Adjustment of muscle mechanics model parameters to simulate dynamic contractions in older adults

Experiment-guided tuning of muscle-tendon parameters to estimate muscle fiber lengths and passive forces

The workflow to simulate motion with recorded data usually starts with selecting a generic musculoskeletal model and scaling it to represent subject-specific characteristics. Simulating muscle dynamics with muscle-tendon parameters computed from existing scaling methods in literature, however, yields some inconsistencies compared to measurable outcomes. For instance, simulating fiber lengths and muscle excitations during walking with linearly scaled parameters does not resemble established patterns in the literature. This study presents a tool that leverages reported in vivo experimental observations to tune muscle-tendon parameters and evaluates their influence in estimating muscle excitations and metabolic costs during walking. From a scaled generic musculoskeletal model, we tuned optimal fiber length, tendon slack length, and tendon stiffness to match reported fiber lengths from ultrasound imaging and muscle passive force-length relationships to match reported in vivo joint moment-angle relationships. With tuned parameters, muscle contracted more isometrically, and soleus's operating range was better estimated than with linearly scaled parameters. Also, with tuned parameters, on/off timing of nearly all muscles' excitations in the model agreed with reported electromyographic signals, and metabolic rate trajectories varied significantly throughout the gait cycle compared to linearly scaled parameters. Our tool, freely available online, can customize muscle-tendon parameters easily and be adapted to incorporate more experimental data.

Exoskeletons vs. exosuits: A comparative analysis using biological-based computer simulation

BACKGROUND

Interest in the design of gait assistance devices has experienced significant growth in recent years. Among various uses of assistive devices, those aimed at supporting the elderly have gained importance due to the rising population of this age group.

METHODS

This study aims to compare the efficacy of two types of assistive devices through musculoskeletal simulations. One case is an ideal device, simulating the motor actuation as it would be in a rigid exoskeleton, and, cable-assisted devices, simulating the assistance of an exosuit. The simulations were based on data obtained from 9 subjects. OpenSim, an open-source software, was employed to conduct the simulations.

RESULTS

Our findings indicate that the cable-assisted device outperforms the traditional exoskeleton by achieving a more significant reduction in the metabolic cost with relatively lower assistance power.

CONCLUSION

Cable-assisted gait assistance devices have shown comparable results to traditional exoskeletons, with the added advantage of improved performance through reduced power requirements.

Experiment-free exoskeleton assistance via learning in simulation

Exoskeletons have enormous potential to improve human locomotive performance1-3. However, their development and broad dissemination are limited by the requirement for lengthy human tests and handcrafted control laws2. Here we show an experiment-free method to learn a versatile control policy in simulation. Our learning-in-simulation framework leverages dynamics-aware musculoskeletal and exoskeleton models and data-driven reinforcement learning to bridge the gap between simulation and reality without human experiments. The learned controller is deployed on a custom hip exoskeleton that automatically generates assistance across different activities with reduced metabolic rates by 24.3%, 13.1% and 15.4% for walking, running and stair climbing, respectively. Our framework may offer a generalizable and scalable strategy for the rapid development and widespread adoption of a variety of assistive robots for both able-bodied and mobility-impaired individuals.

Development of a musculoskeletal shoulder model considering anatomic joint structures and soft-tissue deformation for dynamic simulation

The shoulder joint has a high degree of freedom and an extremely complex and unstable kinematic mechanism. Coordinated contraction of the rotator cuff muscles that stop around the humeral head and the deltoid muscles and the extensibility of soft tissues, such as the joint capsule, labrum, and ligaments, contribute to shoulder-joint stability. Understanding the mechanics of shoulder-joint movement, including soft-tissue characteristics, is important for disease prevention and the development of a device for disease treatment. This study aimed to create a musculoskeletal shoulder model to represent the realistic behavior of joint movement and soft-tissue deformation as a dynamic simulation using a rigid-body model for bones and a soft-body model for soft tissues via a spring-damper-mass system. To reproduce the muscle-contraction properties of organisms, we used a muscle-expansion representation and Hill's mechanical muscle model. Shoulder motion, including the movement of the center of rotation in joints, was reproduced, and the strain in the joint capsule during dynamic shoulder movement was quantified. Furthermore, we investigated narrowing of the acromiohumeral distance in several situations to induce tissue damage due to rotator cuff impingement at the anterior-subacromial border during shoulder abduction. Given that the model can analyze exercises under disease conditions, such as muscle and tendon injuries and impingement syndrome, the proposed model is expected to help elucidate disease mechanisms and develop treatment guidelines.

Finite Element Model of the Shoulder with Active Rotator Cuff Muscles: Application to Wheelchair Propulsion

The rotator cuff is prone to injury, remarkably so for manual wheelchair users. To understand its pathomechanisms, finite element models incorporating three-dimensional activated muscles are needed to predict soft tissue strains during given tasks. This study aimed to develop such a model to understand pathomechanisms associated with wheelchair propulsion. We developed an active muscle model associating a passive fiber-reinforced isotropic matrix with an activation law linking calcium ion concentration to tissue tension. This model was first evaluated against known physiological muscle behavior; then used to activate the rotator cuff during a wheelchair propulsion cycle. Here, experimental kinematics and electromyography data was used to drive a shoulder finite element model. Finally, we evaluated the importance of muscle activation by comparing the results of activated and non-activated rotator cuff muscles during both propulsion and isometric contractions. Qualitatively, the muscle constitutive law reasonably reproduced the classical Hill model force-length curve and the behavior of a transversally loaded muscle. During wheelchair propulsion, the deformation and fiber stretch of the supraspinatus muscle-tendon unit pointed towards the possibility for this tendon to develop tendinosis due to the multiaxial loading imposed by the kinematics of propulsion. Finally, differences in local stretch and positions of the lines of action between activated and non-activated models were only observed at activation levels higher than 30%. Our novel finite element model with active muscles is a promising tool for understanding the pathomechanisms of the rotator cuff for various dynamic tasks, especially those with high muscle activation levels.

Muscle Morphology Does Not Solely Determine Knee Flexion Weakness After Anterior Cruciate Ligament Reconstruction with a Semitendinosus Tendon Graft: A Combined Experimental and Computational Modeling Study

The distal semitendinosus tendon is commonly harvested for anterior cruciate ligament reconstruction, inducing substantial morbidity at the knee. The aim of this study was to probe how morphological changes of the semitendinosus muscle after harvest of its distal tendon for anterior cruciate ligament reconstruction affects knee flexion strength and whether the knee flexor synergists can compensate for the knee flexion weakness. Ten participants 8-18 months after anterior cruciate ligament reconstruction with an ipsilateral distal semitendinosus tendon autograft performed isometric knee flexion strength testing (15°, 45°, 60°, and 90°; 0° = knee extension) positioned prone on an isokinetic dynamometer. Morphological parameters extracted from magnetic resonance images were used to inform a musculoskeletal model. Knee flexion moments estimated by the model were then compared with those measured experimentally at each knee angle position. A statistically significant between-leg difference in experimentally-measured maximal isometric strength was found at 60° and 90°, but not 15° or 45°, of knee flexion. The musculoskeletal model matched the between-leg differences observed in experimental knee flexion moments at 15° and 45° but did not well estimate between-leg differences with a more flexed knee, particularly at 90°. Further, the knee flexor synergists could not physiologically compensate for weakness in deep knee flexion. These results suggest additional factors other than knee flexor muscle morphology play a role in knee flexion weakness following anterior cruciate ligament reconstruction with a distal semitendinosus tendon graft and thus more work at neural and microscopic levels is required for informing treatment and rehabilitation in this demographic.

Assessment and modelling of the activation-dependent shift in optimal length of the human soleus muscle in vivo

Previous in vitro and in situ studies have reported a shift in optimal muscle fibre length for force generation (L0) towards longer length at decreasing activation levels (also referred to as length-dependent activation), yet the relevance for in vivo human muscle contractions with a variable activation pattern remains largely unclear. By a combination of dynamometry, ultrasound and electromyography (EMG), we experimentally obtained muscle force-fascicle length curves of the human soleus at 100%, 60% and 30% EMGmax levels from 15 participants aiming to investigate activation-dependent shifts in L0 in vivo. The results showed a significant increase in L0 of 6.5 ± 6.0% from 100% to 60% EMGmax and of 9.1 ± 7.2% from 100% to 30% EMGmax (both P < 0.001), respectively, providing evidence of a moderate in vivo activation dependence of the soleus force-length relationship. Based on the experimental results, an approximation model of an activation-dependent force-length relationship was defined for each individual separately and for the collective data of all participants, both with sufficiently high accuracy (R2 of 0.899 ± 0.056 and R2 = 0.858). This individual approximation approach and the general approximation model outcome are freely accessible and may be used to integrate activation-dependent shifts in L0 in experimental and musculoskeletal modelling studies to improve muscle force predictions. KEY POINTS: The phenomenon of the activation-dependent shift in optimal muscle fibre length for force generation (length-dependent activation) is poorly understood for human muscle in vivo dynamic contractions. We experimentally observed a moderate shift in optimal fascicle length towards longer length at decreasing electromyographic activity levels for the human soleus muscle in vivo. Based on the experimental results, we developed a freely accessible approximation model that allows the consideration of activation-dependent shifts in optimal length in future experimental and musculoskeletal modelling studies to improve muscle force predictions.

Contribution of Soft Tissue Passive Forces in Thumb Carpometacarpal Joint Distraction

Thumb carpometacarpal joint space changes when the surrounding soft tissues including the capsule, ligaments, and tendons are stretched or pulled away. When at rest, joint forces originate from passive contraction of muscles and the involvement of joint capsule and ligaments. Previous biomechanical models of hand and finger joints have mostly focused on the assessment of joint properties when muscles were active. This study aims to present an experimental-numerical biomechanical model of thumb carpometacarpal joint to explore the contribution of tendons, ligaments, and other soft tissues in the passive forces during distraction. Five fresh cadaveric specimens were tested using a distractor device to measure the applied forces upon gradual distraction of the intact joint. The subsequent step involved inserting a minuscule sensor into the joint capsule through a small incision, while preserving the integrity of tendons and ligaments, in order to accurately measure the fundamental intra-articular forces. A numerical model was also used to calculate the passive forces of tendons and ligaments. Before the separation of bones, the forces exerted by tendons and ligaments were relatively small compared to the capsule force, which accounted for approximately 92% of the total applied force. Contribution of tendons and ligaments, however, increased by further distraction. The passive force contribution by tendons at 2-mm distraction was determined less than 11%, whereas it reached up to 74% for the ligaments. The present study demonstrated that the ligament-capsule complex plays significant contribution in passive forces of thumb carpometacarpal joint during distraction.

An OpenSim-Based Closed-Loop Biomechanical Wrist Model for Subject-Specific Pathological Tremor Simulation

OBJECTIVE

A pathological tremor (PT) is an involuntary rhythmic movement of varying frequency and amplitude that affects voluntary motion, thus compromising individuals' independence. A comprehensive model incorporating PT's physiological and biomechanical aspects can enhance our understanding of the disorder and provide valuable insights for therapeutic approaches. This study aims to build a biomechanical model of pathological tremors using OpenSim's realistic musculoskeletal representation of the human wrist with two degrees of freedom.

METHODS

We implemented a Matlab/OpenSim interface for a forward dynamics simulation, which allows for the modeling, simulation, and design of a physiological H∞ closed-loop control. This system replicates pathological tremors similar to those observed in patients when their arm is extended forward, the wrist is pronated, and the hand is subject to gravity forces. The model was individually tuned to five subjects (four Parkinson's disease patients and one diagnosed with essential tremor), each exhibiting distinct tremor characteristics measured by an inertial sensor and surface EMG electrodes. Simulation agreement with the experiments for EMGs, central frequency, joint angles, and angular velocities were evaluated by Jensen-Shannon divergence, histogram centroid error, and histogram intersection.

RESULTS

The model emulated individual tremor statistical characteristics, including muscle activations, frequency, variability, and wrist kinematics, with greater accuracy for the four Parkinson's patients than the essential tremor.

CONCLUSION

The proposed model replicated the main statistical features of subject-specific wrist tremor kinematics.

SIGNIFICANCE

Our methodology may facilitate the design of patient-specific rehabilitation devices for tremor suppression, such as neural prostheses and electromechanical orthoses.

Scalable musculoskeletal model for dynamic simulations of upper body movement

A musculoskeletal (MSK) model is a valuable tool for assessing complex biomechanical problems, estimating joint torques during motion, optimizing motion in sports, and designing exoskeletons and prostheses. This study proposes an open-source upper body MSK model that supports biomechanical analysis of human motion. The MSK model of the upper body consists of 8 body segments (torso, head, left/right upper arm, left/right forearm, and left/right hand). The model has 20 degrees of freedom (DoFs) and 40 muscle torque generators (MTGs), which are constructed using experimental data. The model is adjustable for different anthropometric measurements and subject body characteristics: sex, age, body mass, height, dominant side, and physical activity. Joint limits are modeled using experimental dynamometer data within the proposed multi-DoF MTG model. The model equations are verified by simulating the joint range of motion (ROM) and torque; all simulation results have a good agreement with previously published research.

Scalable musculoskeletal model for dynamic simulations of lower body movement

A musculoskeletal (MSK) model is an important tool for analysing human motions, calculating joint torques during movement, enhancing sports activity, and developing exoskeletons and prostheses. To enable biomechanical investigation of human motion, this work presents an open-source lower body MSK model. The MSK model of the lower body consists of 7 body segments (pelvis, left/right thigh, left/right leg, and left/right foot). The model has 20 degrees of freedom (DoFs) and 28 muscle torque generators (MTGs), which are developed from experimental data. The model can be modified for different anthropometric measurements and subject body characteristics, including sex, age, body mass, height, physical activity, and skin temperature. The model is validated by simulating the torque within the range of motion (ROM) of isolated movements; all simulation findings exhibit a good level of agreement with the literature.

SpasticSim: a synthetic data generation method for upper limb spasticity modelling in neurorehabilitation

In neurorehabilitation, assessment of functional problems is essential to define optimal rehabilitation treatments. Usually, this assessment process requires distinguishing between impaired and non-impaired behavior of limbs. One of the common muscle motor disorders affecting limbs is spasticity, which is complicated to quantify objectively due to the complex nature of motor control. Thus, the lack of heterogeneous samples of patients constituting an acceptable amount of data is an obstacle which is relevant to understanding the behavior of spasticity and, consequently, quantifying it. In this article, we use the 3D creation suite Blender combined with the MBLab add-on to generate synthetic samples of human body models, aiming to be as sufficiently representative as possible to real human samples. Exporting these samples to OpenSim and performing four specific upper limb movements, we analyze the muscle behavior by simulating the six degrees of spasticity contemplated by the Modified Ashworth Scale (MAS). The complete dataset of patients and movements is open-source and available for future research. This approach advocates the potential to generate synthetic data for testing and validating musculoskeletal models.

The gait1415+2 OpenSim musculoskeletal model of transfemoral amputees with a generic bone-anchored prosthesis

This short communication presents the gait1415+2 musculoskeletal model, that has been developed in OpenSim to describe the lower-extremity of a human subject with transfemoral amputation wearing a generic lower-limb bone-anchored prosthesis. The model has fourteen degrees of freedom, governed by fifteen musculotendon units (placed at the contralateral and residual limbs) and two generic actuators (one placed at the knee joint and one at the ankle joint of the prosthetic leg). Even though the model is a simplified abstraction, it is capable of generating a human-like walking gait and, specifically, it is capable of reproducing both the kinematics and the dynamics of a person with transfemoral amputation wearing a bone-anchored prosthesis during normal level-ground walking. The model is released as support material to this short communication with the final goal of providing the scientific community with a tool for performing forward and inverse dynamics simulations, and for developing computationally-demanding control schemes based on artificial intelligence methods for lower-limb prostheses.

Higher coactivations of lower limb muscles increase stability during walking on slippery ground in forward dynamics musculoskeletal simulation

The energy efficiency theory of human bipedal locomotion has been widely accepted as a neuro-musculoskeletal control method. However, coactivation of agonist and antagonist muscles in the lower limb has been observed during various limb movements, including walking. The emergence of this coactivation cannot be explained solely by the energy efficiency theory and remains a subject of debate. To shed light on this, we investigated the role of muscle coactivations in walking stability using a forward dynamics musculoskeletal simulation combined with neural-network-based gait controllers. Our study revealed that a gait controller with minimal muscle activations had a high probability of falls under challenging gait conditions such as slippery ground and uneven terrain. Lower limb muscle coactivations emerged in the process of gait controller training on slippery ground. Controllers with physiological coactivation levels demonstrated a significantly reduced probability of falls. Our results suggest that achieving stable walking requires muscle coactivations beyond the minimal level of muscle energy. This study implies that coactivations likely emerge to maintain gait stability under challenging conditions, and both coactivation and energy optimization of lower limb muscles should be considered when exploring the foundational control mechanisms of human walking.

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.

Investigating the roles of reflexes and central pattern generators in the control and modulation of human locomotion using a physiologically plausible neuromechanical model

Objective.Studying the neural components regulating movement in human locomotion is obstructed by the inability to perform invasive experimental recording in the human neural circuits. Neuromechanical simulations can provide insights by modeling the locomotor circuits. Past neuromechanical models proposed control of locomotion either driven by central pattern generators (CPGs) with simple sensory commands or by a purely reflex-based network regulated by state-machine mechanisms, which activate and deactivate reflexes depending on the detected gait cycle phases. However, the physiological interpretation of these state machines remains unclear. Here, we present a physiologically plausible model to investigate spinal control and modulation of human locomotion.Approach.We propose a bio-inspired controller composed of two coupled CPGs that produce the rhythm and pattern, and a reflex-based network simulating low-level reflex pathways and Renshaw cells. This reflex network is based on leaky-integration neurons, and the whole system does not rely on changing reflex gains according to the gait cycle state. The musculoskeletal model is composed of a skeletal structure and nine muscles per leg generating movement in sagittal plane.Main results.Optimizing the open parameters for effort minimization and stability, human kinematics and muscle activation naturally emerged. Furthermore, when CPGs were not activated, periodic motion could not be achieved through optimization, suggesting the necessity of this component to generate rhythmic behavior without a state machine mechanism regulating reflex activation. The controller could reproduce ranges of speeds from 0.3 to 1.9 m s-1. The results showed that the net influence of feedback on motoneurons (MNs) during perturbed locomotion is predominantly inhibitory and that the CPGs provide the timing of MNs' activation by exciting or inhibiting muscles in specific gait phases.Significance.The proposed bio-inspired controller could contribute to our understanding of locomotor circuits of the intact spinal cord and could be used to study neuromotor disorders.

A spinal cord neuroprosthesis for locomotor deficits due to Parkinson's disease

People with late-stage Parkinson's disease (PD) often suffer from debilitating locomotor deficits that are resistant to currently available therapies. To alleviate these deficits, we developed a neuroprosthesis operating in closed loop that targets the dorsal root entry zones innervating lumbosacral segments to reproduce the natural spatiotemporal activation of the lumbosacral spinal cord during walking. We first developed this neuroprosthesis in a non-human primate model that replicates locomotor deficits due to PD. This neuroprosthesis not only alleviated locomotor deficits but also restored skilled walking in this model. We then implanted the neuroprosthesis in a 62-year-old male with a 30-year history of PD who presented with severe gait impairments and frequent falls that were medically refractory to currently available therapies. We found that the neuroprosthesis interacted synergistically with deep brain stimulation of the subthalamic nucleus and dopaminergic replacement therapies to alleviate asymmetry and promote longer steps, improve balance and reduce freezing of gait. This neuroprosthesis opens new perspectives to reduce the severity of locomotor deficits in people with PD.

Designing customized temporomandibular fossa prosthesis based on envelope surface of condyle movement: validation via in silico musculoskeletal simulation

Objective: This study presents an innovative articular fossa prosthesis generated by the envelope surface of condyle movement, and compares its mandible movements, muscle activities, and joint reaction forces with two temporomandibular joint (TMJ) prostheses using multibody musculoskeletal simulation. Methods: A healthy 23-year-old female was recruited for this study. Cone-beam computed tomographic (CBCT) was performed to reconstruct the mandibular bone geometry. A customized TMJ fossa prosthesis was designed based on the subject-specific envelope surface of condyle movement (ESCM). Mandibular kinematics and jaw-closing muscle electromyography (EMG) were simultaneously recorded during maximum jaw opening-closing movements. To validate our prosthesis design, a mandibular musculoskeletal model was established using flexible multibody dynamics and the obtained kinematics and EMG data. The Biomet fossa prosthesis and the ellipsoidal fossa prosthesis designed by imitating the lower limb prostheses were used for comparison. Simulations were performed to analyze the effects of different fossa prostheses on jaw opening-closing motions, mandibular muscle activation, and contact forces. Results: The maximum opening displacement for the envelope-based fossa prosthesis was greater than those for Biomet and ellipsoidal prostheses (36 mm, 35 mm, and 33 mm, respectively). The mandibular musculoskeletal model with ellipsoidal prosthesis led to dislocation near maximal jaw opening. Compared to Biomet, the envelope-based fossa reduced the digastric and lateral pterygoid activation at maximal jaw opening. It also reduced the maximal resistance to condylar sliding on the intact side by 63.2 N. Conclusion: A customized TMJ fossa prosthesis was successfully developed using the ESCM concept. Our study of musculoskeletal multibody modeling has highlighted its advantages and potential. The artificial fossa design successfully achieved a wider condylar range of motion. It also reduced the activation of jaw opening muscles on the affected side and resistance on the intact side. This study showed that an ESCM-based approach may be useful for optimizing TMJ fossa prostheses design.

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.

A multi-resolution physics-informed recurrent neural network: formulation and application to musculoskeletal systems

This work presents a multi-resolution physics-informed recurrent neural network (MR PI-RNN), for simultaneous prediction of musculoskeletal (MSK) motion and parameter identification of the MSK systems. The MSK application was selected as the model problem due to its challenging nature in mapping the high-frequency surface electromyography (sEMG) signals to the low-frequency body joint motion controlled by the MSK and muscle contraction dynamics. The proposed method utilizes the fast wavelet transform to decompose the mixed frequency input sEMG and output joint motion signals into nested multi-resolution signals. The prediction model is subsequently trained on coarser-scale input-output signals using a gated recurrent unit (GRU), and then the trained parameters are transferred to the next level of training with finer-scale signals. These training processes are repeated recursively under a transfer-learning fashion until the full-scale training (i.e., with unfiltered signals) is achieved, while satisfying the underlying dynamic equilibrium. Numerical examples on recorded subject data demonstrate the effectiveness of the proposed framework in generating a physics-informed forward-dynamics surrogate, which yields higher accuracy in motion predictions of elbow flexion-extension of an MSK system compared to the case with single-scale training. The framework is also capable of identifying muscle parameters that are physiologically consistent with the subject's kinematics data.

American Society of Biomechanics Journal of Biomechanics Award 2022: Computer models do not accurately predict human muscle passive muscle force and fiber length: Evaluating subject-specific modeling impact on musculoskeletal model predictions

Musculoskeletal models are valuable for studying and understanding the human body in a variety of clinical applications that include surgical planning, injury prevention, and prosthetic design. Subject-specific models have proven to be more accurate and useful compared to generic models. Nevertheless, it is important to validate all models when possible. To this end, gracilis muscle-tendon parameters were directly measured intraoperatively and used to test model predictions. The aim of this study was to evaluate the benefits and limitations of systematically incorporating subject-specific variables into muscle models used to predict passive force and fiber length. The results showed that incorporating subject-specific values generally reduced errors, although significant errors still existed. Optimization of the modeling parameter "tendon slack length" was explored in two cases: minimizing fiber length error and minimizing passive force error. The results showed that using all subject-specific values yielded the most favorable outcome in both models and optimization cases. However, the trade-off between fiber length error and passive force error will depend on the specific circumstances and research objectives due to significant individual errors. Notably, individual fiber length and passive force errors were as high as 20% and 37% respectively. Finally, the modeling parameter "tendon slack length" did not correlate with any real-world anatomical length.

Musculoskeletal model of osseointegrated transfemoral amputees in OpenSim

This study presents a generic OpenSim musculoskeletal model of people with an osseointegrated unilateral transfemoral amputation wearing a generic prosthesis. The model, which consists of seventy-six musculotendon units and two ideal actuators at the knee and ankle joints of the prosthesis, is tested by designing an optimal control strategy that guarantees the tracking of experimental amputee data during level-ground walking while finding the actuators' torques and minimizing the muscle forces. The model can be made subject-specific and, as such, is able to reproduce the kinematics and dynamics of both healthy and amputee subjects. The model provides a tool to analyze the biomechanics of level-ground walking and to understand the contribution of the muscles and of the prosthesis' actuators. The proposed OpenSim musculoskeletal model is released as support material to this study.

Predictive Control of Peak Achilles Tendon Force in a Simulated System of the Human Ankle Joint with a Parallel Artificial Actuator During Hopping

Latest advances in wearable exoskeletons for the human lower extremity predominantly focus on minimising metabolic cost of walking. However, there currently is no robotic exoskeleton that gains control on the mechanics of biological tissues such as biological muscles or series-elastic tendons. Achieving robotic control of biological tissue mechanics would enable prevention of musculoskeletal injuries or the personalization of rehabilitation treatments following injury with levels of precisions not attained before. In this paper, we introduce a new framework that uses nonlinear model predictive control (NMPC) for the closed-loop control of peak tendon force in a simulated system of the human ankle joint with parallel exoskeletal actuation. We propose a computationally efficient NMPC's inner model consisting of explicit, closed-form equations of muscle-tendon dynamics along with those of the ankle joint with parallel actuation. The proposed formulation is tested and verified on movement data collected during dynamic ankle dorsiflexion/plantarflexion rotations executed on a dynamometer as well as during walking and running on a treadmill. The framework designed using the NMPC controller showed a promising performance in keeping the Achilles tendon force under a predefined threshold. Results indicated that our proposed model was generalizable to different muscles and gaits and suitable for real-time applications due to its low computational time.

sEMG-based prediction of human forearm movements utilizing a biomechanical model based on individual anatomical/ physiological measures and a reduced set of optimization parameters

For assistive devices such as active orthoses, exoskeletons or other close-to-body robotic-systems, the immediate prediction of biological limb movements based on biosignals in the respective control system can be used to enable intuitive operation also by untrained users e.g. in healthcare, rehabilitation or industrial scenarios. Surface electromyography (sEMG) signals from the muscles that drive the limbs can be measured before the actual movement occurs and, hence, can be used as source for predicting limb movements. The aim of this work was to create a model that can be adapted to a new user or movement scenario with little measurement and computing effort. Therefore, a biomechanical model is presented that predicts limb movements of the human forearm based on easy to measure sEMG signals of the main muscles involved in forearm actuation (lateral and long head of triceps and short and long head of biceps). The model has 42 internal parameters of which 37 were attributed to 8 individually measured physiological measures (location of acromion at the shoulder, medial/lateral epicondyles as well as olecranon at the elbow, and styloid processes of radius/ulna at the wrist; maximum muscle forces of biceps and triceps). The remaining 5 parameters are adapted to specific movement conditions in an optimization process. The model was tested in an experimental study with 31 subjects in which the prediction quality of the model was assessed. The quality of the movement prediction was evaluated by using the normalized mean absolute error (nMAE) for two arm postures (lower, upper), two load conditions (2 kg, 4 kg) and two movement velocities (slow, fast). For the resulting 8 experimental combinations the nMAE varied between nMAE = 0.16 and nMAE = 0.21 (lower numbers better). An additional quality score (QS) was introduced that allows direct comparison between different movements. This score ranged from QS = 0.25 to QS = 0.40 (higher numbers better) for the experimental combinations. The above formulated aim was achieved with good prediction quality by using only 8 individual measurements (easy to collect body dimensions) and the subsequent optimization of only 5 parameters. At the same time, just easily accessible sEMG measurement locations are used to enable simple integration, e.g. in exoskeletons. This biomechanical model does not compete with models that measure all sEMG signals of the muscle heads involved in order to achieve the highest possible prediction quality.

Predictive Simulations with OpenSim Moco to Investigate the Interaction Between Human and Assistive Exoskeleton

The purpose of this work was to investigate the interaction between human and lower limbs assistive exoskeleton under different levels of assistance, by using computational simulations. To this, a human-exoskeleton interaction model was used and three predictive simulations were carried out with the OpenSim Moco. The results proved that the increase in the level of robot assistance causes a reduction in human effort. In addition, it was possible to verify the RMS torque of both the robot and the human, as well as the muscle activations, for the different levels of assistance simulated. For future work, we intend to run predictive simulations with more complex movements, such as gait free and with obstacles, in addition to using models that can represent a human being with muscle weakness on one side of the body (hemiparesis).

Tendon compliance and preload must be considered when determining the in vivo force-velocity relationship from the torque-angular velocity relation

In vivo, the force-velocity relation (F-v-r) is typically derived from the torque-angular velocity relation (T-ω-r), which is subject to two factors that may influence resulting measurements: tendon compliance and preload prior to contraction. The in vivo plantar flexors' T-ω-r was determined during preloaded maximum voluntary shortening contractions at 0-200°/s. Additionally, we used a two factor block simulation study design to independently analyze the effects of preload and tendon compliance on the resulting T-ω-r. Therefore, we replicated the in vivo experiment using a Hill-type muscle model of the gastrocnemius medialis. The simulation results matched a key pattern observed in our recorded in vivo experimental data: during preloaded contractions, torque output of the muscle was increased when compared with non-preloaded contractions from literature. This effect increased with increasing contraction velocity and can be explained by a rapidly recoiling tendon, allowing the contractile element to contract more slowly, thus developing higher forces compared with non-preloaded contractions. Our simulation results also indicate that a more compliant tendon results in increased ankle joint torques. The simulation and the experimental data clearly show that the deduction of the in vivo F-v-r from the T-ω-r is compromised due to the two factors preloading and tendon compliance.

Video-driven simulation of lower limb mechanical loading during aquatic exercises

Understanding the mechanical demands of an exercise on the musculoskeletal system is crucial to prescribe effective training or therapeutic interventions. Yet, that knowledge is currently limited in water, mostly because of the difficulty in evaluating external resistance. Here I reconcile recent advances in 3D markerless pose and mesh estimation, biomechanical simulations, and hydrodynamic modeling, to predict lower limb mechanical loading during aquatic exercises. Simulations are driven exclusively from a single video. Fluid forces were estimated within 12.5±4.1% of the peak forces determined through computational fluid dynamics analyses, at a speed three orders of magnitude greater. In silico hip and knee resultant joint forces agreed reasonably well with in vivo instrumented implant recordings (R²=0.74) downloaded from the OrthoLoad database, both in magnitude (RMSE =251±125 N) and direction (cosine similarity = 0.92±0.09). Hip flexors, glutes, adductors, and hamstrings were the main contributors to hip joint compressive forces (40.4±12.7%, 25.6±9.7%, 14.2±4.8%, 13.0±8.2%, respectively), while knee compressive forces were mostly produced by the gastrocnemius (39.1±15.9%) and vasti (29.4±13.7%). Unlike dry-land locomotion, non-hip- and non-knee-spanning muscles provided little to no offloading effect via dynamic coupling. This noninvasive method has the potential to standardize the reporting of exercise intensity, inform the design of rehabilitation protocols and improve their reproducibility.

A neuromuscular human body model for lumbar injury risk analysis in a vibration loading environment

BACKGROUND AND OBJECTIVE

Long-term intensive exposure to whole-body vibration substantially increases the risk of low back pain and degenerative diseases in special occupational groups, like motor vehicle drivers, military vehicle occupants, aircraft pilots, etc. This study aims to establish and validate a neuromuscular human body model focusing on improvement of the detailed description of anatomic structures and neural reflex control, for lumbar injury analysis in vibration loading environments.

METHODS

A whole-body musculoskeletal in Opensim codes was first improved by including a detailed anatomic description of spinal ligaments, non-linear intervertebral disc, and lumbar facet joints, and coupling a proprioceptive feedback closed-loop control strategy with GTOs and muscle spindles modeling in Python codes. Then, the established neuromuscular model was multi-levelly validated from sub-segments to the whole model, from regular movements to dynamic responses to vibration loadings. Finally, the neuromuscular model was combined with a dynamic model of an armored vehicle to analyze occupant lumbar injury risk in vibration loadings due to different road conditions and traveling velocities.

RESULT

Based on a series of biomechanical indexes, including lumbar joint rotation angles, the lumbar intervertebral pressures, the displacement of the lumbar segments, and the lumbar muscle activities, the validation results show that the present neuromuscular model is available and feasible in predicting lumbar biomechanical responses in normal daily movement and vibration loading environments. Furthermore, the combined analysis with the armored vehicle model predicted similar lumbar injury risk to the experimental or epidemiologic studies. The preliminary analysis results also showed that road types and travelling velocities have substantial combined effects on lumbar muscle activities, and indicated that intervertebral joint pressure and muscle activity indexes can need to be jointly considered for lumbar injury risk evaluation.

CONCLUSION

In conclusion, the established neuromuscular model is an effective tool to evaluate vibration loading effects on injury risk of the human body and assist vehicle design vibration comfort by directly concerning the human body injury itself.

Robust walking control of a lower limb rehabilitation exoskeleton coupled with a musculoskeletal model via deep reinforcement learning

BACKGROUND

Few studies have systematically investigated robust controllers for lower limb rehabilitation exoskeletons (LLREs) that can safely and effectively assist users with a variety of neuromuscular disorders to walk with full autonomy. One of the key challenges for developing such a robust controller is to handle different degrees of uncertain human-exoskeleton interaction forces from the patients. Consequently, conventional walking controllers either are patient-condition specific or involve tuning of many control parameters, which could behave unreliably and even fail to maintain balance.

METHODS

We present a novel, deep neural network, reinforcement learning-based robust controller for a LLRE based on a decoupled offline human-exoskeleton simulation training with three independent networks, which aims to provide reliable walking assistance against various and uncertain human-exoskeleton interaction forces. The exoskeleton controller is driven by a neural network control policy that acts on a stream of the LLRE's proprioceptive signals, including joint kinematic states, and subsequently predicts real-time position control targets for the actuated joints. To handle uncertain human interaction forces, the control policy is trained intentionally with an integrated human musculoskeletal model and realistic human-exoskeleton interaction forces. Two other neural networks are connected with the control policy network to predict the interaction forces and muscle coordination. To further increase the robustness of the control policy to different human conditions, we employ domain randomization during training that includes not only randomization of exoskeleton dynamics properties but, more importantly, randomization of human muscle strength to simulate the variability of the patient's disability. Through this decoupled deep reinforcement learning framework, the trained controller of LLREs is able to provide reliable walking assistance to patients with different degrees of neuromuscular disorders without any control parameter tuning.

RESULTS AND CONCLUSION

A universal, RL-based walking controller is trained and virtually tested on a LLRE system to verify its effectiveness and robustness in assisting users with different disabilities such as passive muscles (quadriplegic), muscle weakness, or hemiplegic conditions without any control parameter tuning. Analysis of the RMSE for joint tracking, CoP-based stability, and gait symmetry shows the effectiveness of the controller. An ablation study also demonstrates the strong robustness of the control policy under large exoskeleton dynamic property ranges and various human-exoskeleton interaction forces. The decoupled network structure allows us to isolate the LLRE control policy network for testing and sim-to-real transfer since it uses only proprioception information of the LLRE (joint sensory state) as the input. Furthermore, the controller is shown to be able to handle different patient conditions without the need for patient-specific control parameter tuning.

Age-related changes in gait biomechanics and their impact on the metabolic cost of walking: Report from a National Institute on Aging workshop

Changes in old age that contribute to the complex issue of an increased metabolic cost of walking (mass-specific energy cost per unit distance traveled) in older adults appear to center at least in part on changes in gait biomechanics. However, age-related changes in energy metabolism, neuromuscular function and connective tissue properties also likely contribute to this problem, of which the consequences are poor mobility and increased risk of inactivity-related disease and disability. The U.S. National Institute on Aging convened a workshop in September 2021 with an interdisciplinary group of scientists to address the gaps in research related to the mechanisms and consequences of changes in mobility in old age. The goal of the workshop was to identify promising ways to move the field forward toward improving gait performance, decreasing energy cost, and enhancing mobility for older adults. This report summarizes the workshop and brings multidisciplinary insight into the known and potential causes and consequences of age-related changes in gait biomechanics. We highlight how gait mechanics and energy cost change with aging, the potential neuromuscular mechanisms and role of connective tissue in these changes, and cutting-edge interventions and technologies that may be used to measure and improve gait and mobility in older adults. Key gaps in the literature that warrant targeted research in the future are identified and discussed.

Multibody Models of the Thoracolumbar Spine: A Review on Applications, Limitations, and Challenges

Numerical models of the musculoskeletal system as investigative tools are an integral part of biomechanical and clinical research. While finite element modeling is primarily suitable for the examination of deformation states and internal stresses in flexible bodies, multibody modeling is based on the assumption of rigid bodies, that are connected via joints and flexible elements. This simplification allows the consideration of biomechanical systems from a holistic perspective and thus takes into account multiple influencing factors of mechanical loads. Being the source of major health issues worldwide, the human spine is subject to a variety of studies using these models to investigate and understand healthy and pathological biomechanics of the upper body. In this review, we summarize the current state-of-the-art literature on multibody models of the thoracolumbar spine and identify limitations and challenges related to current modeling approaches.

The impact of age-related increase in passive muscle stiffness on simulated upper limb reaching

Ageing changes the musculoskeletal and neural systems, potentially affecting a person's ability to perform daily living activities. One of these changes is increased passive stiffness of muscles, but its contribution to performance is difficult to separate experimentally from other ageing effects such as loss of muscle strength or cognitive function. A computational upper limb model was used to study the effects of increasing passive muscle stiffness on reaching performance across the model's workspace (all points reachable with a given model geometry). The simulations indicated that increased muscle stiffness alone caused deterioration of reaching accuracy, starting from the edges of the workspace. Re-tuning the model's control parameters to match the ageing muscle properties does not fully reverse ageing effects but can improve accuracy in selected regions of the workspace. The results suggest that age-related muscle stiffening, isolated from other ageing effects, impairs reaching performance. The model also exhibited oscillatory instability in a few simulations when the controller was tuned to the presence of passive muscle stiffness. This instability is not observed in humans, implying the presence of natural stabilizing strategies, thus pointing to the adaptive capacity of neural control systems as a potential area of future investigation in age-related muscle stiffening.

The force response of muscles to activation and length perturbations depends on length history

Recent studies have demonstrated that muscle force is not determined solely by activation under dynamic conditions, and that length history has an important role in determining dynamic muscle force. Yet, the mechanisms for how muscle force is produced under dynamic conditions remain unclear. To explore this, we investigated the effects of muscle stiffness, activation and length perturbations on muscle force. First, submaximal isometric contraction was established for whole soleus muscles. Next, the muscles were actively shortened at three velocities. During active shortening, we measured muscle stiffness at optimal muscle length (L0) and the force response to time-varying activation and length perturbations. We found that muscle stiffness increased with activation but decreased as shortening velocity increased. The slope of the relationship between maximum force and activation amplitude differed significantly among shortening velocities. Also, the intercept and slope of the relationship between length perturbation amplitude and maximum force decreased with shortening velocity. As shortening velocities were related to muscle stiffness, the results suggest that length history determines muscle stiffness and the history-dependent muscle stiffness influences the contribution of activation and length perturbations to muscle force. A two-parameter viscoelastic model including a linear spring and a linear damper in parallel with measured stiffness predicted history-dependent muscle force with high accuracy. The results and simulations support the hypothesis that muscle force under dynamic conditions can be accurately predicted as the force response of a history-dependent viscoelastic material to length perturbations.

Understanding altered contractile properties in advanced age: insights from a systematic muscle modelling approach

Age-related alterations of skeletal muscle are numerous and present inconsistently, and the effect of their interaction on contractile performance can be nonintuitive. Hill-type muscle models predict muscle force according to well-characterised contractile phenomena. Coupled with simple, yet reasonably realistic activation dynamics, such models consist of parameters that are meaningfully linked to fundamental aspects of muscle excitation and contraction. We aimed to illustrate the utility of a muscle model for elucidating relevant mechanisms and predicting changes in output by simulating the individual and combined effects on isometric force of several known ageing-related adaptations. Simulating literature-informed reductions in free Ca2+ concentration and Ca2+ sensitivity generated predictions at odds qualitatively with the characteristic slowing of contraction speed. Conversely, incorporating slower Ca2+ removal or a fractional increase in type I fibre area emulated expected changes; the former was required to simulate slowing of the twitch measured experimentally. Slower Ca2+ removal more than compensated for force loss arising from a large reduction in Ca2+ sensitivity or moderate reduction in Ca2+ release, producing realistic age-related shifts in the force-frequency relationship. Consistent with empirical data, reductions in free Ca2+ concentration and Ca2+ sensitivity reduced maximum tetanic force only slightly, even when acting in concert, suggesting a modest contribution to lower specific force. Lower tendon stiffness and slower intrinsic shortening speed slowed and prolonged force development in a compliance-dependent manner without affecting force decay. This work demonstrates the advantages of muscle modelling for exploring sources of variation and identifying mechanisms underpinning the altered contractile properties of aged muscle.

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.

Towards improving the accuracy of musculoskeletal simulation of dynamic three-dimensional spine rotations with optimizing model and algorithm

BACKGROUND

The accuracy of musculoskeletal simulations greatly relies on model structures and optimization algorithms. This study investigated the unclarified influence of accounting for several commonly-simplified different model components and optimization criteria on spinal musculoskeletal simulations.

METHODS

The study constructed a full-body musculoskeletal model with passive components of functional spinal units and spinal muscles subject-specifically refined. A muscle redundancy solver was built with 15 optimization criteria. Three-dimensional spine rotations and spinal muscle activities were measured using optical motion capture and electromyogram techniques when eight healthy volunteers performed standing, flexion/extension, lateral bending, and axial rotation. The effect of the model with four different conditions of the passive components and the sensitivity of the 15 optimization criteria on simulations were investigated.

RESULTS

Accounting for the refined passive components significantly improved the simulation accuracy. Different optimization criteria behaved distinctly for different motions. Generally minimizing the sum of squared muscle activations outperformed the others, with the highest averaged correlation coefficient (0.82) between the estimated erector spinae muscle activations and measured electromyography and with the estimated joint compression forces comparable to in vivo reference data.

CONCLUSION

This study highlights the importance of passive model components and proposes a suitable optimization framework for realistic spinal musculoskeletal simulations.

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.

Effects of changes in optimal muscle fibre length in the biceps femoris long head on muscle force during the late swing phase of maximal speed sprinting: a simulation study

Hamstring strain injuries would frequently occur during the late swing phase of sprinting, while increasing biceps femoris long head's (BFlh) fascicle length induced by eccentric contraction exercises can reduce the risk of strain injuries. Thus, using a musculoskeletal modelling simulation, we examined how manipulating BFlh optimal muscle fibre length would change muscle force during the late swing phase of sprinting for providing knowledge preventing hamstring strain injuries. A motion capture system was used to collect kinematic data from 40 male athletes during maximal speed sprinting. Muscle force and force-generating capabilities determined by force-length-velocity properties were estimated with three BFlh optimal muscle fibre lengths (90%, 110% and 120%), which were perturbed from the nominal (100%). During the late swing phase of sprinting, the muscle force and force-generating capabilities, induced by the force-length property rather than the force-velocity property, were increased by increases in BFlh optimal muscle fibre length. Moreover, magnitudes of the simulated increases in muscle force and force-generating capabilities were correlated with the peak BFlh muscle-tendon unit length. These results demonstrate that lengthening BFlh optimal muscle fibre might increase muscle force during the late swing phase, and the magnitude of increment would be associated with increasing muscle-tendon unit length.

Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model

This paper proposes to use deep reinforcement learning to teach a physics-based human musculoskeletal model to ascend stairs and ramps. The deep reinforcement learning architecture employs the proximal policy optimization algorithm combined with imitation learning and is trained with experimental data of a public dataset. The human model is developed in the open-source simulation software OpenSim, together with two objects (i.e., the stairs and ramp) and the elastic foundation contact dynamics. The model can learn to ascend stairs and ramps with muscle forces comparable to healthy subjects and with a forward dynamics comparable to the experimental training data, achieving an average correlation of 0.82 during stair ascent and of 0.58 during ramp ascent across both the knee and ankle joints.

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.

Manual and semi-automatic determination of elbow angle-independent parameters for a model of the biceps brachii distal tendon based on ultrasonic imaging

Tendons consist of passive soft tissue with non linear material properties. They play a key role in force transmission from muscle to skeletal structure. The properties of tendons have been extensively examined in vitro. In this work, a non linear model of the distal biceps brachii tendon was parameterized based on measurements of myotendinous junction displacements in vivo at different load forces and elbow angles. The myotendinous junction displacement was extracted from ultrasound B-mode images within an experimental setup which also allowed for the retrieval of the exerted load forces as well as the elbow joint angles. To quantify the myotendinous junction movement based on visual features from ultrasound images, a manual and an automatic method were developed. The performance of both methods was compared. By means of exemplary data from three subjects, reliable fits of the tendon model were achieved. Further, different aspects of the non linear tendon model generated in this way could be reconciled with individual experiments from literature.

Contribution of stroke-related changes in neuromuscular factors to gear ratio during isometric contraction of medial gastrocnemius muscle: A simulation study

BACKGROUND

It is not clear which neuromuscular factors are most closely associated with the loss of variable fascicle gearing after chronic stroke. The purpose of this simulation study is to determine the effects of stroke-related changes in key neuromuscular factors on the gear ratio.

METHODS

A modified Hill-type model of the medial gastrocnemius was developed to determine the gear ratio for a given muscle activation level and musculotendon length. Model parameters were then systematically adjusted to simulate known stroke-related changes in neuromuscular factors, and the gear ratio was computed for each change in the parameters. A Monte Carlo simulation was performed to understand which neuromuscular factors and fiber behavior-related parameters are most relevant to the loss of variable gearing. Dominance analyses were also conducted to quantify the relative importance of fiber behavior-related parameters on the gear ratio.

FINDINGS

The gear ratio decreases significantly with smaller pennation angle and with shorter optimal fiber length. In addition, muscle thickness and pennation angle at optimal fiber length appear to be the most important muscle architectural parameters. Dominance analyses further suggest that primary determinants of gear ratio include initial pennation angle, fiber rotation-shortening ratio, initial muscle thickness, and fiber rotation.

INTERPRETATION

Our findings provide insight that the pennation angle may play an important role for efficient muscular contraction, implying that maintaining muscle architecture and/or improving fiber/fascicle rotation could a key goal in rehabilitation interventions. Our findings will help us to better interpret altered gearing behavior in aging and pathological muscles.

Hamstrings force-length relationships and their implications for angle-specific joint torques: a narrative review

Temporal biomechanical and physiological responses to physical activity vary between individual hamstrings components as well as between exercises, suggesting that hamstring muscles operate differently, and over different lengths, between tasks. Nevertheless, the force-length properties of these muscles have not been thoroughly investigated. The present review examines the factors influencing the hamstrings’ force-length properties and relates them to in vivo function. A search in four databases was performed for studies that examined relations between muscle length and force, torque, activation, or moment arm of hamstring muscles. Evidence was collated in relation to force-length relationships at a sarcomere/fiber level and then moment arm-length, activation-length, and torque-joint angle relations. Five forward simulation models were also used to predict force-length and torque-length relations of hamstring muscles. The results show that, due to architectural differences alone, semitendinosus (ST) produces less peak force and has a flatter active (contractile) fiber force-length relation than both biceps femoris long head (BFlh) and semimembranosus (SM), however BFlh and SM contribute greater forces through much of the hip and knee joint ranges of motion. The hamstrings’ maximum moment arms are greater at the hip than knee, so the muscles tend to act more as force producers at the hip but generate greater joint rotation and angular velocity at the knee for a given muscle shortening length and speed. However, SM moment arm is longer than SM and BFlh, partially alleviating its reduced force capacity but also reducing its otherwise substantial excursion potential. The current evidence, bound by the limitations of electromyography techniques, suggests that joint angle-dependent activation variations have minimal impact on force-length or torque-angle relations. During daily activities such as walking or sitting down, the hamstrings appear to operate on the ascending limbs of their force-length relations while knee flexion exercises performed with hip angles 45–90° promote more optimal force generation. Exercises requiring hip flexion at 45–120° and knee extension 45–0° (e.g. sprint running) may therefore evoke greater muscle forces and, speculatively, provide a more optimum adaptive stimulus. Finally, increases in resistance to stretch during hip flexion beyond 45° result mainly from SM and BFlh muscles.

Policy Design for an Ankle-Foot Orthosis Using Simulated Physical Human-Robot Interaction via Deep Reinforcement Learning

This paper presents a novel approach for designing a robotic orthosis controller considering physical human-robot interaction (pHRI). Computer simulation for this human-robot system can be advantageous in terms of time and cost due to the laborious nature of designing a robot controller that effectively assists humans with the appropriate magnitude and phase. Therefore, we propose a two-stage policy training framework based on deep reinforcement learning (deep RL) to design a robot controller using human-robot dynamic simulation. In Stage 1, the optimal policy of generating human gaits is obtained from deep RL-based imitation learning on a healthy subject model using the musculoskeletal simulation in OpenSim-RL. In Stage 2, human models in which the right soleus muscle is weakened to a certain severity are created by modifying the human model obtained from Stage 1. A robotic orthosis is then attached to the right ankle of these models. The orthosis policy that assists walking with optimal torque is then trained on these models. Here, the elastic foundation model is used to predict the pHRI in the coupling part between the human and robotic orthosis. Comparative analysis of kinematic and kinetic simulation results with the experimental data shows that the derived human musculoskeletal model imitates a human walking. It also shows that the robotic orthosis policy obtained from two-stage policy training can assist the weakened soleus muscle. The proposed approach was validated by applying the learned policy to ankle orthosis, conducting a gait experiment, and comparing it with the simulation results.

A subject-specific musculoskeletal model to predict the tibiofemoral contact forces during daily living activities

Accurate prediction of tibiofemoral contact force (TFCF) during daily living activities is significant for understanding the initiation, progression, and treatment of knee osteoarthritis (KOA). However, the diversity of target activities, prediction accuracy, and computational efficiency of the current musculoskeletal simulations need to be further improved. In this study, a subject-specific musculoskeletal model considered the tibiofemoral alignment, medial-lateral contact locations, secondary tibiofemoral and all patellofemoral motions, and knee ligaments was proposed to predict the TFCFs during the five daily activities (normal walking, sit-to-stand, stand-to-sit, stair ascent, and stair descent) in OpenSim software. The standing lower-limbs-full-length radiograph, local radiograph of knee joint, motion capture data, and force plate data of eighteen subjects were acquired as the input data of the musculoskeletal model. The results showed good agreements of TFCFs between the predictions based on our proposed musculoskeletal model and the in-vivo measurements based on instrumented knee implants during the five daily activities (RMSE: 0.16 ∼ 0.31 BW, R²: 0.88 ∼ 0.97, M: -0.11 ∼ -0.02, P: 0.03 ∼ 0.10, and C: 0.04 ∼ 0.14). Additionally, the order of the peak total and lateral TFCFs from low to high was normal walking, stair ascent and stand-to-sit, and stair descent and sit-to-stand (P < 0.05), and the peak medial TFCF was stand-to-sit, sit-to-stand, normal walking, stair ascent and stair descent (P < 0.05). The outcomes of this study are valuable for further understanding the knee biomechanics during daily living activities and providing theoretical guidance for the treatments of KOA.

Evaluating Muscle Synergies With EMG Data and Physics Simulation in the Neurorobotics Platform

Although we can measure muscle activity and analyze their activation patterns, we understand little about how individual muscles affect the joint torque generated. It is known that they are controlled by circuits in the spinal cord, a system much less well-understood than the cortex. Knowing the contribution of the muscles toward a joint torque would improve our understanding of human limb control. We present a novel framework to examine the control of biomechanics using physics simulations informed by electromyography (EMG) data. These signals drive a virtual musculoskeletal model in the Neurorobotics Platform (NRP), which we then use to evaluate resulting joint torques. We use our framework to analyze raw EMG data collected during an isometric knee extension study to identify synergies that drive a musculoskeletal lower limb model. The resulting knee torques are used as a reference for genetic algorithms (GA) to generate new simulated activation patterns. On the platform the GA finds solutions that generate torques matching those observed. Possible solutions include synergies that are similar to those extracted from the human study. In addition, the GA finds activation patterns that are different from the biological ones while still producing the same knee torque. The NRP forms a highly modular integrated simulation platform allowing these in silico experiments. We argue that our framework allows for research of the neurobiomechanical control of muscles during tasks, which would otherwise not be possible.

Relative contribution of altered neuromuscular factors to muscle activation-force relationships following chronic stroke: A simulation study

The purpose of this study was to investigate the potential effects of key neuromuscular factors on muscle activation-force relationships, thereby helping us understand abnormal EMG-force relationships often reported in chronic stroke-impaired muscles. A modified Hill-type muscle model was developed to calculate muscle force production for a given muscle activation level and musculotendon length. Model parameters used to characterize musculotendon unit properties of medial gastrocnemius were adjusted to simulate known stroke-related changes in neuromuscular factors (e.g., voluntary activation and muscle mechanical properties). The muscle activation-force slope (i.e., muscle activation over force) was computed as a function of ankle joint angle. A Monte Carlo simulation approach was implemented to understand which neuromuscular factors are closely associated with the activation-force slope. Our simulations showed that a reduction in factors linked to voluntary activation capacity and to maximum force-generating capacity may be the primary contributors that increase the activation-force slope in dorsiflexed positions, and that a narrower active force-length curve appears to be the most significant factor that increases the slope in plantar flexed positions. In addition, our Monte Carlo simulation results demonstrated that an increase in the activation-force slope is strongly correlated with a reduction in voluntary activation capacity, in the maximum force-generating capacity, and in the active force-length curve width. These findings will help us to better interpret altered EMG-force relationships following chronic stroke.

RBF Sliding Mode Control Method for an Upper Limb Rehabilitation Exoskeleton Based on Intent Recognition

Aiming at the lack of active willingness of patients to participate in the current upper limb exoskeleton rehabilitation training control methods, this study proposed a radial basis function (RBF) sliding mode impedance control method based on surface electromyography (sEMG) to identify the movement intention of upper limb rehabilitation. The proposed control method realizes the process of active and passive rehabilitation training according to the wearer’s movement intention. This study first established a joint angle prediction model based on sEMG for the problem of poor human–machine coupling and used the least-squares support vector machine method (LSSVM) to complete the upper limb joint angle prediction. In addition, in view of the problem of poor compliance in the rehabilitation training process, an adaptive sliding mode controller based on the RBF network approximation system model was proposed. In the process of active training, an impedance model was added based on the position loop control, which could dynamically adjust the motion trajectory according to the interaction force. The experiment results showed that the impedance control method based on the RBF could effectively reduce the interaction force between the human and machine to improve the compliance of the exoskeleton manipulator and achieve the purpose of stabilizing the impedance characteristics of the system.

A Differentiable Dynamic Model for Musculoskeletal Simulation and Exoskeleton Control

An exoskeleton, a wearable device, was designed based on the user's physical and cognitive interactions. The control of the exoskeleton uses biomedical signals reflecting the user intention as input, and its algorithm is calculated as an output to make the movement smooth. However, the process of transforming the input of biomedical signals, such as electromyography (EMG), into the output of adjusting the torque and angle of the exoskeleton is limited by a finite time lag and precision of trajectory prediction, which result in a mismatch between the subject and exoskeleton. Here, we propose an EMG-based single-joint exoskeleton system by merging a differentiable continuous system with a dynamic musculoskeletal model. The parameters of each muscle contraction were calculated and applied to the rigid exoskeleton system to predict the precise trajectory. The results revealed accurate torque and angle prediction for the knee exoskeleton and good performance of assistance during movement. Our method outperformed other models regarding the rate of convergence and execution time. In conclusion, a differentiable continuous system merged with a dynamic musculoskeletal model supported the effective and accurate performance of an exoskeleton controlled by EMG signals.