Biomechanical models in the lower-limb exoskeletons development: a review

Lower limb exoskeletons serve multiple purposes, like supporting and augmenting movement. Biomechanical models are practical tools to understand human movement, and motor control. This paper provides an overview of these models and a comprehensive review of the current applications of them in assistive device development. It also critically analyzes the existing literature to identify research gaps and suggest future directions. Biomechanical models can be broadly classified as conceptual and detailed models and can be used for the design, control, and assessment of exoskeletons. Also, these models can estimate unmeasurable or hard-to-measure variables, which is also useful within the aforementioned applications. We identified the validation of simulation studies and the enhancement of the accuracy and fidelity of biomechanical models as key future research areas for advancing the development of assistive devices. Additionally, we suggest using exoskeletons as a tool to validate and refine these models. We also emphasize the exploration of model-based design and control approaches for exoskeletons targeting pathological gait, and utilizing biomechanical models for diverse design objectives of exoskeletons. In addition, increasing the availability of open source resources accelerates the advancement of the exoskeleton and biomechanical models. Although biomechanical models are widely applied to improve movement assistance and rehabilitation, their full potential in developing human-compatible exoskeletons remains underexplored and requires further investigation. This review aims to reveal existing needs and cranks new perspectives for developing more effective exoskeletons based on biomechanical models.

User experience of lower extremity exoskeletons and its improvement methodologies: A narrative review

In this review, user experience (UX) of recent lower limb exoskeletons (LLEs) and its improvement methodologies are investigated. First, statistics based on standardised and custom UX evaluations are presented. It is indicated that, LLE users have positive UX, especially in the aspects of safety, dimension and effectiveness. Further, overall, UX levels of ankle and hip-knee exoskeletons are higher than those of other exoskeleton types; unilateral LLEs have higher mean UX levels than that of the bilateral ones. Then, design practices for improving UX are studied; the focused points are burden reduction and improvement of device fit. The former is achieved through lightweight design and approaches that reduce device's moment of inertia (MOI) at mechanical joints. Works on the latter refer to the endeavours to enhance static and dynamic fit; they mainly rely on the optimisations of human-robot interface (HRS) and endeavours to rectify misalignment of axes of mechanical and anatomic joints, respectively. The following section is control approaches to enhance wearing comfort level; it is mainly focused on adaptive, interaction and compensation-based controls. Finally, existing problems and future directions are stated and prospected respectively.

Muscle-controlled physics simulations of bird locomotion resolve the grounded running paradox

Humans and birds use very different running styles. Unlike humans, birds adopt "grounded running" at intermediate speeds-a running gait where at least one foot always maintains ground contact. Avian grounded running is a paradox: Animals usually minimize locomotor energy expenditure, but birds prefer grounded running despite incurring higher energy costs. Using predictive gait simulations of the emu (Dromaius novaehollandiae), we resolve this paradox by demonstrating that grounded running represents an optimal gait for birds, from both energetics and muscle excitations perspectives. Our virtual experiments decoupled effects of posture and tendon elasticity, biomechanically relevant anatomical features that cannot be isolated in real birds. The avian body plan prevents (near) vertical leg postures, making the running style used by humans impossible. Under this anatomical constraint, grounded running is optimal if the muscles produce the highest forces in crouched postures, as is true in most birds. Shared anatomical features suggest that, as a behavior, avian grounded running first evolved within non-avian dinosaurs.

Wearable Robot Design Optimization Using Closed-Form Human-Robot Dynamic Interaction Model

Wearable robots are emerging as a viable and effective solution for assisting and enabling people who suffer from balance and mobility disorders. Virtual prototyping is a powerful tool to design robots, preventing the costly iterative physical prototyping and testing. Design of wearable robots through modelling, however, often involves computationally expensive and error-prone multi-body simulations wrapped in an optimization framework to simulate human-robot-environment interactions. This paper proposes a framework to make the human-robot link segment system statically determinate, allowing for the closed-form inverse dynamics formulation of the link-segment model to be solved directly in order to simulate human-robot dynamic interactions. The paper also uses a technique developed by the authors to estimate the walking ground reactions from reference kinematic data, avoiding the need to measure them. The proposed framework is (a) computationally efficient and (b) transparent and easy to interpret, and (c) eliminates the need for optimization, detailed musculoskeletal modelling and measuring ground reaction forces for normal walking simulations. It is used to optimise the position of hip and ankle joints and the actuator torque-velocity requirements for a seven segments of a lower-limb wearable robot that is attached to the user at the shoes and pelvis. Gait measurements were carried out on six healthy subjects, and the data were used for design optimization and validation. The new technique promises to offer a significant advance in the way in which wearable robots can be designed.

Rethinking Exoskeleton Simulation-Based Design: The Effect of Using Different Cost Functions

Designing an exoskeleton that can improve user capabilities is a challenging task, and most designs rely on experiments to achieve this goal. A different approach is to use simulation-based designs to determine optimal device parameters. Most of these simulations use full trajectory tracking limb kinematics during a natural gait as a reference. However, exoskeletons typically change the natural gait kinematics of the user. Other types of simulations assume that human gait is optimized for a cost function that combines several objectives, such as the cost of transport, injury prevention, and stabilization. In this study, we use a 2D OpenSim model consisting of 10 degrees of freedom and considering 18 muscles, together with the Moco optimization tool, to investigate the differences between these two approaches with respect to running with a passive knee exoskeleton. Utilizing this model, we test the effect of a full trajectory tracking objective with different weights (representing the importance of the objective in the optimization cost function) and show that when using weights that are typically used in the literature, there is no deviation from the experimental data. Next, we develop a multi-objective cost function with foot clearance term based on peak knee angle during swing, that achieves trajectories similar (RMSE=7.4 deg) to experimental running data. Finally, we investigate the effect of different parameters in the design of a clutch-based passive knee exoskeleton (1.5 kg at each leg) and find that a design that utilizes a 2.5 Nm/deg spring achieves an improvement of up to 8% in net metabolic energy. Our results show that tracking objectives in the cost function, even with a low weight, hinders the simulation's ability to change the gait trajectory. Thus, there is a need for other predictive simulation methods for exoskeletons.

A dynamic foot model for predictive simulations of human gait reveals causal relations between foot structure and whole-body mechanics

The unique structure of the human foot is seen as a crucial adaptation for bipedalism. The foot's arched shape enables stiffening the foot to withstand high loads when pushing off, without compromising foot flexibility. Experimental studies demonstrated that manipulating foot stiffness has considerable effects on gait. In clinical practice, altered foot structure is associated with pathological gait. Yet, experimentally manipulating individual foot properties (e.g. arch height or tendon and ligament stiffness) is hard and therefore our understanding of how foot structure influences gait mechanics is still limited. Predictive simulations are a powerful tool to explore causal relationships between musculoskeletal properties and whole-body gait. However, musculoskeletal models used in three-dimensional predictive simulations assume a rigid foot arch, limiting their use for studying how foot structure influences three-dimensional gait mechanics. Here, we developed a four-segment foot model with a longitudinal arch for use in predictive simulations. We identified three properties of the ankle-foot complex that are important to capture ankle and knee kinematics, soleus activation, and ankle power of healthy adults: (1) compliant Achilles tendon, (2) stiff heel pad, (3) the ability to stiffen the foot. The latter requires sufficient arch height and contributions of plantar fascia, and intrinsic and extrinsic foot muscles. A reduced ability to stiffen the foot results in walking patterns with reduced push-off power. Simulations based on our model also captured the effects of walking with anaesthetised intrinsic foot muscles or an insole limiting arch compression. The ability to reproduce these different experiments indicates that our foot model captures the main mechanical properties of the foot. The presented four-segment foot model is a potentially powerful tool to study the relationship between foot properties and gait mechanics and energetics in health and disease.

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.

A novel computational framework for the estimation of internal musculoskeletal loading and muscle adaptation in hypogravity

Introduction: Spaceflight is associated with substantial and variable musculoskeletal (MSK) adaptations. Characterisation of muscle and joint loading profiles can provide key information to better align exercise prescription to astronaut MSK adaptations upon return-to-Earth. A case-study is presented of single-leg hopping in hypogravity to demonstrate the additional benefit computational MSK modelling has when estimating lower-limb MSK loading. Methods: A single male participant performed single-leg vertical hopping whilst attached to a body weight support system to replicate five gravity conditions (0.17, 0.25, 0.37, 0.50, 1 g). Experimental joint kinematics, joint kinetics and ground reaction forces were tracked in a data-tracking direct collocation simulation framework. Ground reaction forces, sagittal plane hip, knee and ankle net joint moments, quadriceps muscle forces (Rectus Femoris and three Vasti muscles), and hip, knee and ankle joint reaction forces were extracted for analysis. Estimated quadriceps muscle forces were input into a muscle adaptation model to predict a meaningful increase in muscle cross-sectional area, defined in (DeFreitas et al., 2011). Results: Two distinct strategies were observed to cope with the increase in ground reaction forces as gravity increased. Hypogravity was associated with an ankle dominant strategy with increased range of motion and net plantarflexor moment that was not seen at the hip or knee, and the Rectus Femoris being the primary contributor to quadriceps muscle force. At 1 g, all three joints had increased range of motion and net extensor moments relative to 0.50 g, with the Vasti muscles becoming the main muscles contributing to quadriceps muscle force. Additionally, hip joint reaction force did not increase substantially as gravity increased, whereas the other two joints increased monotonically with gravity. The predicted volume of exercise needed to counteract muscle adaptations decreased substantially with gravity. Despite the ankle dominant strategy in hypogravity, the loading on the knee muscles and joint also increased, demonstrating this provided more information about MSK loading. Discussion: This approach, supplemented with muscle-adaptation models, can be used to compare MSK loading between exercises to enhance astronaut exercise prescription.

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.

OpenCap: Human movement dynamics from smartphone videos

Measures of human movement dynamics can predict outcomes like injury risk or musculoskeletal disease progression. However, these measures are rarely quantified in large-scale research studies or clinical practice due to the prohibitive cost, time, and expertise required. Here we present and validate OpenCap, an open-source platform for computing both the kinematics (i.e., motion) and dynamics (i.e., forces) of human movement using videos captured from two or more smartphones. OpenCap leverages pose estimation algorithms to identify body landmarks from videos; deep learning and biomechanical models to estimate three-dimensional kinematics; and physics-based simulations to estimate muscle activations and musculoskeletal dynamics. OpenCap's web application enables users to collect synchronous videos and visualize movement data that is automatically processed in the cloud, thereby eliminating the need for specialized hardware, software, and expertise. We show that OpenCap accurately predicts dynamic measures, like muscle activations, joint loads, and joint moments, which can be used to screen for disease risk, evaluate intervention efficacy, assess between-group movement differences, and inform rehabilitation decisions. Additionally, we demonstrate OpenCap's practical utility through a 100-subject field study, where a clinician using OpenCap estimated musculoskeletal dynamics 25 times faster than a laboratory-based approach at less than 1% of the cost. By democratizing access to human movement analysis, OpenCap can accelerate the incorporation of biomechanical metrics into large-scale research studies, clinical trials, and clinical practice.

Modelling Physical Human-Robot Interface with Different Users, Cuffs, and Strapping Pressures: A Case Study

Assisting persons during physical therapy or augmenting their performance often requires precise delivery of an intervention. Robotic devices are perfectly placed to do so, but their intervention highly depends on the physical human-robot connection. The inherent compliance in the connection leads to delays and losses in bi-directional power transmission and can lead to human-robot joint axes misalignment. This is often neglected in the literature by assuming a rigid connection and has a negative impact on the intervention's effectiveness and robustness. This paper presents the preliminary results of a study that aims to close that gap. The study investigates what model forms and parameters best capture human-robot connection dynamics across different persons, connection designs (cuffs), and cuff strapping pressures. The results show that the linear spring-damper model is the best compromise, but its parameters must be adjusted for each individual and different conditions separately.

Minimum effort simulations of split-belt treadmill walking exploit asymmetry to reduce metabolic energy expenditure

Walking on a split-belt treadmill elicits an adaptation response that changes baseline step length asymmetry. The underlying causes of this adaptation, however, are difficult to determine. It has been proposed that effort minimization may drive this adaptation, based on the idea that adopting longer steps on the fast belt, or positive step length asymmetry (SLA), can cause the treadmill to exert net-positive mechanical work on a bipedal walker. However, humans walking on split-belt treadmills have not been observed to reproduce this behavior when allowed to freely adapt. To determine if an effort-minimization motor control strategy would result in experimentally observed adaptation patterns, we conducted simulations of walking on different combinations of belt speeds with a human musculoskeletal model that minimized muscle excitations and metabolic rate. The model adopted increasing amounts of positive SLA and decreased its net metabolic rate with increasing belt speed difference, reaching +42.4% SLA and -5.7% metabolic rate relative to tied-belt walking at our maximum belt speed ratio of 3:1. These gains were primarily enabled by an increase in braking work and a reduction in propulsion work on the fast belt. The results suggest that a purely effort minimization driven split-belt walking strategy would involve substantial positive SLA, and that the lack of this characteristic in human behavior points to additional factors influencing the motor control strategy, such as aversion to excessive joint loads, asymmetry, or instability.NEW & NOTEWORTHY Behavioral observations of split-belt treadmill adaptation have been inconclusive toward its underlying causes. To estimate gait patterns when driven exclusively by one of these possible underlying causes, we simulated split-belt treadmill walking with a musculoskeletal model that minimized its summed muscle excitations. Our model took significantly longer steps on the fast belt and reduced its metabolic rate below tied-belt walking, unlike experimental observations. This suggests that asymmetry is energetically optimal, but human adaptation involves additional factors.

Predictive simulation of single-leg landing scenarios for ACL injury risk factors evaluation

The Anterior Cruciate Ligament (ACL) rupture is a very common knee injury during sport activities. Landing after jump is one of the most prominent human body movements that can lead to such an injury. The landing-related ACL injury risk factors have been in the spotlight of research interest. Over the years, researchers and clinicians acquire knowledge about human movement during daily-life activities by organizing complex in vivo studies that feature high complexity, costs and technical and most importantly physical challenges. In an attempt to overcome these limitations, this paper introduces a computational modeling and simulation pipeline that aims to predict and identify key parameters of interest that are related to ACL injury during single-leg landings. We examined the following conditions: a) landing height, b) hip internal and external rotation, c) lumbar forward and backward leaning, d) lumbar medial and lateral bending, e) muscle forces permutations and f) effort goal weight. Identified on related research studies, we evaluated the following risk factors: vertical Ground Reaction Force (vGRF), knee joint Anterior force (AF), Medial force (MF), Compressive force (CF), Abduction moment (AbdM), Internal rotation moment (IRM), quadricep and hamstring muscle forces and Quadriceps/Hamstrings force ratio (Q/H force ratio). Our study clearly demonstrated that ACL injury is a rather complicated mechanism with many associated risk factors which are evidently correlated. Nevertheless, the results were mostly in agreement with other research studies regarding the ACL risk factors. The presented pipeline showcased promising potential of predictive simulations to evaluate different aspects of complicated phenomena, such as the ACL injury.

Modelling the interaction between wearable assistive devices and digital human models-A systematic review

Exoskeletons, orthoses, exosuits, assisting robots and such devices referred to as wearable assistive devices are devices designed to augment or protect the human body by applying and transmitting force. Due to the problems concerning cost- and time-consuming user tests, in addition to the possibility to test different configurations of a device, the avoidance of a prototype and many more advantages, digital human models become more and more popular for evaluating the effects of wearable assistive devices on humans. The key indicator for the efficiency of assistance is the interface between device and human, consisting mainly of the soft biological tissue. However, the soft biological tissue is mostly missing in digital human models due to their rigid body dynamics. Therefore, this systematic review aims to identify interaction modelling approaches between wearable assistive devices and digital human models and especially to study how the soft biological tissue is considered in the simulation. The review revealed four interaction modelling approaches, which differ in their accuracy to recreate the occurring interactions in reality. Furthermore, within these approaches there are some incorporating the appearing relative motion between device and human body due to the soft biological tissue in the simulation. The influence of the soft biological tissue on the force transmission due to energy absorption on the other side is not considered in any publication yet. Therefore, the development of an approach to integrate the viscoelastic behaviour of soft biological tissue in the digital human models could improve the design of the wearable assistive devices and thus increase its efficiency and efficacy.

Estimating lumbar spine loading when using back-support exoskeletons in lifting tasks

Low-back pain (LBP) continues as the leading cause of work-related musculoskeletal disorders, and the high LBP burden is attributed largely to physical risk factors prevalent in manual material handling tasks. Industrial back-support exoskeletons (BSEs) are a promising ergonomic intervention to help control/prevent exposures to such risk factors. While earlier research has demonstrated beneficial effects of BSEs in terms of reductions in superficial back muscle activity, limited evidence is available regarding the impacts of these devices on spine loads. We evaluated the effects of two passive BSEs (BackX™ AC and Laevo™ V2.5) on lumbosacral compression and shear forces during repetitive lifting using an optimization-based model. Eighteen participants (gender-balanced) completed four minutes of repetitive lifting in nine different conditions, involving symmetric and asymmetric postures when using the BSEs (along with no BSE as a control condition). Using both BSEs reduced estimated peak compression and anteroposterior shear forces (by ∼8-15%). Such reductions, however, were task-specific and depended on the BSE design. Laevo™ use reduced mediolateral shear forces during asymmetric lifting (by ∼35%). We also found that reductions in composite measures of trunk muscle activity may not correspond well with changes in spine forces when using a BSE. These results can help guide the proper selection and application of BSEs during repetitive lifting tasks. Future work is recommended to explore the viability of different biomechanical models to assess changes in spine mechanical loads when using BSEs and whether reasonable estimates would be obtained using such models.

Be Careful What You Wish for: Cost Function Sensitivity in Predictive Simulations for Assistive Device Design

Software packages that use optimization to predict the motion of dynamic systems are powerful tools for studying human movement. These “predictive simulations” are gaining popularity in parameter optimization studies for designing assistive devices such as exoskeletons. The cost function is a critical component of the optimization problem and can dramatically affect the solution. Many cost functions have been proposed that are biologically inspired and that produce reasonable solutions, but which may lead to different conclusions in some contexts. We used OpenSim Moco to generate predictive simulations of human walking using several cost functions, each of which produced a reasonable trajectory of the human model. We then augmented the model with motors that generated hip flexion, knee flexion, or ankle plantarflexion torques, and repeated the predictive simulations to determine the optimal motor torques. The model was assumed to be planar and bilaterally symmetric to reduce computation time. Peak torques varied from 41.3 to 79.0 N·m for the hip flexion motors, from 48.0 to 94.2 N·m for the knee flexion motors, and from 42.6 to 79.8 N·m for the ankle plantarflexion motors, which could have important design consequences. This study highlights the importance of evaluating the robustness of results from predictive simulations.

Movement in low gravity environments (MoLo) programme-The MoLo-L.O.O.P. study protocol

BACKGROUND

Exposure to prolonged periods in microgravity is associated with deconditioning of the musculoskeletal system due to chronic changes in mechanical stimulation. Given astronauts will operate on the Lunar surface for extended periods of time, it is critical to quantify both external (e.g., ground reaction forces) and internal (e.g., joint reaction forces) loads of relevant movements performed during Lunar missions. Such knowledge is key to predict musculoskeletal deconditioning and determine appropriate exercise countermeasures associated with extended exposure to hypogravity.

OBJECTIVES

The aim of this paper is to define an experimental protocol and methodology suitable to estimate in high-fidelity hypogravity conditions the lower limb internal joint reaction forces. State-of-the-art movement kinetics, kinematics, muscle activation and muscle-tendon unit behaviour during locomotor and plyometric movements will be collected and used as inputs (Objective 1), with musculoskeletal modelling and an optimisation framework used to estimate lower limb internal joint loading (Objective 2).

METHODS

Twenty-six healthy participants will be recruited for this cross-sectional study. Participants will walk, skip and run, at speeds ranging between 0.56-3.6 m/s, and perform plyometric movement trials at each gravity level (1, 0.7, 0.5, 0.38, 0.27 and 0.16g) in a randomized order. Through the collection of state-of-the-art kinetics, kinematics, muscle activation and muscle-tendon behaviour, a musculoskeletal modelling framework will be used to estimate lower limb joint reaction forces via tracking simulations.

CONCLUSION

The results of this study will provide first estimations of internal musculoskeletal loads associated with human movement performed in a range of hypogravity levels. Thus, our unique data will be a key step towards modelling the musculoskeletal deconditioning associated with long term habitation on the Lunar surface, and thereby aiding the design of Lunar exercise countermeasures and mitigation strategies.

Modifications to the net knee moments lead to the greatest improvements in accelerative sprinting performance: a predictive simulation study

The current body of sprinting biomechanics literature together with the front-side mechanics coaching framework provide various technique recommendations for improving performance. However, few studies have attempted to systematically explore technique modifications from a performance enhancement perspective. The aims of this investigation were therefore to explore how hypothetical technique modifications affect accelerative sprinting performance and assess whether the hypothetical modifications support the front-side mechanics coaching framework. A three-dimensional musculoskeletal model scaled to an international male sprinter was used in combination with direct collocation optimal control to perform (data-tracking and predictive) simulations of the preliminary steps of accelerative sprinting. The predictive simulations differed in the net joint moments that were left 'free' to change. It was found that the 'knee-free' and 'knee-hip-free' simulations resulted in the greatest performance improvements (13.8% and 21.9%, respectively), due to a greater knee flexor moment around touchdown (e.g., 141.2 vs. 70.5 Nm) and a delayed and greater knee extensor moment during stance (e.g., 188.5 vs. 137.5 Nm). Lastly, the predictive simulations which led to the greatest improvements were also found to not exhibit clear and noticeable front-side mechanics technique, thus the underpinning principles of the coaching framework may not be the only key aspect governing accelerative sprinting.

Patterns of asymmetry and energy cost generated from predictive simulations of hemiparetic gait

Hemiparesis, defined as unilateral muscle weakness, often occurs in people post-stroke or people with cerebral palsy, however it is difficult to understand how this hemiparesis affects movement patterns as it often presents alongside a variety of other neuromuscular impairments. Predictive musculoskeletal modeling presents an opportunity to investigate how impairments affect gait performance assuming a particular cost function. Here, we use predictive simulation to quantify the spatiotemporal asymmetries and changes to metabolic cost that emerge when muscle strength is unilaterally reduced and how reducing spatiotemporal symmetry affects metabolic cost. We modified a 2-D musculoskeletal model by uniformly reducing the peak isometric muscle force unilaterally. We then solved optimal control simulations of walking across a range of speeds by minimizing the sum of the cubed muscle excitations. Lastly, we ran additional optimizations to test if reducing spatiotemporal asymmetry would result in an increase in metabolic cost. Our results showed that the magnitude and direction of effort-optimal spatiotemporal asymmetries depends on both the gait speed and level of weakness. Also, the optimal speed was 1.25 m/s for the symmetrical and 20% weakness models but slower (1.00 m/s) for the 40% and 60% weakness models, suggesting that hemiparesis can account for a portion of the slower gait speed seen in people with hemiparesis. Modifying the cost function to minimize spatiotemporal asymmetry resulted in small increases (~4%) in metabolic cost. Overall, our results indicate that spatiotemporal asymmetry may be optimal for people with hemiparesis. Additionally, the effect of speed and the level of weakness on spatiotemporal asymmetry may help explain the well-known heterogenous distribution of spatiotemporal asymmetries observed in the clinic. Future work could extend our results by testing the effects of other neuromuscular impairments on optimal gait strategies, and therefore build a more comprehensive understanding of the gait patterns observed in clinical populations.

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.

Digital Guinea Pig: Merits and Methods of Human-in-the-Loop Simulation for Upper-Limb Exoskeletons

Exoskeletons operate in continuous haptic interaction with a human limb. Thus, this interaction is a key factor to consider during the development of hardware and control policies for these devices. Physics simulations can complement real-world experiments for prototype validation, leading to higher efficiency in hardware and software development iterations as well as increased safety for participants and robot hardware. Here, we present a simulation framework of the full rigid-body dynamics of a coupled human and exoskeleton arm built to validate the full software stack. We present a method to model the human-robot interaction dynamics as decoupled spring-damper systems based on anthropometric data. Further, we demonstrate the application of the simulation framework to predict the closed-loop haptic-rendering performance of a 9-DOF exoskeleton in interaction with a human. The simulation was capable of simulating the closed-loop system's reaction to an impact on a haptic wall. The intrusion into the compliant walls was predicted with a relative accuracy of 6% to 13%. Admissible control gains could be predicted with an accuracy of around 14%, and higher prediction accuracy is indicated when modeling the torque tracking bandwidth of the actuators. Hence, the simulation is valuable for validating prototype software, developing intuition, and a better understanding of the complex characteristics of the coupled system dynamics, even though the quantitative prediction is limited.

Relating Underlying Performance Objectives of Overground Walking to Observable Walking Mechanics using Predictive Musculoskeletal Simulations

There exists motor redundancy during human gait that allows individuals to perform the same task in different observable ways (i.e., with varied styles). However, how differences in observable walking mechanics depend on unique and underlying biomechanical objectives is unclear. As an example, these objectives could include metabolic energy consumption, sum of muscle activations, limb mechanical loading, balance and combinations thereof. In this study, we develop predictive neuromuscular simulations to investigate the relationships between these biomechanical objectives and observable mechanics during level walking. We simulated 3D normal walking of five healthy subjects, while optimizing each of the aforementioned objectives-resulting in 25 forward dynamics simulations for analysis. We compared the resulting joint kinematics and moments of different simulations. One of main findings suggests that decreased hip abduction angle is tightly related to when the regulation of dynamic balance (computed as whole-body angular momentum) is included in a movement cost function. We also find that increased joint moments are related to including metabolic cost (i.e., objectives associated with improving the energy economy of movement). Further, the timing of joint kinematics is adjusted for different performance objectives. These findings could guide the development of rehabilitation training and assistive devices that target specific individuals, tasks, and specific styles of movement.

Evaluating Knee Mechanisms for Assistive Devices

State-of-the-art knee braces use a polycentric mechanism with a predefined locus of the instantaneous center of rotation (centrode) and most exoskeleton devices use a knee mechanism with a single axis of rotation. However, human knees do not share a common centrode nor do they have a single axis. This leads to misalignment between the assistive device's joint axis and the user's knee axis, resulting in device migration and interaction forces, which can lead to sores, pain, and abandonment of the device over time. There has been some research into self-aligning knee mechanisms; however, there is a lack of consensus on the benefit of these mechanisms. There is no research that looked purely at the impact of the knee mechanisms, either. In this article, we compare three different knee brace mechanisms: single axis (SA), polycentric with predefined centrode (PPC), and polycentric with a self-aligning center of rotation (PSC). We designed and conducted an experiment to evaluate different joint mechanisms on device migration and interaction forces. Brace material, weight, size, cuff design, fitment location, and tightness were consistent across trials, making the knee joint mechanism the sole variable. The brace mechanisms had no significant effect on walking kinematics or kinetics. However, the PPC brace had greater interaction forces on the top brace strap than the SA and PSC. The PSC and SA had significantly lower interaction forces on the bottom strap compared to the PPC brace. The PSC had significantly less migration than both the SA and PPC braces. These results show that a PPC mechanism may not be beneficial for a wide range of users. This also shows that the PSC mechanisms may improve mechanism alignment and lessen device migration.

Design of a control framework for lower limb exoskeleton rehabilitation robot based on predictive assessment

BACKGROUND

Patients suffering from lower limb dyskinesia, especially in early stages of rehabilitation, have weak residual muscle strength in affected limb and require passive training by the lower limb rehabilitation robot. Anatomy indicates that the biceps femoris short head muscle has a strong influence on knee motion at the swing phase of walking. We sought to explore how it would influence on gait cycle in optimization framework. However, the training trajectory of conventional rehabilitation robots performing passive training usually follows gait planning based on general human gait data, which cannot simultaneously ensure both effective rehabilitation of affected limbs with varying severity pathological gait and comfort of the wearer within a safe motion trajectory.

METHODS

To elucidate the effects of weakness and contracture, we systematically introduced isolated defects into the musculoskeletal model and generated walking simulations to predict the gait adaptation due to these defects. An impedance control model of the rehabilitation robot is developed. Knee joint parameters optimized by predictive forward dynamics simulation are adopted as the expected values for the robot controller to achieve customized adjustment of the robot motion trajectory.

FINDINGS

Severe muscle contracture leads to severe knee flexion; severe muscle weakness induces a significant posterior tilt of the upper trunk, which hinders walking speed.

INTERPRETATION

Our simulation results attempt to reveal pathological gait features, which may help to reproduce the simulation of pathological gait. Furthermore, the robot simulation results show that the robot system achieves a speedy tracking by setting a larger stiffness value. The model also allows the implementation of different levels of damping or elasticity effects.

TRIAL REGISTRATION

The method proposed in this paper is an initial basic study that did not reach clinical trials and therefore retains retrospectively registered.

Human Joint Torque Estimation Based on Mechanomyography for Upper Extremity Exosuit

Human intention recognition belongs to the algorithm basis for exoskeleton robots to generate synergic movements and provide corresponding assistance. In this article, we acquire and analyze the mechanomyography (MMG) to estimate the current joint torque and apply this method to the rehabilitation training research of the upper extremity exosuit. In order to obtain relatively pure biological signals, a MMG processing method based on the Hilbert-Huang Transform (HHT) is proposed to eliminate the mixed noise and motion artifacts. After extracting features and forming the dataset, a random forest regression (RFR) model is designed to build the mapping relationship between MMG and human joint output through offline learning. In addition, an upper extremity exosuit is constructed for multi-joint assistance. Based on the above research, we develop a torque estimation-based control strategy and make it responsible for the intention understanding and motion servo of this customized system. Finally, an actual test verifies the accuracy and reliability of this recognition algorithm, and an efficiency evaluation experiment also proves the feasibility for power assistance.

Modeling toes contributes to realistic stance knee mechanics in three-dimensional predictive simulations of walking

Physics-based predictive simulations have been shown to capture many salient features of human walking. Yet they often fail to produce realistic stance knee and ankle mechanics. While the influence of the performance criterion on the predicted walking pattern has been previously studied, the influence of musculoskeletal mechanics has been less explored. Here, we investigated the influence of two mechanical assumptions on the predicted walking pattern: the complexity of the foot model and the stiffness of the Achilles tendon. We found, through three-dimensional muscle-driven predictive simulations of walking, that modeling the toes, and thus using two-segment instead of single-segment foot models, contributed to robustly eliciting physiological stance knee flexion angles, knee extension torques, and knee extensor activity. Modeling toes also slightly decreased the first vertical ground reaction force peak, increasing its agreement with experimental data, and improved stance ankle kinetics. It nevertheless slightly worsened predictions of ankle kinematics. Decreasing Achilles tendon stiffness improved the realism of ankle kinematics, but there remain large discrepancies with experimental data. Overall, this simulation study shows that not only the performance criterion but also mechanical assumptions affect predictive simulations of walking. Improving the realism of predictive simulations is required for their application in clinical contexts. Here, we suggest that using more complex foot models might contribute to such realism.

Evaluation of Optimal Control Approaches for Predicting Active Knee-Ankle-Foot-Orthosis Motion for Individuals With Spinal Cord Injury

Gait restoration of individuals with spinal cord injury can be partially achieved using active orthoses or exoskeletons. To improve the walking ability of each patient as much as possible, it is important to personalize the parameters that define the device actuation. This study investigates whether using an optimal control-based predictive simulation approach to personalize pre-defined knee trajectory parameters for an active knee-ankle-foot orthosis (KAFO) used by spinal cord injured (SCI) subjects could potentially be an alternative to the current trial-and-error approach. We aimed to find the knee angle trajectory that produced an improved orthosis-assisted gait pattern compared to the one with passive support (locked knee). We collected experimental data from a healthy subject assisted by crutches and KAFOs (with locked knee and with knee flexion assistance) and from an SCI subject assisted by crutches and KAFOs (with locked knee). First, we compared different cost functions and chose the one that produced results closest to experimental locked knee walking for the healthy subject (angular coordinates mean RMSE was 5.74°). For this subject, we predicted crutch-orthosis-assisted walking imposing a pre-defined knee angle trajectory for different maximum knee flexion parameter values, and results were evaluated against experimental data using that same pre-defined knee flexion trajectories in the real device. Finally, using the selected cost function, gait cycles for different knee flexion assistance were predicted for an SCI subject. We evaluated changes in four clinically relevant parameters: foot clearance, stride length, cadence, and hip flexion ROM. Simulations for different values of maximum knee flexion showed variations of these parameters that were consistent with experimental data for the healthy subject (e.g., foot clearance increased/decreased similarly in experimental and predicted motions) and were reasonable for the SCI subject (e.g., maximum parameter values were found for moderate knee flexion). Although more research is needed before this method can be applied to choose optimal active orthosis controller parameters for specific subjects, these findings suggest that optimal control prediction of crutch-orthosis-assisted walking using biomechanical models might be used in place of the trial-and-error method to select the best maximum knee flexion angle during gait for a specific SCI subject.

A model-free deep reinforcement learning approach for control of exoskeleton gait patterns

Lower-body exoskeleton control that adapts to users and provides assistance-as-needed can increase user participation and motor learning and allow for more effective gait rehabilitation. Adaptive model-based control methods have previously been developed to consider a user’s interaction with an exoskeleton; however, the predefined dynamics models required are challenging to define accurately, due to the complex dynamics and nonlinearities of the human-exoskeleton interaction. Model-free deep reinforcement learning (DRL) approaches can provide accurate and robust control in robotics applications and have shown potential for lower-body exoskeletons. In this paper, we present a new model-free DRL method for end-to-end learning of desired gait patterns for over-ground gait rehabilitation with an exoskeleton. This control technique is the first to accurately track any gait pattern desired in physiotherapy without requiring a predefined dynamics model and is robust to varying post-stroke individuals’ baseline gait patterns and their interactions and perturbations. Simulated experiments of an exoskeleton paired to a musculoskeletal model show that the DRL method is robust to different post-stroke users and is able to accurately track desired gait pattern trajectories both seen and unseen in training.

Development and Functional Testing of An Unloading Concept for Knee Osteoarthritis Patients: A Pilot Study

This paper presents a knee brace design that applies an extension moment to unload the muscles in stance phase during gait, and thereby the knee, as alternative to conventional valgus braces for knee osteoarthritis patients. The concept was tested on one healthy subject during normal gait with a prototype, which was designed to activate and deactivate in order to apply the extension moment in the stance phase only and hereby avoid any interference during the swing phase. Electromyography measurements and musculoskeletal models were used to evaluate the brace effects on muscle activation and knee compressive forces respectively. Simulations predicted an ideal reduction of up to 36%, whereas experimental tests revealed a reduction of up to 24% with the current prototype. The prototype brace also reduced the knee joint force impulse up to 9% and EMG peak signal of the vasti muscles with up to 19%. Due to these reductions on a healthy subject, this bracing approach seem promising for reducing knee loads during normal gait. However, further clinical experiments on knee osteoarthritis patients are required to evaluate the effect on both pain and disease progression.

Model-Based Comparison of Passive and Active Assistance Designs in an Occupational Upper Limb Exoskeleton for Overhead Lifting

OCCUPATIONAL APPLICATIONSIn recent years, various upper limb exoskeletons have been developed aiming to support industrial workers for a range of tasks and reduce risks of work-related musculoskeletal disorders. Most commercially available upper limb exoskeletons are passive systems that use compliant elements such as springs or elastic components to store and release energy to assist the user's motion. In contrast, many active exoskeletons, which are typically comprised of one or more powered actuators to provide joint assistance, are still in the research and development stages. Nevertheless, the functions and efficacy of various exoskeleton systems need to be further compared and assessed. This study presents a model-based approach to evaluate different designs of passive and active assistance and demonstrates the benefits of both assistance methods in an overhead lifting task. In addition, the modeling and simulation indicate the potential advantages of using the active assistance, based on electromyography.

Three-dimensional data-tracking simulations of sprinting using a direct collocation optimal control approach

Biomechanical simulation and modelling approaches have the possibility to make a meaningful impact within applied sports settings, such as sprinting. However, for this to be realised, such approaches must first undergo a thorough quantitative evaluation against experimental data. We developed a musculoskeletal modelling and simulation framework for sprinting, with the objective to evaluate its ability to reproduce experimental kinematics and kinetics data for different sprinting phases. This was achieved by performing a series of data-tracking calibration (individual and simultaneous) and validation simulations, that also featured the generation of dynamically consistent simulated outputs and the determination of foot-ground contact model parameters. The simulated values from the calibration simulations were found to be in close agreement with the corresponding experimental data, particularly for the kinematics (average root mean squared differences (RMSDs) less than 1.0° and 0.2 cm for the rotational and translational kinematics, respectively) and ground reaction force (highest average percentage RMSD of 8.1%). Minimal differences in tracking performance were observed when concurrently determining the foot-ground contact model parameters from each of the individual or simultaneous calibration simulations. The validation simulation yielded results that were comparable (RMSDs less than 1.0° and 0.3 cm for the rotational and translational kinematics, respectively) to those obtained from the calibration simulations. This study demonstrated the suitability of the proposed framework for performing future predictive simulations of sprinting, and gives confidence in its use to assess the cause-effect relationships of technique modification in relation to performance. Furthermore, this is the first study to provide dynamically consistent three-dimensional muscle-driven simulations of sprinting across different phases.

Modeling and Simulation of a Human Knee Exoskeleton's Assistive Strategies and Interaction

Exoskeletons are increasingly used in rehabilitation and daily life in patients with motor disorders after neurological injuries. In this paper, a realistic human knee exoskeleton model based on a physical system was generated, a human-machine system was created in a musculoskeletal modeling software, and human-machine interactions based on different assistive strategies were simulated. The developed human-machine system makes it possible to compute torques, muscle impulse, contact forces, and interactive forces involved in simulated movements. Assistive strategies modeled as a rotational actuator, a simple pendulum model, and a damped pendulum model were applied to the knee exoskeleton during simulated normal and fast gait. We found that the rotational actuator-based assistive controller could reduce the user's required physiological knee extensor torque and muscle impulse by a small amount, which suggests that joint rotational direction should be considered when developing an assistive strategy. Compared to the simple pendulum model, the damped pendulum model based controller made little difference during swing, but further decreased the user's required knee flexor torque during late stance. The trade-off that we identified between interaction forces and physiological torque, of which muscle impulse is the main contributor, should be considered when designing controllers for a physical exoskeleton system. Detailed information at joint and muscle levels provided in this human-machine system can contribute to the controller design optimization of assistive exoskeletons for rehabilitation and movement assistance.

OpenSim Moco: Musculoskeletal optimal control

Musculoskeletal simulations are used in many different applications, ranging from the design of wearable robots that interact with humans to the analysis of patients with impaired movement. Here, we introduce OpenSim Moco, a software toolkit for optimizing the motion and control of musculoskeletal models built in the OpenSim modeling and simulation package. OpenSim Moco uses the direct collocation method, which is often faster and can handle more diverse problems than other methods for musculoskeletal simulation. Moco frees researchers from implementing direct collocation themselves-which typically requires extensive technical expertise-and allows them to focus on their scientific questions. The software can handle a wide range of problems that interest biomechanists, including motion tracking, motion prediction, parameter optimization, model fitting, electromyography-driven simulation, and device design. Moco is the first musculoskeletal direct collocation tool to handle kinematic constraints, which enable modeling of kinematic loops (e.g., cycling models) and complex anatomy (e.g., patellar motion). To show the abilities of Moco, we first solved for muscle activity that produced an observed walking motion while minimizing squared muscle excitations and knee joint loading. Next, we predicted how muscle weakness may cause deviations from a normal walking motion. Lastly, we predicted a squat-to-stand motion and optimized the stiffness of an assistive device placed at the knee. We designed Moco to be easy to use, customizable, and extensible, thereby accelerating the use of simulations to understand the movement of humans and other animals.

In Silico-Enhanced Treatment and Rehabilitation Planning for Patients with Musculoskeletal Disorders: Can Musculoskeletal Modelling and Dynamic Simulations Really Impact Current Clinical Practice?

Over the past decades, the use of computational physics-based models representative of the musculoskeletal (MSK) system has become increasingly popular in many fields of clinically driven research, locomotor rehabilitation in particular. These models have been applied to various functional impairments given their ability to estimate parameters which cannot be readily measured in vivo but are of interest to clinicians. The use of MSK modelling and simulations allows analysis of relevant MSK biomarkers such as muscle and joint contact loading at a number of different stages in the clinical treatment pathway in order to benefit patient functional outcome. Applications of these methods include optimisation of rehabilitation programs, patient stratification, disease characterisation, surgical pre-planning, and assistive device and exoskeleton design and optimisation. This review provides an overview of current approaches, the components of standard MSK models, applications, limitations, and assumptions of these modelling and simulation methods, and finally proposes a future direction.