Computer modeling and simulation of human movement

Full Hill-type muscle model of the I1/I3 retractor muscle complex in Aplysia californica

The coordination of complex behavior requires knowledge of both neural dynamics and the mechanics of the periphery. The feeding system of Aplysia californica is an excellent model for investigating questions in soft body systems' neuromechanics because of its experimental tractability. Prior work has attempted to elucidate the mechanical properties of the periphery by using a Hill-type muscle model to characterize the force generation capabilities of the key protractor muscle responsible for moving Aplysia's grasper anteriorly, the I2 muscle. However, the I1/I3 muscle, which is the main driver of retractions of Aplysia's grasper, has not been characterized. Because of the importance of the musculature's properties in generating functional behavior, understanding the properties of muscles like the I1/I3 complex may help to create more realistic simulations of the feeding behavior of Aplysia, which can aid in greater understanding of the neuromechanics of soft-bodied systems. To bridge this gap, in this work, the I1/I3 muscle complex was characterized using force-frequency, length-tension, and force-velocity experiments and showed that a Hill-type model can accurately predict its force-generation properties. Furthermore, the muscle's peak isometric force and stiffness were found to exceed those of the I2 muscle, and these results were analyzed in the context of prior studies on the I1/I3 complex's kinematics in vivo.

Biomechanical mechanisms of anterior cruciate ligament injury in the jerk dip phase of clean and jerk: A case study of an injury event captured on-site

Background

Weightlifting exposes athletes to significant loads, potentially placing the knee joint in an abnormal mechanical environment and leading to anterior cruciate ligament (ACL) injuries. Once an ACL injury occurs, it can affect athletes' competitive ability to varying degrees and even prematurely end their career. Understanding the biomechanical mechanisms of ACL injuries in weightlifters helps in comprehensively understanding the stress patterns and degrees on ACL during human movement, and identifying potential injury-causing factors, thereby enabling the implementation of appropriate preventive measures to reduce the occurrence of injuries. This study aimed to explore the biomechanical mechanisms of ACL injuries during the jerk dip phase of clean and jerk in weightlifters, providing a theoretical basis for the prevention of ACL injuries in weightlifting sports.

Methods

This study utilized the German SIMI Motion 10.2 movement analysis system and the AnyBody simulation system to analyze the kinematic and dynamic parameters of a 109 kg + class weightlifter (height: 191 cm, age: 22 years, weight: 148 kg, athletic level: elite) performing a 205 kg clean and jerk (non-injured) and a 210 kg clean and jerk (ACL injury occurred). The differences in kinematic and dynamic indicators of lower limb joints under injured and non-injured jerk dip conditions were investigated.

Results

Knee joint torque during non-injured clean and jerk was consistently positive (i.e., external rotation) but turned from positive to negative (i.e., from external rotation to internal rotation) during injured clean and jerk and reached a maximum internal rotation torque of 21.34 Nm at the moment of injury. At every moment, the muscle activation and simulated muscle force of the quadriceps and gastrocnemius during the injured clean and jerk were higher than those during the non-injured clean and jerk. By contrast, the muscle activation and simulated muscle force of the semitendinosus, semimembranosus, biceps femoris, and soleus during non-injured clean and jerk were higher than those during injured clean and jerk. The knee joint internal rotation angle during injured clean and jerk first increased and then declined, reaching a peak at 46.93° at the moment of injury, whereas it gradually increased during non-injured clean and jerk. The proximal tibia on the left side during the injured clean and jerk moved forward faster by 0.76 m/s compared with that during the non-injured clean and jerk.

Conclusions

The small muscle activation and simulated muscle force of the hamstring and soleus could not resist timely and effectively the large muscle activation and simulated muscle force of the quadriceps (especially the medial quad) and gastrocnemius. As such, the force applied to the ACL could exceed its ultimate load-bearing capacity. Kinematic indicators in the athlete's injured lift demonstrated certain disparities from those in their non-injured lift. Knee internal rotation and tibial anterior translation during the jerk dip phase of weightlifting might be the kinematic characteristics of ACL injuries.

Facial and mandibular landmark tracking with habitual head posture estimation using linear and fiducial markers

This study compared the accuracy of facial landmark measurements using deep learning-based fiducial marker (FM) and arbitrary width reference (AWR) approaches. It quantitatively analysed mandibular hard and soft tissue lateral excursions and head tilting from consumer camera footage of 37 participants. A custom deep learning system recognised facial landmarks for measuring head tilt and mandibular lateral excursions. Circular fiducial markers (FM) and inter-zygion measurements (AWR) were validated against physical measurements using electrognathography and electronic rulers. Results showed notable differences in lower and mid-face estimations for both FM and AWR compared to physical measurements. The study also demonstrated the comparability of both approaches in assessing lateral movement, though fiducial markers exhibited variability in mid-face and lower face parameter assessments. Regardless of the technique applied, hard tissue movement was typically seen to be 30% less than soft tissue among the participants. Additionally, a significant number of participants consistently displayed a 5 to 10° head tilt.

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.

Muscle-driven forward dynamic active hybrid model of the lumbosacral spine: combined FEM and multibody simulation

Most spine models belong to either the musculoskeletal multibody (MB) or finite element (FE) method. Recently, coupling of MB and FE models has increasingly been used to combine advantages of both methods. Active hybrid FE-MB models, still rarely used in spine research, avoid the interface and convergence problems associated with model coupling. They provide the inherent ability to account for the full interplay of passive and active mechanisms for spinal stability. In this paper, we developed and validated a novel muscle-driven forward dynamic active hybrid FE-MB model of the lumbosacral spine (LSS) in ArtiSynth to simultaneously calculate muscle activation patterns, vertebral movements, and internal mechanical loads. The model consisted of the rigid vertebrae L1-S1 interconnected with hyperelastic fiber-reinforced FE intervertebral discs, ligaments, facet joints, and force actuators representing the muscles. Morphological muscle data were implemented via a semi-automated registration procedure. Four auxiliary bodies were utilized to describe non-linear muscle paths by wrapping and attaching the anterior abdominal muscles. This included an abdominal plate whose kinematics was optimized using motion capture data from upper body movements. Intra-abdominal pressure was calculated from the forces of the abdominal muscles compressing the abdominal cavity. For the muscle-driven approach, forward dynamics assisted data tracking was used to predict muscle activation patterns that generate spinal postures and balance the spine without prescribing accurate spinal kinematics. During calibration, the maximum specific muscle tension and spinal rhythms resulting from the model dynamics were evaluated. To validate the model, load cases were simulated from -10° extension to +30° flexion with weights up to 20 kg in both hands. The biomechanical model responses were compared with in vivo literature data of intradiscal pressures, intra-abdominal pressures, and muscle activities. The results demonstrated high agreement with this data and highlight the advantages of active hybrid modeling for the LSS. Overall, this new self-contained tool provides a robust and efficient estimation of LSS biomechanical responses under in vivo similar loads, for example, to improve pain treatment by spinal stabilization therapies.

Estimation of Joint Torque by EMG-Driven Neuromusculoskeletal Models and LSTM Networks

Accurately predicting joint torque using wearable sensors is crucial for designing assist-as-needed exoskeleton controllers to assist muscle-generated torque and ensure successful task performance. In this paper, we estimated ankle dorsiflexion/plantarflexion, knee flexion/extension, hip flexion/extension, and hip abduction/adduction torques from electromyography (EMG) and kinematics during daily activities using neuromusculoskeletal (NMS) models and long short-term memory (LSTM) networks. The joint torque ground truth for model calibrating and training was obtained through inverse dynamics of captured motion data. A cluster approach that grouped movements based on characteristic similarity was implemented, and its ability to improve the estimation accuracy of both NMS and LSTM models was evaluated. We compared torque estimation accuracy of NMS and LSTM models in three cases: Pooled, Individual, and Clustered models. Pooled models used data from all 10 movements to calibrate or train one model, Individual models used data from each individual movement, and Clustered models used data from each cluster. Individual, Clustered and Pooled LSTM models all had relatively high joint torque estimation accuracy. Individual and Clustered NMS models had similarly good estimation performance whereas the Pooled model may be too generic to satisfy all movement patterns. While the cluster approach improved the estimation accuracy in NMS models in some movements, it made relatively little difference in the LSTM neural networks, which already had high estimation accuracy. Our study provides practical implications for designing assist-as-needed exoskeleton controllers by offering guidelines for selecting the appropriate model for different scenarios, and has potential to enhance the functionality of wearable exoskeletons and improve rehabilitation and assistance for individuals with motor disorders.

Neuromusculoskeletal model-informed machine learning-based control of a knee exoskeleton with uncertainties quantification

Introduction

Research interest in exoskeleton assistance strategies that incorporate the user's torque capacity is growing rapidly. However, the predicted torque capacity from users often includes uncertainty from various sources, which can have a significant impact on the safety of the exoskeleton-user interface.

Methods

To address this challenge, this paper proposes an adaptive control framework for a knee exoskeleton that uses muscle electromyography (EMG) signals and joint kinematics. The framework predicted the user's knee flexion/extension torque with confidence bounds to quantify the uncertainty based on a neuromusculoskeletal (NMS) solver-informed Bayesian Neural Network (NMS-BNN). The predicted torque, with a specified confidence level, controlled the assistive torque provided by the exoskeleton through a TCP/IP stream. The performance of the NMS-BNN model was also compared to that of the Gaussian process (NMS-GP) model.

Results

Our findings showed that both the NMS-BNN and NMS-GP models accurately predicted knee joint torque with low error, surpassing traditional NMS models. High uncertainties were observed at the beginning of each movement, and at terminal stance and terminal swing in self-selected speed walking in both NMS-BNN and NMS-GP models. The knee exoskeleton provided the desired assistive torque with a low error, although lower torque was observed during terminal stance of fast walking compared to self-selected walking speed.

Discussion

The framework developed in this study was able to predict knee flexion/extension torque with quantifiable uncertainty and to provide adaptive assistive torque to the user. This holds significant potential for the development of exoskeletons that provide assistance as needed, with a focus on the safety of the exoskeleton-user interface.

Effect of neglecting passive spinal structures: a quantitative investigation using the forward-dynamics and inverse-dynamics musculoskeletal approach

Purpose: Inverse-dynamics (ID) analysis is an approach widely used for studying spine biomechanics and the estimation of muscle forces. Despite the increasing structural complexity of spine models, ID analysis results substantially rely on accurate kinematic data that most of the current technologies are not capable to provide. For this reason, the model complexity is drastically reduced by assuming three degrees of freedom spherical joints and generic kinematic coupling constraints. Moreover, the majority of current ID spine models neglect the contribution of passive structures. The aim of this ID analysis study was to determine the impact of modelled passive structures (i.e., ligaments and intervertebral discs) on remaining joint forces and torques that muscles must balance in the functional spinal unit. Methods: For this purpose, an existing generic spine model developed for the use in the demoa software environment was transferred into the musculoskeletal modelling platform OpenSim. The thoracolumbar spine model previously used in forward-dynamics (FD) simulations provided a full kinematic description of a flexion-extension movement. By using the obtained in silico kinematics, ID analysis was performed. The individual contribution of passive elements to the generalised net joint forces and torques was evaluated in a step-wise approach increasing the model complexity by adding individual biological structures of the spine. Results: The implementation of intervertebral discs and ligaments has significantly reduced compressive loading and anterior torque that is attributed to the acting net muscle forces by -200% and -75%, respectively. The ID model kinematics and kinetics were cross-validated against the FD simulation results. Conclusion: This study clearly shows the importance of incorporating passive spinal structures on the accurate computation of remaining joint loads. Furthermore, for the first time, a generic spine model was used and cross-validated in two different musculoskeletal modelling platforms, i.e., demoa and OpenSim, respectively. In future, a comparison of neuromuscular control strategies for spinal movement can be investigated using both approaches.

Exploring the effects of skeletal architecture and muscle properties on bipedal standing in the common chimpanzee (Pan troglodytes) from the perspective of biomechanics

Introduction: It is well known that the common chimpanzee, as both the closest living relative to humans and a facultative bipedal, has the capability of bipedal standing but cannot do so fully upright. Accordingly, they have been of exceeding significance in elucidating the evolution of human bipedalism. There are many reasons why the common chimpanzee can only stand with its hips-knees bent, such as the distally oriented long ischial tubercle and the almost absent lumbar lordosis. However, it is unknown how the relative positions of their shoulder-hip-knee-ankle joints are coordinated. Similarly, the distribution of the biomechanical characteristics of the lower-limb muscles and the factors that affect the erectness of standing as well as the muscle fatigue of the lower limbs remain a mystery. The answers are bound to light up the evolutional mechanism of hominin bipedality, but these conundrums have not been shed much light upon, because few studies have comprehensively explored the effects of skeletal architecture and muscle properties on bipedal standing in common chimpanzees. Methods: Thus, we first built a musculoskeletal model comprising the head-arms-trunk (HAT), thighs, shanks, and feet segments of the common chimpanzee, and then, the mechanical relationships of the Hill-type muscle-tendon units (MTUs) in bipedal standing were deduced. Thereafter, the equilibrium constraints were established, and a constrained optimization problem was formulated where the optimization objective was defined. Finally, thousands of simulations of bipedal standing experiments were performed to determine the optimal posture and its corresponding MTU parameters including muscle lengths, muscle activation, and muscle forces. Moreover, to quantify the relationship between each pair of the parameters from all the experimental simulation outcomes, the Pearson correlation analysis was employed. Results: Our results demonstrate that in the pursuit of the optimal bipedal standing posture, the common chimpanzee cannot simultaneously achieve maximum erectness and minimum muscle fatigue of the lower limbs. For uni-articular MTUs, the relationship between muscle activation, relative muscle lengths, together with relative muscle forces, and the corresponding joint angle is generally negatively correlated for extensors and positively correlated for flexors. For bi-articular MTUs, the relationship between muscle activation, coupled with relative muscle forces, and the corresponding joint angles does not show the same pattern as in the uni-articular MTUs. Discussion: The results of this study bridge the gap between skeletal architecture, along with muscle properties, and biomechanical performance of the common chimpanzee during bipedal standing, which enhances existing biomechanical theories and advances the comprehension of bipedal evolution in humans.

Dynamics of Two-Link Musculoskeletal Chains during Fast Movements: Endpoint Force, Axial, and Shear Joint Reaction Forces

This study provides a dynamic model for a two-link musculoskeletal chain controlled by single-joint and two-joint muscles. The chain endpoint force, and the axial and shear components of the joint reaction forces, were expressed analytically as a function of the muscle forces or torques, the chain configuration, and the link angular velocities and accelerations. The model was applied to upper-limb ballistic push movements involving transverse plane shoulder flexion and elbow extension. The numerical simulation highlights that the shoulder flexion and elbow extension angular acceleration at the initial phase of the movement, and the elbow extension angular velocity and acceleration at the later phase of the movement, induce a proportional medial deviation in the endpoint force direction. The forearm angular velocity and acceleration selectively affect the value of the axial and shear components of the shoulder reaction force, depending on the chain configuration. The same goes for the upper arm and elbow. The combined contribution of the elbow extension angular velocity and acceleration may give rise to anterior shear force acting on the humerus and axial forearm traction force as high as 300 N. This information can help optimize the performance and estimate/control of the joint loads in ballistic sport activities and power-oriented resistance exercises.

Effects of geometric individualisation of a human spine model on load sharing: neuro-musculoskeletal simulation reveals significant differences in ligament and muscle contribution

In spine research, two possibilities to generate models exist: generic (population-based) models representing the average human and subject-specific representations of individuals. Despite the increasing interest in subject specificity, individualisation of spine models remains challenging. Neuro-musculoskeletal (NMS) models enable the analysis and prediction of dynamic motions by incorporating active muscles attaching to bones that are connected using articulating joints under the assumption of rigid body dynamics. In this study, we used forward-dynamic simulations to compare a generic NMS multibody model of the thoracolumbar spine including fully articulated vertebrae, detailed musculature, passive ligaments and linear intervertebral disc (IVD) models with an individualised model to assess the contribution of individual biological structures. Individualisation was achieved by integrating skeletal geometry from computed tomography and custom-selected muscle and ligament paths. Both models underwent a gravitational settling process and a forward flexion-to-extension movement. The model-specific load distribution in an equilibrated upright position and local stiffness in the L4/5 functional spinal unit (FSU) is compared. Load sharing between occurring internal forces generated by individual biological structures and their contribution to the FSU stiffness was computed. The main finding of our simulations is an apparent shift in load sharing with individualisation from an equally distributed element contribution of IVD, ligaments and muscles in the generic spine model to a predominant muscle contribution in the individualised model depending on the analysed spine level.

A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculoskeletal Systems

Identification of muscle-tendon force generation properties and muscle activities from physiological measurements, e.g., motion data and raw surface electromyography (sEMG), offers opportunities to construct a subject-specific musculoskeletal (MSK) digital twin system for health condition assessment and motion prediction. While machine learning approaches with capabilities in extracting complex features and patterns from a large amount of data have been applied to motion prediction given sEMG signals, the learned data-driven mapping is black-box and may not satisfy the underlying physics and has reduced generality. In this work, we propose a feature-encoded physics-informed parameter identification neural network (FEPI-PINN) for simultaneous prediction of motion and parameter identification of human MSK systems. In this approach, features of high-dimensional noisy sEMG signals are projected onto a low-dimensional noise-filtered embedding space for the enhancement of forwarding dynamics prediction. This FEPI-PINN model can be trained to relate sEMG signals to joint motion and simultaneously identify key MSK parameters. The numerical examples demonstrate that the proposed framework can effectively identify subject-specific muscle parameters and the trained physics-informed forward-dynamics surrogate yields accurate motion predictions of elbow flexion-extension motion that are in good agreement with the measured joint motion data.

A direct collocation framework for optimal control simulation of pedaling using OpenSim

The direct collocation (DC) method has shown low computational costs in solving optimization problems in human movements, but it has rarely been used for solving optimal control pedaling problems. Thus, the aim of this study was to develop a DC framework for optimal control simulation of human pedaling within the OpenSim modeling environment. A planar bicycle-rider model was developed in OpenSim. The DC method was formulated in MATLAB to solve an optimal control pedaling problem using a data tracking approach. Using the developed DC framework, the optimal control pedaling problem was successfully solved in 24 minutes to ten hours with different objective function weightings and number of nodes from two different initial conditions. The optimal solutions for equal objective function weightings were successful in terms of tracking, with the model simulated pedal angles and pedal forces within ±1 standard deviation of the experimental data. With these weightings, muscle tendon unit (MTU) excitation patterns generally matched with burst timings and shapes observed in the experimental EMG data. Tracking quality and MTU excitation patterns were changed little by selection of node density above 31, and the optimal solution quality was not affected by initial guess used. The proposed DC framework could easily be turned into a predictive simulation with other objective functions such as fastest pedaling rate. This flexible and computationally efficient framework should facilitate the use of optimal control methods to study the biomechanics, energetics, and control of human pedaling.

Lower-limb Joint Torque Prediction using LSTM Neural Networks and Transfer Learning

Estimation of joint torque during movement provides important information in several settings, such as effect of athletes' training or of a medical intervention, or analysis of the remaining muscle strength in a wearer of an assistive device. The ability to estimate joint torque during daily activities using wearable sensors is increasingly relevant in such settings. In this study, lower limb joint torques during ten daily activities were predicted by long short-term memory (LSTM) neural networks and transfer learning. LSTM models were trained with muscle electromyography signals and lower limb joint angles. Hip flexion/extension, hip abduction/adduction, knee flexion/extension and ankle dorsiflexion/plantarflexion torques were predicted. The LSTM models' performance in predicting torque was investigated in both intra-subject and inter-subject scenarios. Each scenario was further divided into intra-task and inter-task tests. We observed that LSTM models could predict lower limb joint torques during various activities accurately with relatively low error (root mean square error ≤ 0.14 Nm/kg, normalized root mean square error ≤8.7%) either through a uniform model or through ten separate models in intra-subject tests. Furthermore, a transfer learning technique was adopted in the inter-task and inter-subject tests to further improve the generalizability of LSTM models by pre-training a model on multiple subjects and/or tasks and transferring the learned knowledge to a target task/subject. Particularly in the inter-subject tests, we could predict joint torques accurately in several movements after training from only a few movements from new subjects.

Exercise-induced piezoelectric stimulation for cartilage regeneration in rabbits

More than 32.5 million American adults suffer from osteoarthritis, and current treatments including pain medicines and anti-inflammatory drugs only alleviate symptoms but do not cure the disease. Here, we have demonstrated that a biodegradable piezoelectric poly(L-lactic acid) (PLLA) nanofiber scaffold under applied force or joint load could act as a battery-less electrical stimulator to promote chondrogenesis and cartilage regeneration. The PLLA scaffold under applied force or joint load generated a controllable piezoelectric charge, which promoted extracellular protein adsorption, facilitated cell migration or recruitment, induced endogenous TGF-β via calcium signaling pathway, and improved chondrogenesis and cartilage regeneration both in vitro and in vivo. Rabbits with critical-sized osteochondral defects receiving the piezoelectric scaffold and exercise treatment experienced hyaline-cartilage regeneration and completely healed cartilage with abundant chondrocytes and type II collagen after 1 to 2 months of exercise (2 to 3 months after surgery including 1 month of recovery before exercise), whereas rabbits treated with nonpiezoelectric scaffold and exercise treatment had unfilled defect and limited healing. The approach of combining biodegradable piezoelectric tissue scaffolds with controlled mechanical activation (via physical exercise) may therefore be useful for the treatment of osteoarthritis and is potentially applicable to regenerating other injured tissues.

Correlating Skeletal Muscle Output Force and Intramuscular Pressure via a 3-Dimensional Finite Element Muscle Model

PURPOSE

The inclusion of muscle pressure in muscle models may have important implications in biomechanics. This notion builds from the known correlation between muscle contractile force and internal pressure. However, this relation is often omitted in numerical models leveraged to study biomechanics. Thus, the purpose of this study was to develop and validate a method of modeling muscles, via finite elements, inclusive of the correlation between muscle contractile force and intramuscular pressure.

METHODS

A MRI-scanned tibialis anterior muscle was modelled via a simple, yet easily scalable, mixed shell and pressure finite element model. Then a validation study was conducted on intramuscular pressure, resulting from applied muscle contractile force, through leveraging special fluid elements type.

RESULTS

The fluid-structure based model and adopted methods exhibited muscle forces and intramuscular pressure that were highly linearly correlated. Indirect validation was achieved with a maximum discrepancy of 7.25%. Furthermore, force-length curves followed a trend similar to documented conventional muscle data during concentric contraction, which added to the model's validity. Mesh, material properties, and tendon stiffness sensitivity studies supported model's robustness.

CONCLUSION

This study has introduced a novel 3-dimensional finite element modelling method that respects the physiological force and intramuscular pressure relationship. Although similar models have been previously explored, their complex physiological representation and time-consuming solvers make their scalability and real-time implementation questionable. Thus, the developed model may address such limitations while improving the realism of volumetric finite element models inclusive of muscle contribution.

Hybrid Predictive Model for Lifting by Integrating Skeletal Motion Prediction with an OpenSim Musculoskeletal Model

OBJECTIVE

In this study, a novel hybrid predictive musculoskeletal model is proposed which has both motion prediction and muscular dynamics assessment capabilities.

METHODS

First, a two-dimensional (2D) skeletal model with 10 degrees of freedom is used to predict a symmetric lifting motion, outputting joint angle profiles, ground reaction forces (GRFs), and center of pressure (COP). These intermediate outputs are input to the scaled musculoskeletal model in OpenSim for muscle activation and joint reaction load analysis. Finally, the experimental validation is carried out.

RESULTS

Static Optimization tool is used to estimate the muscle activation data in OpenSim for the predicted lifting motion. Joint reaction forces of the lumbosacral joint (L5-S1) are generated using the OpenSim Joint Reaction analysis tool. The predicted joint angles, muscle activations, and peak joint reaction forces are compared with experimental data and data from literature to validate the hybrid model.

CONCLUSION

The proposed hybrid model combines the skeletal models rapid motion prediction with OpenSims complex muscular dynamics assessment, and it can serve as a new generic tool for motion prediction and injury analysis in ergonomics and biomechanics.

Prediction of gait trajectories based on the Long Short Term Memory neural networks

The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim of this work was to examine four Long Short Term Memory (LSTM) neural network architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in predicting the future trajectories of lower limb kinematics, i.e. Angular Velocity (AV) and Linear Acceleration (LA). Kinematics data of foot, shank and thigh (LA and AV) were collected from 13 male and 3 female participants (28 ± 4 years old, 1.72 ± 0.07 m in height, 66 ± 10 kg in mass) who walked for 10 minutes at preferred walking speed (4.34 ± 0.43 km.h-1) and at an imposed speed (5km.h-1, 15.4% ± 7.6% faster) on a 0% gradient treadmill. The sliding window technique was adopted for training and testing the LSTM models with total kinematics time-series data of 10,500 strides. Results based on leave-one-out cross validation, suggested that the LSTM autoencoders is the top predictor of the lower limb kinematics trajectories (i.e. up to 0.1s). The normalised mean squared error was evaluated on trajectory predictions at each time-step and it obtained 2.82-5.31% for the LSTM autoencoders. The ability to predict future lower limb motions may have a wide range of applications including the design and control of bionics allowing improved human-machine interface and mitigating the risk of falls and balance loss.

The effect of the use of smartphone while walking on the electromyography activity of the lower extremity in young students

The study aims to determine the effects of smartphone use on the muscle activity of the lower extremity when walking. Twenty-three healthy young students were asked to perform a 10-m walk test between normal walking without using a smartphone and walking while two-handed texting on a smartphone. The electromyography activities of the lower extremity were quantified. To quantitatively assess the cervical flexion range of smartphone users, the cervical flexion angle was measured using a digital goniometer. The study results indicated that the use of a smartphone while walking could lessen muscle activity on the tibialis anterior, gastrocnemius, rectus femoris, gluteus maximus, and gluteus medius than that of normal walking without using a smartphone. The walking speeds were reduced in walking while using a smartphone compared with normal walking without using a smartphone. The cervical flexion angle was greater when walking while using a smartphone compared to that of normal walking without using a smartphone. These results suggest that frequently using a smartphone while walking could be a potential risk for musculoskeletal problems.

Muscle Co-Activation around the Knee during Different Walking Speeds in Healthy Females

The purpose of this study was to examine the changes in co-activation around the knee joint during different walking speeds in healthy females using the co-activation index. Ten healthy females (age: 21.20 ± 7.21 years, height: 164.00 ± 4.00 cm, mass: 60.60 ± 4.99 kg) participated in this study and performed three walking speeds (slow, normal, and fast). A Qualisys 11-camera motion analysis system sampling at a frequency of 200 Hz was synchronized with a Trigno EMG Wireless system operating at a 2000 Hz sampling frequency. A significant decrease in the co-activation index of thigh muscles was observed between the slow and fast, and between the normal and fast, walking speeds during all walking phases. A non-significant difference was observed between the slow and normal walking speeds during most walking phases, except the second double support phase, during which the difference was significant. A negative relationship was found between walking speed and the co-activation index of thigh muscles in all speeds during walking phases: first double support (r = −0.3386, p < 0.001), single support (r = −0.2144, p < 0.01), second double support (r = −0.4949, p < 0.001), and Swing (r = −0.1639, p < 0.05). In conclusion, the results indicated high variability of thigh muscle co-activation in healthy females during the different walking speeds, and a decrease in the co-activation of the thigh muscles with the increase of speed.

12 Degrees of Freedom Muscle Force Driven Fibril-Reinforced Poroviscoelastic Finite Element Model of the Knee Joint

Accurate knowledge of the joint kinematics, kinetics, and soft tissue mechanical responses is essential in the evaluation of musculoskeletal (MS) disorders. Since in vivo measurement of these quantities requires invasive methods, musculoskeletal finite element (MSFE) models are widely used for simulations. There are, however, limitations in the current approaches. Sequentially linked MSFE models benefit from complex MS and FE models; however, MS model's outputs are independent of the FE model calculations. On the other hand, due to the computational burden, embedded (concurrent) MSFE models are limited to simple material models and cannot estimate detailed responses of the soft tissue. Thus, first we developed a MSFE model of the knee with a subject-specific MS model utilizing an embedded 12 degrees of freedom (DoFs) knee joint with elastic cartilages in which included both secondary kinematic and soft tissue deformations in the muscle force estimation (inverse dynamics). Then, a muscle-force-driven FE model with fibril-reinforced poroviscoelastic cartilages and fibril-reinforced poroelastic menisci was used in series to calculate detailed tissue mechanical responses (forward dynamics). Second, to demonstrate that our workflow improves the simulation results, outputs were compared to results from the same FE models which were driven by conventional MS models with a 1 DoF knee, with and without electromyography (EMG) assistance. The FE model driven by both the embedded and the EMG-assisted MS models estimated similar results and consistent with experiments from literature, compared to the results estimated by the FE model driven by the MS model with 1 DoF knee without EMG assistance.

Efficient trajectory optimization for curved running using a 3D musculoskeletal model with implicit dynamics

Trajectory optimization with musculoskeletal models can be used to reconstruct measured movements and to predict changes in movements in response to environmental changes. It enables an exhaustive analysis of joint angles, joint moments, ground reaction forces, and muscle forces, among others. However, its application is still limited to simplified problems in two dimensional space or straight motions. The simulation of movements with directional changes, e.g. curved running, requires detailed three dimensional models which lead to a high-dimensional solution space. We extended a full-body three dimensional musculoskeletal model to be specialized for running with directional changes. Model dynamics were implemented implicitly and trajectory optimization problems were solved with direct collocation to enable efficient computation. Standing, straight running, and curved running were simulated starting from a random initial guess to confirm the capabilities of our model and approach: efficacy, tracking and predictive power. Altogether the simulations required 1 h 17 min and corresponded well to the reference data. The prediction of curved running using straight running as tracking data revealed the necessity of avoiding interpenetration of body segments. In summary, the proposed formulation is able to efficiently predict a new motion task while preserving dynamic consistency. Hence, labor-intensive and thus costly experimental studies could be replaced by simulations for movement analysis and virtual product design.

Prediction of Individual Knee Kinematics From an MRI Representation of the Articular Surfaces

OBJECTIVE

The knowledge of individual joint motion may help to understand the articular physiology and to design better treatments and medical devices. Measurements of in-vivo individual motion are nowadays invasive/ionizing (fluoroscopy) or imprecise (skin markers). We propose a new approach to derive the individual knee natural motion from a three-dimensional representation of articular surfaces.

METHODS

We hypothesize that tissue adaptation shapes articular surfaces to optimize load distribution. Thus, the knee natural motion is obtained as the envelope of tibiofemoral positions and orientations that minimize peak contact pressure, i.e. that maximize joint congruence. We investigated four in-vitro and one in-vivo knees. Articular surfaces were reconstructed from a reference MRI. Natural motion was computed by congruence maximization and results were validated versus experimental data, acquired through bone implanted markers, in-vitro, and single-plane fluoroscopy, in-vivo.

RESULTS

In two cases, one of which in-vivo, maximum mean absolute error stays below 2.2° and 2.7 mm for rotations and translations, respectively. The remaining knees showed differences in joint internal rotation between the reference MRI and experimental motion at 0° flexion, possibly due to some laxity. The same difference is found in the model predictions, which, however, still replicate the individual knee motion.

CONCLUSION

The proposed approach allows the prediction of individual joint motion based on non-ionizing MRI data.

SIGNIFICANCE

This method may help to characterize healthy and, by comparison, pathological knee behavior. Moreover, it may provide an individual reference motion for the personalization of musculoskeletal models, opening the way to their clinical application.

Tutorial Review of Bio-Inspired Approaches to Robotic Manipulation for Space Debris Salvage

We present a comprehensive tutorial review that explores the application of bio-inspired approaches to robot control systems for grappling and manipulating a wide range of space debris targets. Current robot manipulator control systems exploit limited techniques which can be supplemented by additional bio-inspired methods to provide a robust suite of robot manipulation technologies. In doing so, we review bio-inspired control methods because this will be the key to enabling such capabilities. In particular, force feedback control may be supplemented with predictive forward models and software emulation of viscoelastic preflexive joint behaviour. This models human manipulation capabilities as implemented by the cerebellum and muscles/joints respectively. In effect, we are proposing a three-level control strategy based on biomimetic forward models for predictive estimation, traditional feedback control and biomimetic muscle-like preflexes. We place emphasis on bio-inspired forward modelling suggesting that all roads lead to this solution for robust and adaptive manipulator control. This promises robust and adaptive manipulation for complex tasks in salvaging space debris.

Direct Validation of Model-Predicted Muscle Forces in the Cat Hindlimb During Locomotion

Various methods are available for simulating the movement patterns of musculoskeletal systems and determining individual muscle forces, but the results obtained from these methods have not been rigorously validated against experiment. The aim of this study was to compare model predictions of muscle force derived for a cat hindlimb during locomotion against direct measurements of muscle force obtained in vivo. The cat hindlimb was represented as a 5-segment, 13-degrees-of-freedom (DOF), articulated linkage actuated by 25 Hill-type muscle-tendon units (MTUs). Individual muscle forces were determined by combining gait data with two widely used computational methods-static optimization and computed muscle control (CMC)-available in opensim, an open-source musculoskeletal modeling and simulation environment. The forces developed by the soleus, medial gastrocnemius (MG), and tibialis anterior muscles during free locomotion were measured using buckle transducers attached to the tendons. Muscle electromyographic activity and MTU length changes were also measured and compared against the corresponding data predicted by the model. Model-predicted muscle forces, activation levels, and MTU length changes were consistent with the corresponding quantities obtained from experiment. The calculated values of muscle force obtained from static optimization agreed more closely with experiment than those derived from CMC.

How does the CNS control arm reaching movements? Introducing a hierarchical nonlinear predictive control organization based on the idea of muscle synergies

In this study, we introduce a hierarchical and modular computational model to explain how the CNS (Central Nervous System) controls arm reaching movement (ARM) in the frontal plane and under different conditions. The proposed hierarchical organization was established at three levels: 1) motor planning, 2) command production, and 3) motor execution. Since in this work we are not discussing motion learning, no learning procedure was considered in the model. Previous models mainly assume that the motor planning level produces the desired trajectories of the joints and feeds it to the next level to be tracked. In the proposed model, the motion control is described based on a regulatory control policy, that is, the output of the motor planning level is a step function defining the initial and final desired position of the hand. For the command production level, a nonlinear predictive model was developed to explain how the time-invariant muscle synergies (MSs) are recruited. We used the same computational model to explain the arm reaching motion for a combined ARM task. The combined ARM is defined as two successive ARM such that it starts from point A and reaches to point C via point B. To develop the model, kinematic and kinetic data from six subjects were recorded and analyzed during ARM task performance. The subjects used a robotic manipulator while moving their hand in the frontal plane. The EMG data of 15 muscles were also recorded. The MSs used in the model were extracted from the recorded EMG data. The proposed model explains two aspects of the motor control system by a novel computational approach: 1) the CNS reduces the dimension of the control space using the notion of MSs and thereby, avoids immense computational loads; 2) at the level of motor planning, the CNS generates the desired position of the hand at the starting, via and the final points, and this amounts to a regulatory and non-tracking structure.

Strategies of elite Chinese gymnasts in coping with landing impact from backward somersault

This study aimed to investigate how elite Chinese gymnasts manage the landing impact from a backward somersault. Six international-level male gymnasts performed backward somersault tests with a synchronous collection of kinematics (250 Hz), ground reaction forces (1,000 Hz), and surface electromyography (EMG) (2,000 Hz). A 19-segment human model was developed and lower extremity joints torques were calculated by means of a computer simulation. The angles of the lower extremity joints initially extended and then flexed. These angular velocities of extension continued to decrease and the joint torques changed from extensor to flexor within 100 ms before touchdown. The angles of the hips, knees, and ankles flexed rapidly by 12°, 36°, and 29°, respectively, and the angular velocities of flexion, flexor torque, and EMG peaked sharply during the initial impact phase of the landing. The angles of the hips, knees, and ankles flexed at approximately 90°, 100°, and 80°, respectively. The torques were reversed with the extensor torques, showing a relatively high level of muscle activation during the terminal impact phase of the landing. The results showed that the international-level gymnasts first extended their lower extremity joints, then flexed just before touchdown. They continued flexing actively and rapidly in the initial impact phase and then extended to resist the landing impact and maintain body posture during the terminal impact phase of the landing. The information gained from this study could improve our understanding of the landings of elite gymnasts and assist in injury prevention.

Knee Forces During Landing in Men and Women

Sex differences in biomechanics may provide one explanation for the greater incidence of knee injuries in women, but few studies have compared internal forces. In this study, a musculoskeletal model was used to compare male and female, bilateral and unilateral landings based on motion capture and force plate data. Participants were classified as landing medially or laterally loaded based upon the mediolateral load share at the knee (bilateral: p < 0.001, η²=0.452; unilateral: p < 0.001, η² = 0.444). Knee kinematics and ground reaction forces were not different between the two groups (p > 0.05, η² = 0.001 - 0.059), but there were differences in muscular recruitment. Landing strategy did not appear to be dependent on sex. However, for both medially and laterally loaded bilateral landings men had greater gluteal (p = 0.017, η² = 0.085) and hamstrings forces (p < 0.001, η² = 0.183), whereas women had greater quadriceps forces (p = 0.004, η² = 0.116). This study demonstrates an association between muscular recruitment and medially loaded landings. Landing strategy seems to be a function of skill not sex; however, within a particular landing strategy there may be sex differences in muscular activation that contribute to the difference in injury rates.

Multiscale modeling of the neuromuscular system: Coupling neurophysiology and skeletal muscle mechanics

Mathematical models and computer simulations have the great potential to substantially increase our understanding of the biophysical behavior of the neuromuscular system. This, however, requires detailed multiscale, and multiphysics models. Once validated, such models allow systematic in silico investigations that are not necessarily feasible within experiments and, therefore, have the ability to provide valuable insights into the complex interrelations within the healthy system and for pathological conditions. Most of the existing models focus on individual parts of the neuromuscular system and do not consider the neuromuscular system as an integrated physiological system. Hence, the aim of this advanced review is to facilitate the prospective development of detailed biophysical models of the entire neuromuscular system. For this purpose, this review is subdivided into three parts. The first part introduces the key anatomical and physiological aspects of the healthy neuromuscular system necessary for modeling the neuromuscular system. The second part provides an overview on state-of-the-art modeling approaches representing all major components of the neuromuscular system on different time and length scales. Within the last part, a specific multiscale neuromuscular system model is introduced. The integrated system model combines existing models of the motor neuron pool, of the sensory system and of a multiscale model describing the mechanical behavior of skeletal muscles. Since many sub-models are based on strictly biophysical modeling approaches, it closely represents the underlying physiological system and thus could be employed as starting point for further improvements and future developments. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.

Simulated hip abductor strengthening reduces peak joint contact forces in patients with total hip arthroplasty

Lower extremity muscle strength training is a focus of rehabilitation following total hip arthroplasty (THA). Strength of the hip abductor muscle group is a predictor of overall function following THA. The purpose of this study was to investigate the effects of hip abductor strengthening following rehabilitation on joint contact forces (JCFs) in the lower extremity and low back during a high demand step down task. Five THA patients performed lower extremity maximum isometric strength tests and a stair descent task. Patient-specific musculoskeletal models were created in OpenSim and maximum isometric strength parameters were scaled to reproduce measured pre-operative joint torques. A pre-operative forward dynamic simulation of each patient performing the stair descent was constructed using their corresponding patient-specific model to predict JCFs at the ankle, knee, hip, and low back. The hip abductor muscles were strengthened with clinically supported increases (0-30%) above pre-operative values in a probabilistic framework to predict the effects on peak JCFs (99% confidence bounds). Simulated hip abductor strengthening resulted in lower peak JCFs relative to pre-operative for all five patients at the hip (18.9-23.8 ± 16.5%) and knee (20.5-23.8 ± 11.2%). Four of the five patients had reductions at the ankle (7.1-8.5 ± 11.3%) and low back (3.5-7.0 ± 5.3%) with one patient demonstrating no change. The reduction in JCF at the hip joint and at joints other than the hip with hip abductor strengthening demonstrates the dynamic and mechanical interdependencies of the knee, hip and spine that can be targeted in early THA rehabilitation to improve overall patient function.

Activities of ankle muscles during gait analyzed by simulation using the human musculoskeletal model

The purpose of this study was to analyze the activity of ankle muscles during normal gait by simulation method using the human musculoskeletal model. The equipment used in this study was three-dimensional motion capture system and force platform, and OpenSim was used for simulation. Collected data was scaled to Gait2392 that is the human musculoskeletal simulation model using in the OpenSim. Tibialis anterior (TA) worked as a major muscle during gait, producing a higher force than other muscles. Main muscles contributing to propulsion were gastrocnemius medial head (GMH) and soleus (SOL) with their maximum forces appear to be more than 1.5 times the body weight. GMH and SOL showed cooperation for maintaining propulsion around left foot initial contact in the gait cycle. This study has shown a difference between activation and force pattern. The peak-activation of the TA and extensor digitorum longus (EDL) was similarly shown to be around 0.8 in the initial double stance, but the peak-force produced by these muscles in the same period showed a difference with 0.4 Newton/body weight higher in TA than in EDL. We suggest that when assessing muscle contribution to gait, it would be reasonable to consider the force generated because the human movement was generated by the mechanical net force of muscles.

Numerical predictions of hip joint and muscle forces during daily activities: A comparison of musculoskeletal models

Musculoskeletal loading plays an important role in pre-clinical evaluations of hip implants, in particular, bone ingrowth and bone remodelling. Joint force estimation using musculoskeletal models evolved as a viable alternative to in vivo measurement owing to the development of computational resources. This study investigated the efficiencies of four eminent open-source musculoskeletal models in order to determine the model that predicts the most accurate values of hip joint reaction and muscle forces during daily activities. Seven daily living activities of slow walking, normal walking, fast walking, sitting down, standing up, stair down and stair up were simulated in OpenSim using inverse dynamics method. Model predictions of joint kinematics, kinetics and muscle activation patterns were compared with published results. The estimated values of hip joint reaction force were found to corroborate well with in vivo measurements for each activity. Although the estimated values of hip joint reaction force were within a satisfactory range, overestimation of hip joint reaction force (75% BW of measured value) was observed during the late stance phase of walking cycles for all the models. In case of stair up, stair down, standing up and sitting down activities, the error in estimated values of hip joint reaction force were within ~20% BW of the measured value. Based on the results of our study, the London Lower Extremity Model predicted the most accurate value of hip joint reaction force and therefore can be used for applied musculoskeletal loading conditions for numerical investigations on hip implants.

A novel training-free method for real-time prediction of femoral strain

Surrogate methods for rapid calculation of femoral strain are limited by the scope of the training data. We compared a newly developed training-free method based on the superposition principle (Superposition Principle Method, SPM) and popular surrogate methods for calculating femoral strain during activity. Finite-element calculations of femoral strain, muscle, and joint forces for five different activity types were obtained previously. Multi-linear regression, multivariate adaptive regression splines, and Gaussian process were trained for 50, 100, 200, and 300 random samples generated using Latin Hypercube (LH) and Design of Experiment (DOE) sampling. The SPM method used weighted linear combinations of 173 activity-independent finite-element analyses accounting for each muscle and hip contact force. Across the surrogate methods, we found that 200 DOE samples consistently provided low error (RMSE < 100 µε), with model construction time ranging from 3.8 to 63.3 h and prediction time ranging from 6 to 1236 s per activity. The SPM method provided the lowest error (RMSE = 40 µε), the fastest model construction time (3.2 h) and the second fastest prediction time per activity (36 s) after Multi-linear Regression (6 s). The SPM method will enable large numerical studies of femoral strain and will narrow the gap between bone strain prediction and real-time clinical applications.

An Efficient Inverse Dynamics Optimization Formulation for Musculoskeletal Motion Prediction

In this study, an inverse dynamics optimization formulation and solution procedure is developed for musculoskeletal simulations. The proposed method has three main features: high order recursive B-spline interpolation, partition of unity, and inverse dynamics formulation. First, joint angle and muscle force profiles are represented by recursive B-splines. The formula for high order recursive B-spline derivatives are derived for state variables calculation. Second, partition of unity is used to handle the multi-contact indeterminacy between human and environment during the motion. The global forces and moments are distributed to each contacting point through the corresponding partition ratio. Third, joint torques are inversely calculated from equations of motion based on state variables and contacts to avoid numerical integration of equations of motion. Therefore, the design variables for the optimization problem are joint angle control points, muscle force control points, knot vector, and partition ratios for contacting points. The sum of muscle stress/activity squared is minimized as the cost function. The constraints are imposed for human physical constraints and task-based constraints. The proposed formulation is demonstrated by simulating a trajectory planning problem of a planar musculoskeletal arm with six muscles. In addition, the gait motion of a two-dimensional musculoskeletal model with sixteen muscles is also optimized by using the approach developed in this paper. The gait optimal solution is obtained in about 1 minute CPU time. The predicted kinematics, kinetics, and muscle forces have general trends that are similar to those reported in the literature.

A computationally efficient strategy to estimate muscle forces in a finite element musculoskeletal model of the lower limb

Concurrent multiscale simulation strategies are required in computational biomechanics to study the interdependence between body scales. However, detailed finite element models rarely include muscle recruitment due to the computational burden of both the finite element method and the optimization strategies widely used to estimate muscle forces. The aim of this study was twofold: first, to develop a computationally efficient muscle force prediction strategy based on proportional-integral-derivative (PID) controllers to track gait and chair rise experimental joint motion with a finite element musculoskeletal model of the lower limb, including a deformable knee representation with 12 degrees of freedom; and, second, to demonstrate that the inclusion of joint-level deformability affects muscle force estimation by using two different knee models and comparing muscle forces between the two solutions. The PID control strategy tracked experimental hip, knee, and ankle flexion/extension with root mean square errors below 1°, and estimated muscle, contact and ligament forces in good agreement with previous results and electromyography signals. Differences up to 11% and 20% in the vasti and biceps femoris forces, respectively, were observed between the two knee models, which might be attributed to a combination of differing joint contact geometry, ligament behavior, joint kinematics, and muscle moment arms. The tracking strategy developed in this study addressed the inevitable tradeoff between computational cost and model detail in musculoskeletal simulations and can be used with finite element musculoskeletal models to efficiently estimate the interdependence between muscle forces and tissue deformation.

Morphofunctional muscle changes influence on foot stability in multiple sclerosis during gait prediction: The rehabilitation potential

BACKGROUND

Multiple sclerosis patients may suffer muscle changes that involve gait disorders of the kinetic and kinematic parameters also their gait may be clinically symmetrical or asymmetrical.

OBJECTIVE

The aim of this study is to analyze how the muscle change, could affect the biomechanical parameters of foot stability during the gait, by disturb the motor control.

METHODS

The study group consisted of 13 patients diagnosed with multiple sclerosis, presenting clinically detectable abnormal gait. The biomechanical evaluation included the foot axes and angles -external and internal rotation; the foot angle deviation from the gait direction; the subtalar angle.

RESULTS

The values of the foot angle were between -10.74∘ to 26.38∘ for the left foot and between -11.16∘ to 30.04∘ for the right foot. The foot axis angle is the axis of the foot in relation to the gait direction, and the subtalar angle is in relation to the vertical axis of the foot. The rotation of the right foot into pronation during the initial contact phase was followed by supination in the semi-support phase, to return to the neutral position during the propulsion phase, which meant being in free zone of minimal risk.

CONCLUSIONS

Biomechanical analysis of the foot angle and of subtalar angle in the patients with multiple sclerosis allows us to objectify the existence of a right-left asymmetry, the behavior ankle-foot during the gait. At the same time this evolution is closely correlated with the contact surface that tends to increase, which means involving the reflex mechanisms that place the foot in the zone of minimum risk and assure the stability of the body.

Bipedal robotic walking control derived from analysis of human locomotion

This paper proposes the design of a bipedal robotic controller where the function between the sensory input and motor output is treated as a black box derived from human data. In order to achieve this, we investigated the causal relationship between ground contact information from the feet and leg muscle activity n human walking and calculated filter functions which transform sensory signals to motor actions. A minimal, nonlinear, and robust control system was created and subsequently analysed by applying it to our bipedal robot RunBot III without any central pattern generators or precise trajectory control. The results demonstrate that our controller can generate stable robotic walking. This indicates that complex locomotion patterns can result from a simple model based on reflexes and supports the premise that human-derived control strategies have potential applications in robotics or assistive devices.

Towards Subject-Specific Strength Training Design through Predictive Use of Musculoskeletal Models

Lower extremity dysfunction is often associated with hip muscle strength deficiencies. Detailed knowledge of the muscle forces generated in the hip under specific external loading conditions enables specific structures to be trained. The aim of this study was to find the most effective movement type and loading direction to enable the training of specific parts of the hip muscles using a standing posture and a pulley system. In a novel approach to release the predictive power of musculoskeletal modelling techniques based on inverse dynamics, flexion/extension and ab-/adduction movements were virtually created. To demonstrate the effectiveness of this approach, three hip orientations and an external loading force that was systematically rotated around the body were simulated using a state-of-the art OpenSim model in order to establish ideal designs for training of the anterior and posterior parts of the M. gluteus medius (GM). The external force direction as well as the hip orientation greatly influenced the muscle forces in the different parts of the GM. No setting was found for simultaneous training of the anterior and posterior parts with a muscle force higher than 50% of the maximum. Importantly, this study has demonstrated the use of musculoskeletal models as an approach to predict muscle force variations for different strength and rehabilitation exercise variations.

Prediction of human gait trajectories during the SSP using a neuromusculoskeletal modeling: A challenge for parametric optimization

The parametric optimization techniques have been widely employed to predict human gait trajectories; however, their applications to reveal the other aspects of gait are questionable. The aim of this study is to investigate whether or not the gait prediction model is able to justify the movement trajectories for the higher average velocities. A planar, seven-segment model with sixteen muscle groups was used to represent human neuro-musculoskeletal dynamics. At first, the joint angles, ground reaction forces (GRFs) and muscle activations were predicted and validated for normal average velocity (1.55 m/s) in the single support phase (SSP) by minimizing energy expenditure, which is subject to the non-linear constraints of the gait. The unconstrained system dynamics of extended inverse dynamics (USDEID) approach was used to estimate muscle activations. Then by scaling time and applying the same procedure, the movement trajectories were predicted for higher average velocities (from 2.07 m/s to 4.07 m/s) and compared to the pattern of movement with fast walking speed. The comparison indicated a high level of compatibility between the experimental and predicted results, except for the vertical position of the center of gravity (COG). It was concluded that the gait prediction model can be effectively used to predict gait trajectories for higher average velocities.

Measuring relative positions and orientations of the tibia with respect to the femur using one-channel 3D-tracked A-mode ultrasound tracking system: A cadaveric study

The purpose of this study is to investigate the technical feasibility of measuring relative positions and orientations of the tibia with respect to the femur in an in-vitro experiment by using a 3D-tracked A-mode ultrasound system and to determine its accuracy of angular and translational measurements. As A-mode ultrasound is capable of detecting bone surface through soft tissue in a non-invasive manner, the combination of a single A-mode ultrasound transducer with an optical motion tracking system provides the possibility for digitizing the 3D locations of bony points at different anatomical regions on the thigh and the shank. After measuring bony points over a large area of both the femur and tibia, the bone models of the femur and tibia that were segmented from CT or MRI images were registered to the corresponding bony points. Then the relative position of the tibia with respect to the femur could be obtained and the angular and translational components could also be measured. A cadaveric experiment was conducted to assess its accuracy compared to the reference measurement obtained by optical markers fixed to intra-cortical bone pins placed in the femur and tibia. The results showed that the ultrasound system could achieve 0.49 ± 0.83°, 0.85 ± 1.86° and 1.85 ± 2.78° (mean ± standard deviation) errors for Flexion-Extension, Adduction-Abduction and External-Internal rotations, respectively, and -2.22 ± 3.62 mm, -2.80 ± 2.35 mm and -1.44 ± 2.90 mm errors for Anterior-Posterior, Proximal-Distal and Lateral-Medial translations, respectively. It was concluded that this technique is feasible and facilitates the integration of arrays of A-mode ultrasound transducers with an optical motion tracking system for non-invasive dynamic tibiofemoral kinematics measurement.

A Dynamic Simulation of Musculoskeletal Function in the Mouse Hindlimb During Trotting Locomotion

Mice are often used as animal models of various human neuromuscular diseases, and analysis of these models often requires detailed gait analysis. However, little is known of the dynamics of the mouse musculoskeletal system during locomotion. In this study, we used computer optimization procedures to create a simulation of trotting in a mouse, using a previously developed mouse hindlimb musculoskeletal model in conjunction with new experimental data, allowing muscle forces, activation patterns, and levels of mechanical work to be estimated. Analyzing musculotendon unit (MTU) mechanical work throughout the stride allowed a deeper understanding of their respective functions, with the rectus femoris MTU dominating the generation of positive and negative mechanical work during the swing and stance phases. This analysis also tested previous functional inferences of the mouse hindlimb made from anatomical data alone, such as the existence of a proximo-distal gradient of muscle function, thought to reflect adaptations for energy-efficient locomotion. The results do not strongly support the presence of this gradient within the mouse musculoskeletal system, particularly given relatively high negative net work output from the ankle plantarflexor MTUs, although more detailed simulations could test this further. This modeling analysis lays a foundation for future studies of the control of vertebrate movement through the development of neuromechanical simulations.

Predictive Simulations of Neuromuscular Coordination and Joint-Contact Loading in Human Gait

We implemented direct collocation on a full-body neuromusculoskeletal model to calculate muscle forces, ground reaction forces and knee contact loading simultaneously for one cycle of human gait. A data-tracking collocation problem was solved for walking at the normal speed to establish the practicality of incorporating a 3D model of articular contact and a model of foot-ground interaction explicitly in a dynamic optimization simulation. The data-tracking solution then was used as an initial guess to solve predictive collocation problems, where novel patterns of movement were generated for walking at slow and fast speeds, independent of experimental data. The data-tracking solutions accurately reproduced joint motion, ground forces and knee contact loads measured for two total knee arthroplasty patients walking at their preferred speeds. RMS errors in joint kinematics were < 2.0° for rotations and < 0.3 cm for translations while errors in the model-computed ground-reaction and knee-contact forces were < 0.07 BW and < 0.4 BW, respectively. The predictive solutions were also consistent with joint kinematics, ground forces, knee contact loads and muscle activation patterns measured for slow and fast walking. The results demonstrate the feasibility of performing computationally-efficient, predictive, dynamic optimization simulations of movement using full-body, muscle-actuated models with realistic representations of joint function.

Advancing quantitative techniques to improve understanding of the skeletal structure-function relationship

Although all functional movement arises from the interplay between the neurological, skeletal, and muscular systems, it is the skeletal system that forms the basic framework for functional movement. Central to understanding human neuromuscular development, along with the genesis of musculoskeletal pathologies, is quantifying how the human skeletal system adapts and mal-adapts to its mechanical environment. Advancing this understanding is hampered by an inability to directly and non-invasively measure in vivo strains, stresses, and forces on bone. Thus, we traditionally have turned to animal models to garner such information. These models enable direct in vivo measures that are not available for human subjects, providing information in regards to both skeletal adaptation and the interplay between the skeletal and muscular systems. Recently, there has been an explosion of new imaging and modeling techniques providing non-invasive, in vivo measures and estimates of skeletal form and function that have long been missing. Combining multiple modalities and techniques has proven to be one of our most valuable resources in enhancing our understanding of the form-function relationship of the human skeletal, muscular, and neurological systems. Thus, to continue advancing our knowledge of the structural-functional relationship, validation of current tools is needed, while development is required to limit the deficiencies in these tools and develop new ones.

The interaction between the vastus medialis and vastus intermedius and its influence on the extensor apparatus of the knee joint

PURPOSE

Although the vastus medialis (VM) is closely associated with the vastus intermedius (VI), there is a lack of data regarding their functional relationship. The purpose of this study was to investigate the anatomical interaction between the VM and VI with regard to their origins, insertions, innervation and function within the extensor apparatus of the knee joint.

METHODS

Eighteen human cadaveric lower limbs were investigated using macro-dissection techniques. Six limbs were cut transversely in the middle third of the thigh. The mode of origin, insertion and nerve supply of the extensor apparatus of the knee joint were studied. The architecture of the VM and VI was examined in detail, as was their anatomical interaction and connective tissue linkage to the adjacent anatomical structures.

RESULTS

The VM originated medially from a broad hammock-like structure. The attachment site of the VM always spanned over a long distance between: (1) patella, (2) rectus femoris tendon and (3) aponeurosis of the VI, with the insertion into the VI being the largest. VM units were inserted twice-once on the anterior and once on the posterior side of the VI. The VI consists of a complex multi-layered structure. The layers of the medial VI aponeurosis fused with the aponeuroses of the tensor vastus intermedius and vastus lateralis. Together, they form the two-layered intermediate layer of the quadriceps tendon. The VM and medial parts of the VI were innervated by the same medial division of the femoral nerve.

CONCLUSION

The VM consists of multiple muscle units inserting into the entire VI. Together, they build a potential functional muscular complex. Therefore, the VM acts as an indirect extensor of the knee joint regulating and adjusting the length of the extensor apparatus throughout the entire range of motion. It is of clinical importance that, besides the VM, substantial parts of the VI directly contribute to the medial pull on the patella and help to maintain medial tracking of the patella during knee extension. The interaction between the VM and VI, with responsibility for the extension of the knee joint and influence on the patellofemoral function, leads readily to an understanding of common clinical problems found at the knee joint as it attempts to meet contradictory demands for both mobility and stability. Surgery or trauma in the anteromedial aspect of the quadriceps muscle group might alter a delicate interplay between the VM and VI. This would affect the extensor apparatus as a whole.

Errors Associated With Utilizing Prescribed Scapular Kinematics to Estimate Unconstrained, Natural Upper Extremity Motion in Musculoskeletal Modeling

Musculoskeletal modeling is capable of estimating physiological parameters that cannot be directly measured, however, the validity of the results must be assessed. Several models utilize a scapular rhythm to prescribe kinematics, yet it is unknown how well they replicate natural scapular motion. This study evaluated kinematic errors associated with a model that employs a scapular rhythm using 2 shoulder movements: abduction and forward reach. Two versions of the model were tested: the original MoBL ARMS model that utilizes a scapular rhythm, and a modified MoBL ARMS model that permits unconstrained scapular motion. Model estimates were compared against scapulothoracic kinematics directly measured from motion capture. Three-dimensional scapulothoracic resultant angle errors associated with the rhythm model were greater than 10° for abduction (mean: 16.4°, max: 22.4°) and forward reach (mean: 11.1°, max: 16.5°). Errors generally increased with humerothoracic elevation with all subjects reporting greater than 10° differences at elevations greater than 45°. Errors associated with the unconstrained model were less than 10°. Consequently, use of the original MoBL ARMS model is cautioned for applications requiring precise scapulothoracic kinematics. These findings can help determine which research questions are suitable for investigation with these models and assist in contextualizing model results.