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

Tapping Into Skeletal Muscle Biomechanics for Design and Control of Lower Limb Exoskeletons: A Narrative Review

Lower limb exoskeletons and exosuits ("exos") are traditionally designed with a strong focus on mechatronics and actuation, whereas the "human side" is often disregarded or minimally modeled. Muscle biomechanics principles and skeletal muscle response to robot-delivered loads should be incorporated in design/control of exos. In this narrative review, we summarize the advances in literature with respect to the fusion of muscle biomechanics and lower limb exoskeletons. We report methods to measure muscle biomechanics directly and indirectly and summarize the studies that have incorporated muscle measures for improved design and control of intuitive lower limb exos. Finally, we delve into articles that have studied how the human-exo interaction influences muscle biomechanics during locomotion. To support neurorehabilitation and facilitate everyday use of wearable assistive technologies, we believe that future studies should investigate and predict how exoskeleton assistance strategies would structurally remodel skeletal muscle over time. Real-time mapping of the neuromechanical origin and generation of muscle force resulting in joint torques should be combined with musculoskeletal models to address time-varying parameters such as adaptation to exos and fatigue. Development of smarter predictive controllers that steer rather than assist biological components could result in a synchronized human-machine system that optimizes the biological and electromechanical performance of the combined system.

Musculotendon Parameters in Lower Limb Models: Simplifications, Uncertainties, and Muscle Force Estimation Sensitivity

Musculotendon parameters are key factors in the Hill-type muscle contraction dynamics, determining the muscle force estimation accuracy of a musculoskeletal model. Their values are mostly derived from muscle architecture datasets, whose emergence has been a major impetus for model development. However, it is often not clear if such parameter update indeed improves simulation accuracy. Our goal is to explain to model users how these parameters are derived and how accurate they are, as well as to what extent errors in parameter values might influence force estimation. We examine in detail the derivation of musculotendon parameters in six muscle architecture datasets and four prominent OpenSim models of the lower limb, and then identify simplifications which could add uncertainties to the derived parameter values. Finally, we analyze the sensitivity of muscle force estimation to these parameters both numerically and analytically. Nine typical simplifications in parameter derivation are identified. Partial derivatives of the Hill-type contraction dynamics are derived. Tendon slack length is determined as the musculotendon parameter that muscle force estimation is most sensitive to, whereas pennation angle is the least impactful. Anatomical measurements alone are not enough to calibrate musculotendon parameters, and the improvement on muscle force estimation accuracy will be limited if the source muscle architecture datasets are the only main update. Model users may check if a dataset or model is free of concerning factors for their research or application requirements. The derived partial derivatives may be used as the gradient for musculotendon parameter calibration. For model development, we demonstrate that it is more promising to focus on other model parameters or components and seek alternative strategies to further increase simulation accuracy.

Modern three-dimensional digital methods for studying locomotor biomechanics in tetrapods

Here, we review the modern interface of three-dimensional (3D) empirical (e.g. motion capture) and theoretical (e.g. modelling and simulation) approaches to the study of terrestrial locomotion using appendages in tetrapod vertebrates. These tools span a spectrum from more empirical approaches such as XROMM, to potentially more intermediate approaches such as finite element analysis, to more theoretical approaches such as dynamic musculoskeletal simulations or conceptual models. These methods have much in common beyond the importance of 3D digital technologies, and are powerfully synergistic when integrated, opening a wide range of hypotheses that can be tested. We discuss the pitfalls and challenges of these 3D methods, leading to consideration of the problems and potential in their current and future usage. The tools (hardware and software) and approaches (e.g. methods for using hardware and software) in the 3D analysis of tetrapod locomotion have matured to the point where now we can use this integration to answer questions we could never have tackled 20 years ago, and apply insights gleaned from them to other fields.

The Impact of Patellar Tendon Advancement on Knee Joint Moment and Muscle Forces in Patients with Cerebral Palsy

Background: Patellar tendon advancement (PTA) is performed for the treatment of crouch gait in patients with cerebral palsy (CP). In this study, we aimed to determine the influence of PTA in the context of single-event multilevel surgery (SEMLS) on knee joint moment and muscle forces through musculoskeletal modeling; Methods: Gait data of children with CP and crouch gait were retrospectively analyzed. Patients were included if they had a SEMLS with a PTA (PTA group, n = 18) and a SEMLS without a PTA (NoPTA group, n = 18). A musculoskeletal model was used to calculate the pre- and postoperative knee joint moments and muscle forces; Results: Knee extensor moment increased in the PTA group postoperatively (p = 0.016), but there was no statistically significant change in the NoPTA group (p > 0.05). The quadriceps muscle forces increased for the PTA group (p = 0.034), while there was no difference in the NoPTA group (p > 0.05). The hamstring muscle forces increased in the PTA group (p = 0.039), while there was no difference in the NoPTA group (p > 0.05); Conclusions: PTA was found to be an effective surgery for the treatment of crouch gait. It contributes to improving knee extensor moment, decreasing knee flexor moment, and enhancing the quadriceps and hamstring muscle forces postoperatively.

Force-length-velocity behavior and muscle-specific joint moment contributions during countermovement and squat jumps

The countermovement (CMJ) and squat (SJ) jump are common tasks used to assess neuromuscular performance. While much is known about joint-level differences between both tasks, not much is known about differences in muscle-level biomechanics. The purpose of this study was to calculate the forces, force-length-velocity behavior, and muscle-specific contributions to net joint moments (NJM) during CMJ and SJ. Eight basketball players performed maximal CMJ and SJ while motion capture and ground reaction force (GRF) data were recorded. A musculoskeletal model and static optimization algorithm computed muscles forces and force generating abilities of the soleus (SOL), gastrocnemii (GAS), vastii (VAS), rectus femoris (RF), hamstring (HAM), and gluteus maximus (GMAX) muscles during CMJ and SJ. In addition, the moments created by each muscle were calculated and studied in relation to the respective NJMs. CMJ were characterized by longer movement duration, but similar GRFs and jump heights as SJ. VAS and GMAX exhibited greater muscle forces and force generating abilities during CMJ, likely because of more optimal force-velocity behavior. In contrast, the HAM exhibited more favorable force-length behavior during SJ. Muscle moments during CMJ and SJ were similar, except for the HAM, which produced greater hip extension and knee flexion muscle moments during CMJ. Although muscle forces and force generating abilities of the VAS and GMAX were greater during CMJ, more optimal force-length behavior and greater muscle moment contribution to knee NJM by the HAM during SJ appear to balance such that overall GRF and jump height remain similar regardless of jump task.

Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation

Modeling human motor control and predicting how humans will move in novel environments is a grand scientific challenge. Researchers in the fields of biomechanics and motor control have proposed and evaluated motor control models via neuromechanical simulations, which produce physically correct motions of a musculoskeletal model. Typically, researchers have developed control models that encode physiologically plausible motor control hypotheses and compared the resulting simulation behaviors to measurable human motion data. While such plausible control models were able to simulate and explain many basic locomotion behaviors (e.g. walking, running, and climbing stairs), modeling higher layer controls (e.g. processing environment cues, planning long-term motion strategies, and coordinating basic motor skills to navigate in dynamic and complex environments) remains a challenge. Recent advances in deep reinforcement learning lay a foundation for modeling these complex control processes and controlling a diverse repertoire of human movement; however, reinforcement learning has been rarely applied in neuromechanical simulation to model human control. In this paper, we review the current state of neuromechanical simulations, along with the fundamentals of reinforcement learning, as it applies to human locomotion. We also present a scientific competition and accompanying software platform, which we have organized to accelerate the use of reinforcement learning in neuromechanical simulations. This “Learn to Move” competition was an official competition at the NeurIPS conference from 2017 to 2019 and attracted over 1300 teams from around the world. Top teams adapted state-of-the-art deep reinforcement learning techniques and produced motions, such as quick turning and walk-to-stand transitions, that have not been demonstrated before in neuromechanical simulations without utilizing reference motion data. We close with a discussion of future opportunities at the intersection of human movement simulation and reinforcement learning and our plans to extend the Learn to Move competition to further facilitate interdisciplinary collaboration in modeling human motor control for biomechanics and rehabilitation research

How muscles maximize performance in accelerated sprinting

We sought to provide a more comprehensive understanding of how the individual leg muscles act synergistically to generate a ground force impulse and maximize the change in forward momentum of the body during accelerated sprinting. We combined musculoskeletal modelling with gait data to simulate the majority of the acceleration phase (19 foot contacts) of a maximal sprint over ground. Individual muscle contributions to the ground force impulse were found by evaluating each muscle's contribution to the vertical and fore-aft components of the ground force (termed "supporter" and "accelerator/brake," respectively). The ankle plantarflexors played a major role in achieving maximal-effort accelerated sprinting. Soleus acted primarily as a supporter by generating a large fraction of the upward impulse at each step whereas gastrocnemius contributed appreciably to the propulsive and upward impulses and functioned as both accelerator and supporter. The primary role of the vasti was to deliver an upward impulse to the body (supporter), but these muscles also acted as a brake by retarding forward momentum. The hamstrings and gluteus medius functioned primarily as accelerators. Gluteus maximus was neither an accelerator nor supporter as it functioned mainly to decelerate the swinging leg in preparation for foot contact at the next step. Fundamental knowledge of lower-limb muscle function during maximum acceleration sprinting is of interest to coaches endeavoring to optimize sprint performance in elite athletes as well as sports medicine clinicians aiming to improve injury prevention and rehabilitation practices.

Peak Forces and Force Generating Capacities of Lower Extremity Muscles During Dynamic Tasks in People With and Without Chronic Ankle Instability

People with chronic ankle instability (CAI) exhibit neuromuscular deficits. However, no study has investigated deficits in forces or force-generating capacities of individual muscles in people with CAI during dynamic tasks. Therefore, the purpose of this study was to estimate and compare peak forces and force-generating capacities of individual muscles during dynamic tasks in people with CAI and healthy controls (CON). Eleven people with CAI and eleven CON performed landing, anticipated cutting, and unanticipated cutting as motion capture, force plate, and electromyography data were recorded. A musculoskeletal model was used to estimate the force and force-generating capacity of lower extremity muscles. People with CAI exhibited greater gluteus maximus force and force-generating capacity than CON during all tasks. In addition, people with CAI exhibited greater force-generating capacity of the vastii muscles than CON during the unanticipated cutting task. These findings suggest that, during dynamic tasks, people with CAI exhibit a neuromuscular control strategy that is characterised by differences in peak forces and force-generating capacities of proximal muscles, which may allow them to compensate for previously described deficits in distal muscles.

SENSITIVITY OF MODEL-PREDICTED MUSCLE FORCES OF PATIENTS WITH CEREBRAL PALSY TO VARIATIONS IN MUSCLE-TENDON PARAMETERS

Computational musculoskeletal modeling and simulation platforms are efficient tools to gain insight into the muscular coordination of patients with motor disabilities such as cerebral palsy (CP). Muscle force predictions from simulation programs are influenced by the architectural and contractile properties of muscle-tendon units. In this study, we aimed to evaluate the sensitivity of major lower limb muscle forces in patients with CP to changes in muscle-tendon parameters. Open-access datasets of children with CP ([Formula: see text]) and healthy children ([Formula: see text]) were considered. Monte Carlo analysis was executed to specify how sensitive the muscle forces to perturbations between [Formula: see text]% and [Formula: see text]% of the nominal value of the maximum isometric muscle force, optimal muscle fiber length, muscle pennation angle, tendon slack length, and maximum contraction velocity of muscle. The sensitivity analysis revealed that muscle forces of CP patients and healthy individuals were most sensitive to perturbations in the tendon slack length ([Formula: see text]), while forces of CP patients were more sensitive to tendon slack length when compared to the healthy group ([Formula: see text]). Muscle forces of patients and healthy individuals were insensitive to the other four parameters ([Formula: see text]), except for the gracilis and sartorius muscles in which the proportion of optimal muscle fiber length to tendon slack length is higher than 1; forces of these two muscles were also sensitive to the optimal muscle fiber length. The results of this study are expected to contribute to our understanding of which parameters should be personalized when conducting musculoskeletal modeling and simulation of patients with CP.