measurements of varying complexity and neural networks

Subject-specific musculoskeletal models are a promising avenue for personalized healthcare. However, current methods for producing personalized models require dense, biomechanical datasets that include expensive and time-consuming physiological measurements. For personalized models to be clinically useful, we must be able to rapidly generate models from simple, easy to collect data. In this context, the objective of this paper is to evaluate if and how simple data, namely height/weight and pinch force data, can be used to achieve model personalization via machine learning. Using simulated lateral pinch force measurements from a synthetic population of 40,000 randomly generated subjects, we train neural networks to estimate four Hill-type muscle model parameters and bone density. We compare parameter estimates to the true parameters of 10,000 additional synthetic subjects. We also generate new personalized models using the parameter estimates and perform new lateral pinch simulations to compare predicted forces using these personalized models to those generated using a baseline model. We demonstrate that increasing force measurement complexity reduces the root-mean-square error in the majority of parameter estimates. Additionally, musculoskeletal models using neural network-based parameter estimates provide up to an 80% reduction in absolute error in simulated forces when compared to a generic model. Thus, easily obtained force measurements may be suitable for personalizing models of the thumb, although extending the method to more tasks and models involving other joints likely requires additional measurements.

Time series modeling characterizes stride time variability to identify individuals with neurodegenerative disorders

The progressive death and dysfunction of neurons causes altered stride-to-stride variability in individuals with Amyotrophic Lateral Sclerosis (ALS) and Huntington's Disease (HD). Yet these altered gait dynamics can manifest differently in these populations based on how and where these neurodegenerative disorders attack the central nervous system. Time series analyses can quantify differences in stride time variability which can help contribute to the detection and identification of these disorders. Here, autoregressive modeling time series analysis was utilized to quantify differences in stride time variability amongst the Controls, the individuals with ALS, and the individuals with HD. For this study, fifteen Controls, 12 individuals with ALS and 15 individuals with HD walked up and down a hallway continuously for 5-min. Participants wore force sensitive resistors in their shoes to collect stride time data. A second order autoregressive (AR) model was fit to the time series created from the stride time data. The mean stride time and two AR model coefficients served as metrics to identify differences in stride time variability amongst the three groups. The individuals with HD walked with significantly greater stride time variability indicating a more chaotic gait while the individuals with ALS adopted more ordered, less variable stride time dynamics (p < 0.001). A plot of the stride time metrics illustrated how each group exhibited significantly different stride time dynamics. The stride time metrics successfully quantified differences in stride time variability amongst individuals with neurodegenerative disorders. This work provided valuable insight about how these neuromuscular disorders disrupt motor coordination leading to the adoption of new gait dynamics.

Does enforcing glenohumeral joint stability matter? A new rapid muscle redundancy solver highlights the importance of non-superficial shoulder muscles

The complexity of the human shoulder girdle enables the large mobility of the upper extremity, but also introduces instability of the glenohumeral (GH) joint. Shoulder movements are generated by coordinating large superficial and deeper stabilizing muscles spanning numerous degrees-of-freedom. How shoulder muscles are coordinated to stabilize the movement of the GH joint remains widely unknown. Musculoskeletal simulations are powerful tools to gain insights into the actions of individual muscles and particularly of those that are difficult to measure. In this study, we analyze how enforcement of GH joint stability in a musculoskeletal model affects the estimates of individual muscle activity during shoulder movements. To estimate both muscle activity and GH stability from recorded shoulder movements, we developed a Rapid Muscle Redundancy (RMR) solver to include constraints on joint reaction forces (JRFs) from a musculoskeletal model. The RMR solver yields muscle activations and joint forces by minimizing the weighted sum of squared-activations, while matching experimental motion. We implemented three new features: first, computed muscle forces include active and passive fiber contributions; second, muscle activation rates are enforced to be physiological, and third, JRFs are efficiently formulated as linear functions of activations. Muscle activity from the RMR solver without GH stability was not different from the computed muscle control (CMC) algorithm and electromyography of superficial muscles. The efficiency of the solver enabled us to test over 3600 trials sampled within the uncertainty of the experimental movements to test the differences in muscle activity with and without GH joint stability enforced. We found that enforcing GH stability significantly increases the estimated activity of the rotator cuff muscles but not of most superficial muscles. Therefore, a comparison of shoulder model muscle activity to EMG measurements of superficial muscles alone is insufficient to validate the activity of rotator cuff muscles estimated from musculoskeletal models.

From monkeys to humans: observation-basedEMGbrain-computer interface decoders for humans with paralysis

Objective. Intracortical brain-computer interfaces (iBCIs) aim to enable individuals with paralysis to control the movement of virtual limbs and robotic arms. Because patients' paralysis prevents training a direct neural activity to limb movement decoder, most iBCIs rely on 'observation-based' decoding in which the patient watches a moving cursor while mentally envisioning making the movement. However, this reliance on observed target motion for decoder development precludes its application to the prediction of unobservable motor output like muscle activity. Here, we ask whether recordings of muscle activity from a surrogate individual performing the same movement as the iBCI patient can be used as target for an iBCI decoder.Approach. We test two possible approaches, each using data from a human iBCI user and a monkey, both performing similar motor actions. In one approach, we trained a decoder to predict the electromyographic (EMG) activity of a monkey from neural signals recorded from a human. We then contrast this to a second approach, based on the hypothesis that the low-dimensional 'latent' neural representations of motor behavior, known to be preserved across time for a given behavior, might also be preserved across individuals. We 'transferred' an EMG decoder trained solely on monkey data to the human iBCI user after using Canonical Correlation Analysis to align the human latent signals to those of the monkey.Main results. We found that both direct and transfer decoding approaches allowed accurate EMG predictions between two monkeys and from a monkey to a human.Significance. Our findings suggest that these latent representations of behavior are consistent across animals and even primate species. These methods are an important initial step in the development of iBCI decoders that generate EMG predictions that could serve as signals for a biomimetic decoder controlling motion and impedance of a prosthetic arm, or even muscle force directly through functional electrical stimulation.

Simulations suggest walking with reduced propulsive force would not mitigate the energetic consequences of lower tendon stiffness

Aging elicits numerous effects that impact both musculoskeletal structure and walking function. Tendon stiffness (kT) and push-off propulsive force (FP) both impact the metabolic cost of walking and are diminished by age, yet their interaction has not been studied. We combined experimental and computational approaches to investigate whether age-related changes in function (adopting smaller FP) may be adopted to mitigate the metabolic consequences arising from changes in structure (reduced kT). We recruited 12 young adults and asked them to walk on a force-sensing treadmill while prompting them to change FP (±20% & ±40% of typical) using targeted biofeedback. In models driven by experimental data from each of those conditions, we altered the kT of personalized musculoskeletal models across a physiological range (2-8% strain) and simulated individual-muscle metabolic costs for each kT and FP combination. We found that kT and FP independently affect walking metabolic cost, increasing with higher kT or as participants deviated from their typical FP. Our results show no evidence for an interaction between kT and FP in younger adults walking at fixed speeds. We also reveal complex individual muscle responses to the kT and FP landscape. For example, although total metabolic cost increased by 5% on average with combined reductions in kT and FP, the triceps surae muscles experienced a 7% local cost reduction on average. Our simulations suggest that reducing FP during walking would not mitigate the metabolic consequences of lower kT. Wearable devices and rehabilitative strategies can focus on either kT or FP to reduce age-related increases in walking metabolic cost.

Simulating human walking: a model-based reinforcement learning approach with musculoskeletal modeling

Introduction

Recent advancements in reinforcement learning algorithms have accelerated the development of control models with high-dimensional inputs and outputs that can reproduce human movement. However, the produced motion tends to be less human-like if algorithms do not involve a biomechanical human model that accounts for skeletal and muscle-tendon properties and geometry. In this study, we have integrated a reinforcement learning algorithm and a musculoskeletal model including trunk, pelvis, and leg segments to develop control modes that drive the model to walk.

Methods

We simulated human walking first without imposing target walking speed, in which the model was allowed to settle on a stable walking speed itself, which was 1.45 m/s. A range of other speeds were imposed for the simulation based on the previous self-developed walking speed. All simulations were generated by solving the Markov decision process problem with covariance matrix adaptation evolution strategy, without any reference motion data.

Results

Simulated hip and knee kinematics agreed well with those in experimental observations, but ankle kinematics were less well-predicted.

Discussion

We finally demonstrated that our reinforcement learning framework also has the potential to model and predict pathological gait that can result from muscle weakness.

Computer simulation on the cueing movements in cue sports: a validation study

Background

Simulation models have been applied to analyze daily living activities and some sports movements. However, it is unknown whether the current upper extremity musculoskeletal models can be utilized for investigating cue sports movements to generate corresponding kinematic and muscle activation profiles. This study aimed to test the feasibility of applying simulation models to investigate cue sports players' cueing movements with OpenSim. Preliminary muscle forces would be calculated once the model is validated.

Methods

A previously customized and validated unimanual upper extremity musculoskeletal model with six degrees of freedom at the scapula, shoulder, elbow, and wrist, as well as muscles was used in this study. Two types of cueing movements were simulated: (1) the back spin shot, and (2) 9-ball break shot. Firstly, kinematic data of the upper extremity joints were collected with a 3D motion capture system. Using the experimental marker trajectories of the back spin shot on 10 male cue sports players, the simulation on the cueing movements was executed. The model was then validated by comparing the model-generated joint angles against the experimental results using statistical parametric mapping (SPM1D) to examine the entire angle-time waveform as well as t-tests to compare the discrete variables (e.g., joint range of motion). Secondly, simulation of the break shot was run with the experimental marker trajectories and electromyographic (EMG) data of two male cue sports players as the model inputs. A model-estimated muscle activation calculation was performed accordingly for the upper extremity muscles.

Results

The OpenSim-generated joint angles for the back spin shot corresponded well with the experimental results for the elbow, while the model outputs of the shoulder deviated from the experimental data. The discrepancy in shoulder joint angles could be due to the insufficient kinematic inputs for the shoulder joint. In the break shot simulation, the preliminary findings suggested that great shoulder muscle forces could primarily contribute to the forward swing in a break shot. This suggests that strengthening the shoulder muscles may be a viable strategy to improve the break shot performance.

Conclusion

It is feasible to cater simulation modeling in OpenSim for biomechanical investigations of the upper extremity movements in cue sports. Model outputs can help better understand the contributions of individual muscle forces when performing cueing movements.

Modeling Human Suboptimal Control: A Review

This review paper provides an overview of the approaches to model neuromuscular control, focusing on methods to identify nonoptimal control strategies typical of populations with neuromuscular disorders or children. Where possible, the authors tightened the description of the methods to the mechanisms behind the underlying biomechanical and physiological rationale. They start by describing the first and most simplified approach, the reductionist approach, which splits the role of the nervous and musculoskeletal systems. Static optimization and dynamic optimization methods and electromyography-based approaches are summarized to highlight their limitations and understand (the need for) their developments over time. Then, the authors look at the more recent stochastic approach, introduced to explore the space of plausible neural solutions, thus implementing the uncontrolled manifold theory, according to which the central nervous system only controls specific motions and tasks to limit energy consumption while allowing for some degree of adaptability to perturbations. Finally, they explore the literature covering the explicit modeling of the coupling between the nervous system (acting as controller) and the musculoskeletal system (the actuator), which may be employed to overcome the split characterizing the reductionist approach.

Individual Muscle Contributions to the Acceleration of the Center of Mass During the Barbell Back Squat in Trained Female Subjects

ABSTRACT

Goodman, WW, Helms, E, and Graham, DF. Individual muscle contributions to the acceleration of the center of mass during the barbell back squat in trained female subjects. J Strength Cond Res 37(10): 1947-1954, 2023-The squat is used to enhance performance and rehabilitate the lower body. However, muscle forces and how muscles accelerate the center of mass (CoM) are not well understood. The purpose was to determine how lower extremity muscles contribute to the vertical acceleration of the CoM when squatting to parallel using 85% one-repetition maximum. Thirteen female subjects performed squats in a randomized fashion. Musculoskeletal modeling was used to obtain muscle forces and muscle-induced accelerations. The vasti, soleus, and gluteus maximus generated the largest upward accelerations of the CoM, whereas the muscles that produced the largest downward acceleration about the CoM were the hamstrings, iliopsoas, adductors, and tibialis anterior. Our findings indicate that a muscle's function is task and posture specific. That is, muscle function depends on both joint position and how an individual is interacting with the environment.

Development of a more biofidelic musculoskeletal model with humeral head translation and glenohumeral ligaments

Computational musculoskeletal modeling is useful for understanding upper extremity biomechanics, especially when in vivo tests are unfeasible. A musculoskeletal model of the upper limb with increased biofidelity was developed by including humeral head translation (HHT) and ligaments. The model was validated and ligament contribution and effect of shoulder (thoracohumeral) elevation on HHT was evaluated. Humerus translated superiorly with increased elevation, with translations closely matching (avg. difference 2.83 mm) previous in vitro studies. HHT and ligament inclusion in the model will improve biomechanical predictions of upper extremity movements and study of conditions, like subacromial impingement, rotator cuff tear, or shoulder instability.

Inverse distance weighting to rapidly generate large simulation datasets

Obtaining large biomechanical datasets for machine learning is an ongoing challenge. Physics-based simulations offer one approach for generating large datasets, but many simulation methods, such as computed muscle control (CMC), are computationally costly. In contrast, interpolation methods, such as inverse distance weighting (IDW), are computationally fast. We examined whether IDW is a low-cost and accurate approach for interpolating muscle activations from CMC.IDW was evaluated using lateral pinch simulations in OpenSim. Simulated pinch data were organized into grids of varying sparsity (high, medium, and low density), where each grid point represented the muscle activations associated with a unique combination of mass and height of a young adult. For each grid, muscle activations were calculated via CMC and IDW for 108 random mass-height pairs that were not coincident with simulation grid vertices. We evaluated the interpolation errors from IDW for each grid, as well as the sensitivity of lateral pinch force to these errors. The root mean square error (RMSE) associated with interpolated muscle activations decreased with increasing grid density and never exceeded 4%. While CMC received a target thumb-tip force of 40 N, errors from the interpolated muscle activations never impacted the simulated force magnitude by more than 0.1 N. Furthermore, the computation time for CMC simulations averaged 4.22 core-minutes, while IDW averaged 0.95 core-seconds per mass-height pair.These results indicate IDW is a practical approach for rapidly estimating muscle activations from sparse CMC datasets. Future works could adapt our IDW approach to evaluate other tasks, biomechanical features, and/or populations.

The Determination of Assistance-as-Needed Support by an Ankle-Foot Orthosis for Patients with Foot Drop

Patients who suffer from foot drop have impaired gait pattern functions and a higher risk of stumbling and falling. Therefore, they are usually treated with an assistive device, a so-called ankle-foot orthosis. The support of the orthosis should be in accordance with the motor requirements of the patient and should only be provided when needed, which is referred to as assistance-as-needed. Thus, in this publication, an approach is presented to determine the assistance-as-needed support using musculoskeletal human models. Based on motion capture recordings of multiple subjects performing gaits at different speeds, a parameter study varying the optimal force of a reserve actuator representing the ankle-foot orthosis added in the musculoskeletal simulation is conducted. The results show the dependency of the simulation results on the selected optimal force of the reserve actuator but with a possible identification of the assistance-as-needed support required from the ankle-foot orthosis. The required increase in support due to the increasing severity of foot drop is especially demonstrated with the approach. With this approach, information for the required support of individual subjects can be gathered, which can further be used to derive the design of an ankle-foot orthosis that optimally assists the subjects.

Muscle-Specific Contributions to Vertical Ground Reaction Force Profiles During Countermovement Jumps: Case Studies in College Basketball Players

ABSTRACT

Kipp, K and Kim, H. Muscle-specific contributions to vertical ground reaction force profiles during countermovement jumps: case studies in college basketball players. J Strength Cond Res 37(7): 1523-1529, 2023-The purpose of this study was to determine muscle-specific contributions to various types of vertical ground reaction force (vGRF) profiles in collegiate basketball players. Players from a men's ( n = 5; height: 1.84 ± 0.14 m; mass: 92.8 ± 11.4 kg) and a women's ( n = 5; 1.71 ± 0.09 m; mass: 80.1 ± 17.6 kg) basketball team completed 3-5 countermovement jumps (CMJ) while motion capture and force plate data were recorded. Muscle-specific contributions to vGRF were calculated through vGRF decomposition analysis. Profiles of vGRF were analyzed based on the presence of unimodal or bimodal peaks during the CMJ. The results showed that the soleus (SOL), gastrocnemii (GAS), vastii (VAS), and gluteus maximus (GMX) muscles all contributed to upward vGRF generation throughout the entire CMJ duration. The contributions were greatest for the SOL (1.78 body weight [BW]), intermediate for the GAS (0.96 BW) and VAS (0.72 BW), and negligible for the GMX (0.11 BW). For unimodal vGRF profiles, SOL contributions coincided with peak vGRF, whereas VAS contributions were stable throughout most of the CMJ. For bimodal vGRF profiles, SOL and VAS contributions explained the presence of the first vGRF peak, whereas GAS and VAS contributions explained the second vGRF peak. Differences between vGRF profiles appear to be the result of distinct force contributions from the VAS muscle, which may have implications for the analysis of vGRF time series data during CMJ testing.

Closed-Chain Inverse Dynamics for the Biomechanical Analysis of Manual Material Handling Tasks through a Deep Learning Assisted Wearable Sensor Network

Despite the automatization of many industrial and logistics processes, human workers are still often involved in the manual handling of loads. These activities lead to many work-related disorders that reduce the quality of life and the productivity of aged workers. A biomechanical analysis of such activities is the basis for a detailed estimation of the biomechanical overload, thus enabling focused prevention actions. Thanks to wearable sensor networks, it is now possible to analyze human biomechanics by an inverse dynamics approach in ecological conditions. The purposes of this study are the conceptualization, formulation, and implementation of a deep learning-assisted fully wearable sensor system for an online evaluation of the biomechanical effort that an operator exerts during a manual material handling task. In this paper, we show a novel, computationally efficient algorithm, implemented in ROS, to analyze the biomechanics of the human musculoskeletal systems by an inverse dynamics approach. We also propose a method for estimating the load and its distribution, relying on an egocentric camera and deep learning-based object recognition. This method is suitable for objects of known weight, as is often the case in logistics. Kinematic data, along with foot contact information, are provided by a fully wearable sensor network composed of inertial measurement units. The results show good accuracy and robustness of the system for object detection and grasp recognition, thus providing reliable load estimation for a high-impact field such as logistics. The outcome of the biomechanical analysis is consistent with the literature. However, improvements in gait segmentation are necessary to reduce discontinuities in the estimated lower limb articular wrenches.

A review of the efforts to develop muscle and musculoskeletal models for biomechanics in the last 50 years

Both the Hill and the Huxley muscle models had already been described by the time the International Society of Biomechanics was founded 50 years ago, but had seen little use before the 1970s due to the lack of computing. As computers and computational methods became available in the 1970s, the field of musculoskeletal modeling developed and Hill type muscle models were adopted by biomechanists due to their relative computational simplicity as compared to Huxley type muscle models. Muscle forces computed by Hill type muscle models provide good agreement in conditions similar to the initial studies, i.e. for small muscles contracting under steady and controlled conditions. However, more recent validation studies have identified that Hill type muscle models are least accurate for natural in vivo locomotor behaviours at submaximal activations, fast speeds and for larger muscles, and thus need to be improved for their use in understanding human movements. Developments in muscle modelling have tackled these shortcomings. However, over the last 50 years musculoskeletal simulations have been largely based on traditional Hill type muscle models or even simplifications of this model that neglected the interaction of the muscle with a compliant tendon. The introduction of direct collocation in musculoskeletal simulations about 15 years ago along with further improvements in computational power and numerical methods enabled the use of more complex muscle models in simulations of whole-body movement. Whereas Hill type models are still the norm, we may finally be ready to adopt more complex muscle models into musculoskeletal simulations of human movement.

A Musculoskeletal Model of the Hand and Wrist Capable of Simulating Functional Tasks

OBJECTIVE

The purpose of this work was to develop an open-source musculoskeletal model of the hand and wrist and to evaluate its performance during simulations of functional tasks.

METHODS

The current model was developed by adapting and expanding upon existing models. An optimal control theory framework that combines forward-dynamics simulations with a simulated-annealing optimization was used to simulate maximum grip and pinch force. Active and passive hand opening were simulated to evaluate coordinated kinematic hand movements.

RESULTS

The model's maximum grip force production matched experimental measures of grip force, force distribution amongst the digits, and displayed sensitivity to wrist flexion. Simulated lateral pinch strength replicated in vivo palmar pinch strength data. Additionally, predicted activations for 7 of 8 muscles fell within variability of EMG data during palmar pinch. The active and passive hand opening simulations predicted reasonable activations and demonstrated passive motion mimicking tenodesis, respectively.

CONCLUSION

This work advances simulation capabilities of hand and wrist models and provides a foundation for future work to build upon.

SIGNIFICANCE

This is the first open-source musculoskeletal model of the hand and wrist to be implemented during both functional kinetic and kinematic tasks. We provide a novel simulation framework to predict maximal grip and pinch force which can be used to evaluate how potential surgical and rehabilitation interventions influence these functional outcomes while requiring minimal experimental data.

Patella height influences patellofemoral contact and kinematics following cruciate-retaining total knee replacement

The role of patella height is discussed controversially in total knee arthroplasty (TKA). Therefore, this computational study aims to systematically analyze the biomechanical effect of different patella heights on patellofemoral (PF) forces and kinematics after cruciate-retaining (CR) TKA. We implemented a CR bicondylar TKA with a dome patellar button in a validated dynamic musculoskeletal multibody model of a male human knee joint. Retropatellar dynamics (contact force [N], shear force [N], patellar shift [mm], tilt [°], and rotation [°]) were evaluated during dual-limb squat motion (flexion from 0° to 90°) with simulated active muscle forces and the effects of different patella heights (Blackburne-Peel [BP] ratio of 0.39, 0.49, 0.65, 0.85, 1.01, and 1.1 were systematically examined). As active knee flexion increased, PF contact force also increased. Patella alta (BP = 1.1) resulted in higher PF contact forces compared to normal patella height (BP = 0.65) by up to 16%. Contrarily, patella baja was associated with decreased PF forces by 7%. Compared to patella baja (BP = 0.39), patella alta (BP = 1.1) considerably increased the contact force by up to 25%. Different patellar heights mainly affected PF shear forces during early knee flexion. Concerning PF kinematics, patella alta (BP = 1.1) yielded a greater lateral tilt of more than 4° and higher patellar rotation by up to 3° during deep knee flexion, compared to normal patella height (BP = 0.65). Our computational study indicates that patella alta is associated with the highest PF contact and shear force after the implantation of a CR bicondylar TKA. This should be considered in PF disorders following TKA.

Inertia-Constrained Reinforcement Learning to Enhance Human Motor Control Modeling

Locomotor impairment is a highly prevalent and significant source of disability and significantly impacts the quality of life of a large portion of the population. Despite decades of research on human locomotion, challenges remain in simulating human movement to study the features of musculoskeletal drivers and clinical conditions. Most recent efforts to utilize reinforcement learning (RL) techniques are promising in the simulation of human locomotion and reveal musculoskeletal drives. However, these simulations often fail to mimic natural human locomotion because most reinforcement strategies have yet to consider any reference data regarding human movement. To address these challenges, in this study, we designed a reward function based on the trajectory optimization rewards (TOR) and bio-inspired rewards, which includes the rewards obtained from reference motion data captured by a single Inertial Moment Unit (IMU) sensor. The sensor was equipped on the participants' pelvis to capture reference motion data. We also adapted the reward function by leveraging previous research on walking simulations for TOR. The experimental results showed that the simulated agents with the modified reward function performed better in mimicking the collected IMU data from participants, which means that the simulated human locomotion was more realistic. As a bio-inspired defined cost, IMU data enhanced the agent's capacity to converge during the training process. As a result, the models' convergence was faster than those developed without reference motion data. Consequently, human locomotion can be simulated more quickly and in a broader range of environments, with a better simulation performance.

Comparison of the dynamics of exoskeletal-assisted and unassisted locomotion in an FDA-approved lower extremity device: Controlled experiments and development of a subject-specific virtual simulator

Robotic exoskeletons have considerable, but largely untapped, potential to restore mobility in individuals with neurological disorders, and other conditions that result in partial or complete immobilization. The growing demand for these devices necessitates the development of technology to characterize the human-robot system during exoskeletal-assisted locomotion (EAL) and accelerate robot design refinements. The goal of this study was to combine controlled experiments with computational modeling to build a virtual simulator of EAL. The first objective was to acquire a minimum empirical dataset comprising human-robot kinematics, ground reaction forces, and electromyography during exoskeletal-assisted and unassisted locomotion from an able-bodied participant. The second objective was to quantify the dynamics of the human-robot system using a subject-specific virtual simulator reproducing EAL compared to the dynamics of normal gait. We trained an able-bodied participant to ambulate independently in a Food and Drug Administration-approved exoskeleton, the ReWalk P6.0 (ReWalk Robotics, Yoknaem, Israel). We analyzed the motion of the participant during exoskeletal-assisted and unassisted walking, sit-to-stand, and stand-to-sit maneuvers, with simultaneous measurements of (i) three-dimensional marker trajectories, (ii) ground reaction forces, (iii) electromyography, and (iv) exoskeleton encoder data. We created a virtual simulator in OpenSim, comprising a whole-body musculoskeletal model and a full-scale exoskeleton model, to determine the joint kinematics and moments during exoskeletal-assisted and unassisted maneuvers. Mean peak knee flexion angles of the human subject during exoskeletal-assisted walking were 50.1° ± 0.6° (left) and 52.6° ± 0.7° (right), compared to 68.6° ± 0.3° (left) and 70.7° ± 1.1° (right) during unassisted walking. Mean peak knee extension moments during exoskeletal-assisted walking were 0.10 ± 0.10 Nm/kg (left) and 0.22 ± 0.11 Nm/kg (right), compared to 0.64 ± 0.07 Nm/kg (left) and 0.73 ± 0.10 Nm/kg (right) during unassisted walking. This work provides a foundation for parametric studies to characterize the effects of human and robot design variables, and predictive modeling to optimize human-robot interaction during EAL.

Effect of sprinting velocity on anterior cruciate ligament and knee load during sidestep cutting

The purpose of the study was to investigate the effect of an increase in sprinting velocity on the anterior cruciate ligament (ACL) load, knee joint load, and activation of femoral muscles using the musculoskeletal modeling approach. Fourteen high school male athletes were recruited (age: 17.4 ± 0.7 years, height: 1.75 ± 0.04 m, weight: 73.3 ± 8.94 kg), with the right foot dominant and physical activity level of about 3-4 h per day. The kinematics, kinetics, and co-contraction index (CCI) of the extensors and flexors of the right leg's femoral muscles were calculated. The anterior cruciate ligament load was estimated using the musculoskeletal modeling method. In the results, it was observed that the anterior cruciate ligament load (p < 0.017) increased as sidestep cutting velocity increased, resulting in increased adduction (p < 0.017) and the internal rotation moment of the knee joint. This was significantly higher than when sprinting at a similar velocity. The co-contraction index result, which represents the balanced activation of the femoral extensor and flexor muscles, showed a tendency of decrement with increasing sprinting velocity during sidestep cutting (p < 0.017), whereas no significant differences were observed when running at different sprinting conditions. Therefore, we postulate that factors such as knee joint shear force, extended landing posture with increasing sprinting velocity, internal rotation moment, and femoral muscle activity imbalance influence the increase of anterior cruciate ligament load during a sidestep cutting maneuver.

Head supported mass, moment of inertia, neck loads and stability: A simulation study

Occupations or activities where donning head-supported mass (HSM) is commonplace put operators at an elevated risk of chronic neck pain. Yet, there is no consensus about what features of HSM influence the relative contributions to neck loads. Therefore, we tested four hypotheses that could increase neck loads: (i) HSM increases gravitational moments; (ii) more muscle activation is required to stabilize the head with HSM; (iii) the position of the HSM centre of mass (COM) induces gravitational moments; and (iv) the added moment of inertia (MOI) from HSM increases neck loads during head repositioning tasks. We performed a sensitivity analysis on the C5-C6 compression evaluated from a 24-degree freedom cervical spine model in OpenSim for static and dynamic movement trials. For static trials, we varied the magnitude of HSM, the position of its COM, and developed a novel stability constraint for static optimization. In dynamic trials, we varied HSM and the three principle MOIs. HSM magnitude and compression were linearly related to one another for both static and dynamic trials, with amplification factors varying between 1.9 and 3.9. Similar relationships were found for the COM position, although the relationship between C5-C6 peak compression and MOI in dynamic trials was generally nonlinear. This sensitivity analysis uncovered evidence in favour of hypotheses (i), (ii) and (iii). However, the model's prediction of C5-C6 compression was not overly sensitive to the magnitude of MOI. Therefore, the HSM mass properties may be more influential on neck compression than MOI properties, even during dynamic tasks.

Applying a muscle fatigue model when optimizing load-sharing between muscles for short-duration high-intensity exercise: A preliminary study

Introduction: Multiple different mathematical models have been developed to represent muscle force, to represent multiple muscles in the musculoskeletal system, and to represent muscle fatigue. However, incorporating these different models together to describe the behavior of a high-intensity exercise has not been well described. Methods: In this work, we adapted the three-compartment controller (3CCr) muscle fatigue model to be implemented with an inverse-dynamics based optimization algorithm for the muscle recruitment problem for 7 elbow muscles to model a benchmark case: elbow flexion/extension moments. We highlight the difficulties in achieving an accurate subject-specific approach for this multi-level modeling problem, considering different muscular models, compared with experimental measurements. Both an isometric effort and a dynamic bicep curl were considered, where muscle activity and resting periods were simulated to obtain the fatigue behavior. Muscle parameter correction, scaling and calibration are addressed in this study. Moreover, fiber-type recruitment hierarchy in force generation was added to the optimization problem, thus offering an additional novel muscle modeling criterion. Results: It was observed that: i) the results were most accurate for the static case; ii) insufficient torque was predicted by the model at some time points for the dynamic case, which benefitted from a more precise calibration of muscle parameters; iii) modeling the effects of muscular potentiation may be important; and iv) for this multilevel model approach, the 3CCr model had to be modified to avoid reaching situations of unrealistic constant fatigue in high intensity exercise-resting cycles. Discussion: All the methods yield reasonable estimations, but the complexity of obtaining accurate subject-specific human models is highlighted in this study. The proposed novel muscle modeling and force recruitment criterion, which consider the muscular fiber-type distinction, show interesting preliminary results.

Computer-based analysis of different component positions and insert thicknesses on tibio-femoral and patello-femoral joint dynamics after cruciate-retaining total knee replacement

BACKGROUND

Positioning of the implant components and tibial insert thickness constitute critical aspects of total knee replacement (TKR) that influence the postoperative knee joint dynamics. This study aimed to investigate the impact of implant component positioning (anterior-posterior and medio-lateral shift) and varying tibial insert thickness on the tibio-femoral (TF) and patello-femoral (PF) joint kinematics and contact forces after cruciate-retaining (CR)-TKR.

METHOD

A validated musculoskeletal multibody simulation (MMBS) model with a fixed-bearing CR-TKR during a squat motion up to 90° knee flexion was deployed to calculate PF and TF joint dynamics for varied implant component positions and tibial insert thicknesses. Evaluation was performed consecutively by comparing the respective knee joint parameters (e.g. contact force, quadriceps muscle force, joint kinematics) to a reference implant position.

RESULTS

The PF contact forces were mostly affected by the anterior-posterior as well as medio-lateral positioning of the femoral component (by 3 mm anterior up to 31 % and by 6 mm lateral up to 14 %). TF contact forces were considerably altered by tibial insert thickness (24 % in case of + 4 mm increase) and by the anterior-posterior position of the femoral component (by 3 mm posterior up to 16 %). Concerning PF kinematics, a medialised femoral component by 6 mm increased the lateral patellar tilt by more than 5°.

CONCLUSIONS

Our results indicate that regarding PF kinematics and contact forces the positioning of the femoral component was more critical than the tibial component. The positioning of the femoral component in anterior-posterior direction on and PF contact force was evident. Orthopaedic surgeons should strictly monitor the anterior-posterior as well as the medio-lateral position of the femoral component and the insert thickness.

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.

Movement compensation is driven by the deltoid and teres minor muscles following severe rotator cuff tear

BACKGROUND

Rotator cuff tears are common in older adults, negatively affecting function. Previous simulation-based studies reported more posterior and superior oriented glenohumeral loading with increased cuff tear severity and task performance, although corresponding muscle compensation strategies are unclear. Our objective is to determine how shoulder muscle forces change with increased rotator cuff tear severity during functional task performance.

METHODS

Eight musculoskeletal models of increasing tear severity were developed to represent no rotator cuff tear to massive three-tendon tears. Simulations were performed using each combination of model and kinematics for five functional tasks. Individual muscle forces were averaged for each task and tear severity, then normalized by the sum of the muscle forces across the shoulder. Forces were compared across tear severity and muscle.

FINDINGS

For muscle force contribution, interactions between tear severity and muscle and a main effect of muscle were seen for all tasks (P < 0.0001). Middle deltoid increased force contribution by >10% in the greatest tear severity model compared to no cuff tear model for all tasks (all P < 0.0001). Teres minor contribution increased by 7.7%, 5.6%, and 11% in the greatest tear severity model compared to the no cuff tear model for forward reach, axilla wash, and upward reach 105° tasks, respectively (all P < 0.0001).

INTERPRETATION

Functional tasks elicit compensatory responses from uninjured muscles following severe cuff tears, notably in middle deltoid and teres minor, leading to posterior-superior glenohumeral loading. The muscles are potential targets for strengthening to avoid injury from sustained increased muscle force.

Contributions of individual muscle forces to hip, knee, and ankle contact forces during the stance phase of running: a model-based study

Knowledge of muscle forces' contributions to the joint contact forces can assist in the evaluation of muscle function, joint injury prevention, treatment of gait disorders, and arthroplasty planning. This study's objective was to evaluate the contributions of human lower limb muscles to the hip, knee, and ankle joint contact forces during the stance phase of running. A total of 25 muscles (or groups) were investigated based on the OpenSim framework along the anterior-posterior, superoinferior, and mediolateral components of each joint coordinate system. It was revealed that, during the running stance phase, the gluteus medius, gluteus maximus, and iliopsoas mainly contributed to the hip contact force. The soleus, vastus group, and rectus femoris primarily contributed to the knee contact force, while the peroneus, soleus, gluteus medius, and gastrocnemius mainly contributed to the ankle joint force; some muscles simultaneously offloaded the joints during the stance phase. The distributive pattern of the individual muscle functions contributing to the joint load may substantially differ during the running and walking stance phases. This study's findings may further provide suggestive information for the design of lower limb joint prosthesis, the study of the biomechanics of pathologic walking and running, and the progression of joint osteoarthritis.

Hierarchical Motion Learning for Goal-Oriented Movements With Speed-Accuracy Tradeoff of a Musculoskeletal System

Generating various goal-oriented movements via the flexible muscle model of the musculoskeletal system as fast and accurately as possible is a pressing problem, which is also the basis of most human adaptive behaviors, such as reaching, catching, interception, and pointing. This article focuses on the adaptive motion generation of fast goal-oriented motion on the musculoskeletal system by implementing the speed-accuracy tradeoff (SAT) in a hierarchical motion learning framework. First, we introduce Fitts' Law into the modified basal ganglia circuit-inspired iterative decision-making model for achieving dynamic and adaptive decision making. Then, as a time constraint, the decision is decomposed into a series of supervised terms by the proposed striatal FSI-SPN interneuron circuit-inspired velocity modulator to implement the tradeoff smoothly on the musculoskeletal system. Finally, an improved policy gradient algorithm is suggested to generate the muscle excitations of the modulated motion via the proposed muscle co-contraction policy, which promotes general cooperation between flexor and extensor muscles. In experiments, a redundant musculoskeletal arm model is trained to perform the adaptive quick pointing movements. By combining the muscle co-contraction policy with SAT, our algorithm shows the most efficient training and the best performance in the adaptive motion generation among the other three popular reinforcement learning algorithms on the musculoskeletal model.

Multi-level personalization of neuromusculoskeletal models to estimate physiologically plausible knee joint contact forces in children

Neuromusculoskeletal models are a powerful tool to investigate the internal biomechanics of an individual. However, commonly used neuromusculoskeletal models are generated via linear scaling of generic templates derived from elderly adult anatomies and poorly represent a child, let alone children with a neuromuscular disorder whose musculoskeletal structures and muscle activation patterns are profoundly altered. Model personalization can capture abnormalities and appropriately describe the underlying (altered) biomechanics of an individual. In this work, we explored the effect of six different levels of neuromusculoskeletal model personalization on estimates of muscle forces and knee joint contact forces to tease out the importance of model personalization for normal and abnormal musculoskeletal structures and muscle activation patterns. For six children, with and without cerebral palsy, generic scaled models were developed and progressively personalized by (1) tuning and calibrating musculotendon units' parameters, (2) implementing an electromyogram-assisted approach to synthesize muscle activations, and (3) replacing generic anatomies with image-based bony geometries, and physiologically and physically plausible muscle kinematics. Biomechanical simulations of gait were performed in the OpenSim and CEINMS software on ten overground walking trials per participant. A mixed-ANOVA test, with Bonferroni corrections, was conducted to compare all models' estimates. The model with the highest level of personalization produced the most physiologically plausible estimates. Model personalization is crucial to produce physiologically plausible estimates of internal biomechanical quantities. In particular, personalization of musculoskeletal anatomy and muscle activation patterns had the largest effect overall. Increased research efforts are needed to ease the creation of personalized neuromusculoskeletal models.

Evaluation of musculoskeletal models, scaling methods, and performance criteria for estimating muscle excitations and fiber lengths across walking speeds

Muscle-driven simulations have been widely adopted to study muscle-tendon behavior; several generic musculoskeletal models have been developed, and their biofidelity improved based on available experimental data and computational feasibility. It is, however, not clear which, if any, of these models accurately estimate muscle-tendon dynamics over a range of walking speeds. In addition, the interaction between model selection, performance criteria to solve muscle redundancy, and approaches for scaling muscle-tendon properties remain unclear. This study aims to compare estimated muscle excitations and muscle fiber lengths, qualitatively and quantitatively, from several model combinations to experimental observations. We tested three generic models proposed by Hamner et al., Rajagopal et al., and Lai-Arnold et al. in combination with performance criteria based on minimization of muscle effort to the power of 2, 3, 5, and 10, and four approaches to scale the muscle-tendon unit properties of maximum isometric force, optimal fiber length, and tendon slack length. We collected motion analysis and electromyography data in eight able-bodied subjects walking at seven speeds and compared agreement between estimated/modelled muscle excitations and observed muscle excitations from electromyography and computed normalized fiber lengths to values reported in the literature. We found that best agreement in on/off timing in vastus lateralis, vastus medialis, tibialis anterior, gastrocnemius lateralis, gastrocnemius medialis, and soleus was estimated with minimum squared muscle effort than to higher exponents, regardless of model and scaling approach. Also, minimum squared or cubed muscle effort with only a subset of muscle-tendon unit scaling approaches produced the best time-series agreement and best estimates of the increment of muscle excitation magnitude across walking speeds. There were discrepancies in estimated fiber lengths and muscle excitations among the models, with the largest discrepancy in the Hamner et al. model. The model proposed by Lai-Arnold et al. best estimated muscle excitation estimates overall, but failed to estimate realistic muscle fiber lengths, which were better estimated with the model proposed by Rajagopal et al. No single model combination estimated the most accurate muscle excitations for all muscles; commonly observed disagreements include onset delay, underestimated co-activation, and failure to estimate muscle excitation increments across walking speeds.