A real-time, 3-D musculoskeletal model for dynamic simulation of arm movements

The elbow is the load-bearing joint during arm swing

BACKGROUND

Arm swing plays a role in gait by accommodating forward movement through trunk balance. This study evaluates the biomechanical characteristics of arm swing during gait.

METHODS

The study performed computational musculoskeletal modeling based on motion tracking in 15 participants without musculoskeletal or gait disorder. A three-dimensional (3D) motion tracking system using three Azure Kinect (Microsoft) modules was used to obtain information in the 3D location of shoulder and elbow joints. Computational modeling using AnyBody Modeling System was performed to calculate the joint moment and range of motion (ROM) during arm swing.

RESULTS

The mean ROM of the dominant elbow was 29.7°±10.2° and 14.2°±3.2° in flexion-extension and pronation-supination, respectively. The mean joint moment of the dominant elbow was 56.4±12.7 Nm, 25.6±5.2 Nm, and 19.8±4.6 Nm in flexion-extension, rotation, and abduction-adduction, respectively.

CONCLUSIONS

The elbow bears the load created by gravity and muscle contracture in dynamic arm swing movement.

Visual programming for accessible interactive musculoskeletal models

Objective

Musculoskeletal modeling and simulation are powerful research and education tools in engineering, neuroscience, and rehabilitation. Interactive musculoskeletal models (IMMs) can be controlled by muscle activity recorded with electromyography (EMG). IMMs are typically coded using textual programming languages that present barriers to understanding for non-experts. The goal of this project was to use a visual programming language (Simulink) to create and test an IMM that is accessible to non-specialists for research and educational purposes.

Results

The developed IMM allows users to practice a goal-directed task with different control modes (keyboard, mouse, and EMG) and actuator types (muscle model, force generator, and torque generator). Example data were collected using both keyboard and EMG control. One male participant in his early 40’s performed a goal-directed task for four sequential trials using each control mode. For EMG control, the participant used a low-cost EMG system with consumer-grade EMG sensors and an Arduino microprocessor. The participant successfully performed the task with both control modes, but the inability to grade muscle model excitation and co-activate antagonist muscles limited performance with keyboard control. The IMM developed for this project serves as a foundation that can be further tailored to specific research and education needs.

Dynamic Analysis of Lower Limb Exoskeleton Motion and Control Using Differential Transform Method

In this study, the nonlinear dynamic analysis of the motion and control of the lower limb exoskeleton using differential transform method is presented. Devices for medical processes are continuously undergoing improvement such as enhancing and assisting automatic therapies with flexible and configurable programs for treating people with partial disability in lower limbs as applied in lower-limb exoskeleton. The configurable programs in this exoskeleton can be applied to observe and control the motion of the exoskeleton for effective physiotherapy and reduced rehabilitation time for patients with such disability. Hence, a two degree of freedom nonlinear dynamic model for the motion and control of the lower limb exoskeletons was developed for two links. The nonlinear dynamic models are solved by applying the differential transform method (DTM) and verified with the forth order Runge-Kutta numerical method (RK4). The effects of the applied torque on the two links are investigated and it is observed that Link 1 has large negative deflection amplitude that drives link 2 towards the opposite positive direction. An increase in the applied torque resulted in increase in the amplitude of the system for all initial condition considered. This in turns increases the nonlinear dynamic behavior of link 2 due to its lower mass value. The speed of both links dampens out over the history due to the presence of damping term. At equilibrium, both links are in phase and have the same amplitude over the time history. This study provides an analytical tool for observing and controlling the motions of the lower limb exoskeleton and for improving the designs of the medical device.

A segmented forearm model of hand pronation-supination approximates joint moments for real time applications

Musculoskeletal modeling is a new computational tool to reverse engineer human control systems, which require efficient algorithms running in real-time. Human hand pronation-supination movement is accomplished by movement of the radius and ulna bones relative to each other via the complex proximal and distal radioulnar joints, each with multiple degrees of freedom (DOFs). Here, we report two simplified models of this complex kinematic transformation implemented as a part of a 20 DOF model of the hand and forearm. The pronation/supination DOF was implemented as a single rotation joint either within the forearm segment or separating proximal and distal parts of the forearm segment. Torques produced by the inverse dynamic simulations with anatomical architecture of the forearm (OpenSim model) were used as the "gold standard" in the comparison of two simple models. Joint placement was iteratively optimized to achieve the closest representation of torques during realistic hand movements. The model with a split forearm segment performed better than the model with a solid forearm segment in simulating pronation/supination torques. We conclude that simplifying pronation/supination DOF as a single-axis rotation between arm segments is a viable strategy to reduce the complexity of multi-DOF dynamic simulations.

Simulating the effect of glenohumeral capsulorrhaphy on kinematics and muscle function

This study aimed to use a predictive simulation framework to examine shoulder kinematics, muscular effort, and task performance during functional upper limb movements under simulated selective glenohumeral capsulorrhaphy. A musculoskeletal model of the torso and upper limb was adapted to include passive restraints that simulated the changes in shoulder range of motion stemming from selective glenohumeral capsulorrhaphy procedures (anteroinferior, anterosuperior, posteroinferior, posterosuperior, and total anterior, inferior, posterior, and superior). Predictive muscle-driven simulations of three functional movements (upward reach, forward reach, and head touch) were generated with each model. Shoulder kinematics (elevation, elevation plane, and axial rotation), muscle cost (i.e., muscular effort), and task performance time were compared to a baseline model to assess the impact of the capsulorrhaphy procedures. Minimal differences in shoulder kinematics and task performance times were observed, suggesting that task performance could be maintained across the capsulorrhaphy conditions. Increased muscle cost was observed under the selective capsulorrhaphy conditions, however this was dependent on the task and capsulorrhaphy condition. Larger increases in muscle cost were observed under the capsulorrhaphy conditions that incurred the greatest reductions in shoulder range of motion (i.e., total inferior, total anterior, anteroinferior, and total posterior conditions) and during tasks that required shoulder kinematics closer to end range of motion (i.e., upward reach and head touch). The elevated muscle loading observed could present a risk to joint capsule repair. Appropriate rehabilitation following glenohumeral capsulorrhaphy is required to account for the elevated demands placed on muscles, particularly when a significant range of motion loss presents.

Evaluating the effects of arthroscopic Bankart repair and open Latarjet shoulder stabilisation procedures on shoulder joint neuromechanics and function: a single-centre, parallel-arm trial protocol

Introduction

Shoulder instability injuries are common in sports involving collisions and overhead movements. Arthroscopic Bankart repair and the open Latarjet are two commonly used surgical stabilisation procedures. There is a lack of knowledge surrounding movement strategies, joint loading and muscle strength after each of these procedures. This study will compare: (1) shoulder joint neuromechanics during activities of daily living and an overhead sporting task; (2) shoulder range of motion; (3) shoulder strength; and (4) self-reported shoulder function and health status, between individuals who have undergone an arthroscopic Bankart repair versus open Latarjet.

Methods and analysis

This is a prospective cohort, single-centre, non-randomised parallel arm study of surgical interventions for athletic shoulder instability injuries. Thirty participants will be recruited. Of these, 20 will have experienced one or more traumatic shoulder instability injuries requiring surgical stabilisation—and will undergo an arthroscopic Bankart repair or open Latarjet procedure. The remaining 10 participants will have no history of shoulder instability injury and act as controls. Participants will undergo baseline testing and be followed up at 3, 6 and 12 months. A two-way (group×time) analysis of variance with repeated measures on one factor (ie, time) will compare each outcome measure between groups across time points.

Ethics and dissemination

This study was approved by the Barwon Health and Deakin University Human Research Ethics Committees. Outcomes will be disseminated through publications in peer-reviewed journals and presentations at relevant scientific conferences.

Trial registration number

Australian and New Zealand Clinical Trials Registry (ACTRN12620000016932).

Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials

Computational models of the musculoskeletal system are scientific tools used to study human movement, quantify the effects of injury and disease, plan surgical interventions, or control realistic high-dimensional articulated prosthetic limbs. If the models are sufficiently accurate, they may embed complex relationships within the sensorimotor system. These potential benefits are limited by the challenge of implementing fast and accurate musculoskeletal computations. A typical hand muscle spans over 3 degrees of freedom (DOF), wrapping over complex geometrical constraints that change its moment arms and lead to complex posture-dependent variation in torque generation. Here, we report a method to accurately and efficiently calculate musculotendon length and moment arms across all physiological postures of the forearm muscles that actuate the hand and wrist. Then, we use this model to test the hypothesis that the functional similarities of muscle actions are embedded in muscle structure. The posture dependent muscle geometry, moment arms and lengths of modeled muscles were captured using autogenerating polynomials that expanded their optimal selection of terms using information measurements. The iterative process approximated 33 musculotendon actuators, each spanning up to 6 DOFs in an 18 DOF model of the human arm and hand, defined over the full physiological range of motion. Using these polynomials, the entire forearm anatomy could be computed in <10 μs, which is far better than what is required for real-time performance, and with low errors in moment arms (below 5%) and lengths (below 0.4%). Moreover, we demonstrate that the number of elements in these autogenerating polynomials does not increase exponentially with increasing muscle complexity; complexity increases linearly instead. Dimensionality reduction using the polynomial terms alone resulted in clusters comprised of muscles with similar functions, indicating the high accuracy of approximating models. We propose that this novel method of describing musculoskeletal biomechanics might further improve the applications of detailed and scalable models to describe human movement.

The community in the fields of biomechanics, neural engineering, and neuroscience has the need to understand and simulate realistic muscle actions in real-time. In biomechanics, the models of muscle structure have been of paramount importance for understanding the mechanical demands of movements. In neural engineering, the use of biomimetic control schemes require realistic and real-time computations with low latencies to achieve an intuitive interface with high-dimensional active prostheses or orthoses. In neuroscience, the new realization of the close relationship between neural computations and body mechanics has been promoted under the concept of neuromechanics. This concept has been instrumental in the understanding of neural computations for movement planning and execution. To enable the theoretical framework of neuromechanical computations embedded within musculoskeletal organization we propose a novel method for calculating muscle biomechanics in real-time with objective approximations that embed structural and functional attributes of simulated muscles. This description offers a scalable solution that accurately computes muscle kinematic states with real-time latencies surpassing the previous results by an order of magnitude.

An EMG-Driven Musculoskeletal Model for Estimating Continuous Wrist Motion

EMG-based continuous wrist joint motion estimation has been identified as a promising technique with huge potential in assistive robots. Conventional data-driven model-free methods tend to establish the relationship between the EMG signal and wrist motion using machine learning or deep learning techniques, but cannot interpret the functional relationship between neuro-commands and relevant joint motion. In this paper, an EMG-driven musculoskeletal model is proposed to estimate continuous wrist joint motion. This model interprets the muscle activation levels from EMG signals. A muscle-tendon model is developed to compute the muscle force during the voluntary flexion/extension movement, and a joint kinematic model is established to estimate the continuous wrist motion. To optimize the subject-specific physiological parameters, a genetic algorithm is designed to minimize the differences of joint motion prediction from the musculoskeletal model and joint motion measurement using motion data during training. Results show that mean root-mean-square-errors are 10.08°, 10.33°, 13.22° and 17.59° for single flexion/extension, continuous cycle and random motion trials, respectively. The mean coefficient of determination is over 0.9 for all the motion trials. The proposed EMG-driven model provides an accurate tracking performance based on user's intention.

Sensitivity of Neuromechanical Predictions to Choice of Glenohumeral Stability Modeling Approach

Most upper-extremity musculoskeletal models represent the glenohumeral joint with an inherently stable ball-and-socket, but the physiological joint requires active muscle coordination for stability. The authors evaluated sensitivity of common predicted outcomes (instability, net glenohumeral reaction force, and rotator cuff activations) to different implementations of active stabilizing mechanisms (constraining net joint reaction direction and incorporating normalized surface electromyography [EMG]). Both EMG and reaction force constraints successfully reduced joint instability. For flexion, incorporating any normalized surface EMG data reduced predicted instability by 54.8%, whereas incorporating any force constraint reduced predicted instability by 43.1%. Other outcomes were sensitive to EMG constraints, but not to force constraints. For flexion, incorporating normalized surface EMG data increased predicted magnitudes of joint reaction force and rotator cuff activations by 28.7% and 88.4%, respectively. Force constraints had no influence on these predicted outcomes for all tasks evaluated. More restrictive EMG constraints also tended to overconstrain the model, making it challenging to accurately track input kinematics. Therefore, force constraints may be a more robust choice when representing stability.

Predicting Perturbed Human Arm Movements in a Neuro-Musculoskeletal Model to Investigate the Muscular Force Response

Human movement is generated by a dynamic interplay between the nervous system, the biomechanical structures, and the environment. To investigate this interaction, we propose a neuro-musculoskeletal model of human goal-directed arm movements. Using this model, we simulated static perturbations of the inertia and damping properties of the arm, as well as dynamic torque perturbations for one-degree-of freedom movements around the elbow joint. The controller consists of a feed-forward motor command and feedback based on muscle fiber length and contraction velocity representing short-latency (25 ms) or long-latency (50 ms) stretch reflexes as the first neuronal responses elicited by an external perturbation. To determine the open-loop control signal, we parameterized the control signal resulting in a piecewise constant stimulation over time for each muscle. Interestingly, such an intermittent open-loop signal results in a smooth movement that is close to experimental observations. So, our model can generate the unperturbed point-to-point movement solely by the feed-forward command. The feedback only contributed to the stimulation in perturbed movements. We found that the relative contribution of this feedback is small compared to the feed-forward control and that the characteristics of the musculoskeletal system create an immediate and beneficial reaction to the investigated perturbations. The novelty of these findings is (1) the reproduction of static as well as dynamic perturbation experiments in one neuro-musculoskeletal model with only one set of basic parameters. This allows to investigate the model's neuro-muscular response to the perturbations that-at least to some degree-represent stereotypical interactions with the environment; (2) the demonstration that in feed-forward driven movements the muscle characteristics generate a mechanical response with zero-time delay which helps to compensate for the perturbations; (3) that this model provides enough biomechanical detail to allow for the prediction of internal forces, including joint loads and muscle-bone contact forces which are relevant in ergonomics and for the development of assistive devices but cannot be observed in experiments.

Muscle Contributions to Upper-Extremity Movement and Work From a Musculoskeletal Model of the Human Shoulder

Musculoskeletal models enable movement scientists to examine muscle function by computing the mechanical work done by muscles during motor tasks. To estimate muscle work accurately requires a model that is physiologically plausible. Previous models of the human shoulder have coupled scapula movement to humeral movement. While coupled movement produces a stereotypical scapulohumeral rhythm, it cannot model shrugging or independent movement of the scapula and humerus. The artificial coupling of humeral elevation to scapular rotation permits muscles that cross the glenohumeral joint, such as the rotator-cuff muscles and deltoids, to do implausible work to elevate and rotate the scapula. In reality, the motion of the scapula is controlled by thoracoscapular muscles, yet the roles of these muscles in shoulder function remains unclear. To elucidate the roles of the thoracoscapular muscles, we developed a shoulder model with an accurate scapulothoracic joint and includes scapular muscles to drive its motion. We used the model to compute the work done by the thoracoscapular muscles during shrugging and arm elevation. We found that the bulk of the work done in upper-extremity tasks is performed by the largest muscles of the shoulder: trapezius, deltoids, pectoralis major, and serratus-anterior. Trapezius and serratus anterior prove to be important synergists in performing upward-rotation of the scapula. We show that the large thoracoscapular muscles do more work than glenohumeral muscles during arm-elevation tasks. The model, experimental data and simulation results are freely available on SimTK.org to enable anyone to explore our results and to perform further studies in OpenSim 4.0.

Comparing EMG-Based Human-Machine Interfaces for Estimating Continuous, Coordinated Movements

Electromyography (EMG)-based interfaces are trending toward continuous, simultaneous control with multiple degrees of freedom. Emerging methods range from data-driven approaches to biomechanical model-based methods. However, there has been no direct comparison between these two types of continuous EMG-based interfaces. The aim of this study was to compare a musculoskeletal model (MM) with two data-driven approaches, linear regression (LR) and artificial neural network (ANN), for predicting continuous wrist and hand motions for EMG-based interfaces. Six able-bodied subjects and one transradial amputee subject performed (missing) metacarpophalangeal (MCP) and wrist flexion/extension, simultaneously or independently, while four EMG signals were recorded from forearm muscles. To add variation to the EMG signals, the subjects repeated the MCP and wrist motions at various upper extremity postures. For each subject, the EMG signals collected from the neutral posture were used to build the EMG interfaces; the EMG signals collected from all postures were used to evaluate the interfaces. The performance of the interface was quantified by Pearson's correlation coefficient (r) and the normalized root mean square error (NRMSE) between measured and estimated joint angles. The results demonstrated that the MM predicted movements more accurately, with higher r values and lower NRMSE, than either LR or ANN. Similar results were observed in the transradial amputee. Additionally, the variation in r across postures, an indicator of reliability against posture changes, was significantly lower (better) for the MM than for either LR or ANN. Our findings suggest that incorporating musculoskeletal knowledge into EMG-based human-machine interfaces could improve the estimation of continuous, coordinated motion.

Modeling of Motorized Orthosis Control

Orthotic devices are defined as externally applied devices that are used to modify the structural and functional characteristics of the neuro-muscular and skeletal systems. The aim of the current study is to improve the control and movement of a robotic arm orthosis by means of an intelligent optimization system. Firstly, the control problem settlement is defined with the muscle, brain, and arm model. Subsequently, the optimization control, which based on a differential evolution algorithm, is developed to calculate the optimum gain values. Additionally, a cost function is defined in order to control and minimize the effort that is made by the subject and to assure that the algorithm follows as close as possible the defined setpoint value. The results show that, with the optimization algorithm, the necessary development force of the muscles is close to zero and the neural excitation level of biceps and triceps signal values are getting lower with a gain increase. Furthermore, the necessary development force of the biceps muscle to overcome a load added to the orthosis control system is practically the half of the one that is necessary without the optimization algorithm.

Spatial Dependency of Glenohumeral Joint Stability during Dynamic Unimanual and Bimanual Pushing and Pulling

Degenerative wear to the glenoid from repetitive loading can reduce effective concavity depth and lead to future instability. Workspace design should consider glenohumeral stability to prevent initial wear. While stability has been previously explored for activities of daily living including push-pull tasks, whether stability is spatially dependent is unexplored. We simulated bimanual and unimanual push-pull tasks to 4 horizontal targets (planes of elevation: 0º, 45º, 90º, and 135º) at 90º thoracohumeral elevation and 3 elevation targets (thoracohumeral elevations: 20º, 90º, 170º) at 90º plane of elevation. The 45º horizontal target was most stable regardless of exertion type and would be the ideal target placement when considering stability. This target is likely more stable because the applied load acts perpendicular to the glenoid, limiting shear force production. The 135º horizontal target was particularly unstable for unimanual pushing (143% less stable than the 45º target), and the applied force acts parallel to the glenoid, likely creating shear forces. Pushing was less stable than pulling (all targets except sagittal 170º for both task types and horizontal 45º for bimanual) (p<0.01), which is consistent with prior reports. For example, unimanual pushing at the 90º horizontal target was 197% less stable than unimanual pulling. There were limited stability benefits to task placement for pushing, and larger stability benefits may be seen from converting pushing to pulling rather than optimizing task layout. There was no difference in stability between bimanual and unimanual tasks, suggesting no stability benefit to bimanual operation.

Robust simultaneous myoelectric control of multiple degrees of freedom in wrist-hand prostheses by real-time neuromusculoskeletal modeling

OBJECTIVE

Robotic prosthetic limbs promise to replace mechanical function of lost biological extremities and restore amputees' capacity of moving and interacting with the environment. Despite recent advances in biocompatible electrodes, surgical procedures, and mechatronics, the impact of current solutions is hampered by the lack of intuitive and robust man-machine interfaces.

APPROACH

This work presents a biomimetic interface that synthetizes the musculoskeletal function of an individual's phantom limb as controlled by neural surrogates, i.e. electromyography-derived neural activations. With respect to current approaches based on machine learning, our method employs explicit representations of the musculoskeletal system to reduce the space of feasible solutions in the translation of electromyograms into prosthesis control commands. Electromyograms are mapped onto mechanical forces that belong to a subspace contained within the broader operational space of an individual's musculoskeletal system.

MAIN RESULTS

Our results show that this constraint makes the approach applicable to real-world scenarios and robust to movement artefacts. This stems from the fact that any control command must always exist within the musculoskeletal model operational space and be therefore physiologically plausible. The approach was effective both on intact-limbed individuals and a transradial amputee displaying robust online control of multi-functional prostheses across a large repertoire of challenging tasks.

SIGNIFICANCE

The development and translation of man-machine interfaces that account for an individual's neuromusculoskeletal system creates unprecedented opportunities to understand how disrupted neuro-mechanical processes can be restored or replaced via biomimetic wearable assistive technologies.

Current Trends in Metallic Orthopedic Biomaterials: From Additive Manufacturing to Bio-Functionalization, Infection Prevention, and Beyond

There has been a growing interest in metallic biomaterials during the last five years, as recent developments in additive manufacturing (=3D printing), surface bio-functionalization techniques, infection prevention strategies, biodegradable metallic biomaterials, and composite biomaterials have provided many possibilities to develop biomaterials and medical devices with unprecedented combinations of favorable properties and advanced functionalities. Moreover, development of biomaterials is no longer separated from the other branches of biomedical engineering, particularly tissue biomechanics, musculoskeletal dynamics, and image processing aspects of skeletal radiology. In this editorial, I will discuss all the above-mentioned topics, as they constitute some of the most important trends of research on metallic biomaterials. This editorial will, therefore, serve as a foreword to the papers appearing in a special issue covering the current trends in metallic biomaterials.

Fast Forward-Dynamics Tracking Simulation: Application to Upper Limb and Shoulder Modeling

OBJECTIVE

Musculoskeletal simulation can be used to estimate muscle forces in clinical movement studies. However, such simulations typically only target movement measurements and are not applicable to force exertion tasks which are commonly used in rehabilitation therapy. Simulations can also produce nonphysiological joint forces or be too slow for real-time clinical applications, such as rehabilitation with real-time feedback. The objective of this study is to propose and evaluate a new formulation of forward-dynamics assisted tracking simulation that incorporates measured reaction forces as targets or constraints without any additional computational cost.

METHODS

We illustrate our method with idealized proof-of-concept models and evaluate it with two upper limb cases: Tracking of hand reaction forces during an isometric force-generation task and constraining glenohumeral joint reaction forces for stability during arm elevation.

RESULTS

We show that the addition of reaction force optimization terms within our simulations generates plausible muscle force predictions for these tasks, which are strongly related to reaction forces in addition to movement. Execution times for all models tested were not different when run with or without the reaction force optimization term, ensuring that the simulations are fast enough for real-time clinical applications.

CONCLUSION

Our novel reaction force optimization term leads to more realistic shoulder reaction forces, without any additional computational costs.

SIGNIFICANCE

Our formulation is not only valuable for shoulder simulations, but could be used in various clinical situations (e.g., for different joints and rehabilitation therapy tasks) where the direction and/or magnitude of reaction forces are of interest.

B. G, João F. Influence of the Musculotendon Dynamics on the Muscle Force-Sharing Problem of the Shoulder—A Fully Inverse Dynamics Approach

Most dynamic simulations are based on inverse dynamics, being the time-dependent physiological nature of the muscle properties rarely considered due to numerical challenges. Since the influence of muscle physiology on the consistency of inverse dynamics simulations remains unclear, the purpose of the present study is to evaluate the computational efficiency and biological validity of four musculotendon models that differ in the simulation of the muscle activation and contraction dynamics. Inverse dynamic analyses are performed using a spatial musculoskeletal model of the upper limb. The muscle force-sharing problem is solved for five repetitions of unloaded and loaded motions of shoulder abduction and shoulder flexion. The performance of the musculotendon models is evaluated by comparing muscle activation predictions with electromyography (EMG) signals, measured synchronously with motion for 11 muscles, and the glenohumeral joint reaction forces estimated numerically with those measured in vivo. The results show similar muscle activations for all muscle models. Overall, high cross-correlations are computed between muscle activations and the EMG signals measured for all movements analyzed, which provides confidence in the results. The glenohumeral joint reaction forces estimated compare well with those measured in vivo, but the influence of the muscle dynamics is found to be negligible. In conclusion, for slow-speed, standard movements of the upper limb, as those studied here, the activation and musculotendon contraction dynamics can be neglected in inverse dynamic analyses without compromising the prediction of muscle and joint reaction forces.

Evaluating Muscle Activation Models for Elbow Motion Estimation

Adoption of wearable assistive technologies relies heavily on improvement of existing control system models. Knowing which models to use and how to improve them is difficult to determine due to the number of proposed solutions with relatively little broad comparisons. One type of these models, muscle activation models, describes the nonlinear relationship between neural inputs and mechanical activation of the muscle. Many muscle activation models can be found in the literature, but no comparison is available to guide the community on limitations and improvements. In this research, an EMG-driven elbow motion model is developed for the purpose of evaluating muscle activation models. Seven muscle activation models are used in an optimization procedure to determine which model has the best performance. Root mean square errors in muscle torque estimation range from 1.67–2.19 Nm on average over varying input trajectories. The computational resource demand was also measured during the optimization procedure, as it is an important aspect for determining if a model is feasible for use in a particular wearable assistive device. This study provides insight into the ability of these models to estimate elbow motion and the trade-off between estimation accuracy and computational demand.

Muscle function in glenohumeral joint stability during lifting task

Ensuring glenohumeral stability during repetitive lifting tasks is a key factor to reduce the risk of shoulder injuries. Nevertheless, the literature reveals some lack concerning the assessment of the muscles that ensure glenohumeral stability during specific lifting tasks. Therefore, the purpose of this study was to assess the stabilization function of shoulder muscles during a lifting task. Kinematics and muscle electromyograms (n = 9) were recorded from 13 healthy adults during a bi-manual lifting task performed from the hip to the shoulder level. A generic upper-limb OpenSim model was implemented to simulate glenohumeral stability and instability by performing static optimizations with and without glenohumeral stability constraints. This procedure enabled to compute the level of shoulder muscle activity and forces in the two conditions. Without the stability constraint, the simulated movement was unstable during 74%±16% of the time. The force of the supraspinatus was significantly increased of 107% (p<0.002) when the glenohumeral stability constraint was implemented. The increased supraspinatus force led to greater compressive force (p<0.001) and smaller shear force (p<0.001), which contributed to improved glenohumeral stability. It was concluded that the supraspinatus may be the main contributor to glenohumeral stability during lifting task.

Elucidating Sensorimotor Control Principles with Myoelectric Musculoskeletal Models

There is an old saying that you must walk a mile in someone's shoes to truly understand them. This mini-review will synthesize and discuss recent research that attempts to make humans "walk a mile" in an artificial musculoskeletal system to gain insight into the principles governing human movement control. In this approach, electromyography (EMG) is used to sample human motor commands; these commands serve as inputs to mathematical models of muscular dynamics, which in turn act on a model of skeletal dynamics to produce a simulated motor action in real-time (i.e., the model's state is updated fast enough produce smooth motion without noticeable transitions; Manal et al., 2002). In this mini-review, these are termed myoelectric musculoskeletal models (MMMs). After a brief overview of typical MMM design and operation principles, the review will highlight how MMMs have been used for understanding human sensorimotor control and learning by evoking apparent alterations in a user's biomechanics, neural control, and sensory feedback experiences.

An Interactive Simulator for Imposing Virtual Musculoskeletal Dynamics

OBJECTIVE

disease processes are often marked by both neural and muscular changes that alter movement control and execution, but these adaptations are difficult to tease apart because they occur simultaneously. This is addressed by swapping an individual's limb dynamics with a neurally controlled facsimile using an interactive musculoskeletal simulator (IMS) that allows controlled modifications of musculoskeletal dynamics. This paper details the design and operation of the IMS, quantifies and describes human adaptation to the IMS, and determines whether the IMS allows users to move naturally, a prerequisite for manipulation experiments.

METHODS

healthy volunteers (n = 4) practiced a swift goal-directed task (back-and-forth elbow flexion/extension) for 90 trials with the IMS off (normal dynamics) and 240 trials with the IMS on, i.e., the actions of a user's personalized electromyography-driven musculoskeletal model are robotically imposed back onto the user.

RESULTS

after practicing with the IMS on, subjects could complete the task with end-point errors of 1.56°, close to the speed-matched IMS-off error of 0.57°. Muscle activity, joint torque, and arm kinematics for IMS-on and -off conditions were well matched for three subjects (root-mean-squared error [RMSE] = 0.16 N·m), but the error was higher for one subject with a small stature (RMSE = 0.25 N·m).

CONCLUSION

a well-matched musculoskeletal model allowed IMS users to perform a goal-directed task nearly as well as when the IMS was not active.

SIGNIFICANCE

this advancement permits real-time manipulations of musculoskeletal dynamics, which could increase our understanding of muscular and neural co-adaptations to injury, disease, disuse, and aging.

Robust Real-Time Musculoskeletal Modeling Driven by Electromyograms

OBJECTIVE

Current clinical biomechanics involves lengthy data acquisition and time-consuming offline analyses with biomechanical models not operating in real-time for man-machine interfacing. We developed a method that enables online analysis of neuromusculoskeletal function in vivo in the intact human.

METHODS

We used electromyography (EMG)-driven musculoskeletal modeling to simulate all transformations from muscle excitation onset (EMGs) to mechanical moment production around multiple lower-limb degrees of freedom (DOFs). We developed a calibration algorithm that enables adjusting musculoskeletal model parameters specifically to an individual's anthropometry and force-generating capacity. We incorporated the modeling paradigm into a computationally efficient, generic framework that can be interfaced in real-time with any movement data collection system.

RESULTS

The framework demonstrated the ability of computing forces in 13 lower-limb muscle-tendon units and resulting moments about three joint DOFs simultaneously in real-time. Remarkably, it was capable of extrapolating beyond calibration conditions, i.e., predicting accurate joint moments during six unseen tasks and one unseen DOF.

CONCLUSION

The proposed framework can dramatically reduce evaluation latency in current clinical biomechanics and open up new avenues for establishing prompt and personalized treatments, as well as for establishing natural interfaces between patients and rehabilitation systems.

SIGNIFICANCE

The integration of EMG with numerical modeling will enable simulating realistic neuromuscular strategies in conditions including muscular/orthopedic deficit, which could not be robustly simulated via pure modeling formulations. This will enable translation to clinical settings and development of healthcare technologies including real-time bio-feedback of internal mechanical forces and direct patient-machine interfacing.

Musculoskeletal model-based control interface mimics physiologic hand dynamics during path tracing task

OBJECTIVE

We investigated the feasibility of a novel, customizable, simplified EMG-driven musculoskeletal model for estimating coordinated hand and wrist motions during a real-time path tracing task.

APPROACH

A two-degree-of-freedom computational musculoskeletal model was implemented for real-time EMG-driven control of a stick figure hand displayed on a computer screen. After 5-10 minutes of undirected practice, subjects were given three attempts to trace 10 straight paths, one at a time, with the fingertip of the virtual hand. Able-bodied subjects completed the task on two separate test days.

MAIN RESULTS

Across subjects and test days, there was a significant linear relationship between log-transformed measures of accuracy and speed (Pearson's r  =  0.25, p  <  0.0001). The amputee subject could coordinate movement between the wrist and MCP joints, but favored metacarpophalangeal joint motion more highly than able-bodied subjects in 8 of 10 trials. For able-bodied subjects, tracing accuracy was lower at the extremes of the model's range of motion, though there was no apparent relationship between tracing accuracy and fingertip location for the amputee. Our result suggests that, unlike able-bodied subjects, the amputee's motor control patterns were not accustomed to the multi-joint dynamics of the wrist and hand, possibly as a result of post-amputation cortical plasticity, disuse, or sensory deficits.

SIGNIFICANCE

To our knowledge, our study is one of very few that have demonstrated the real-time simultaneous control of multi-joint movements, especially wrist and finger movements, using an EMG-driven musculoskeletal model, which differs from the many data-driven algorithms that dominate the literature on EMG-driven prosthesis control. Real-time control was achieved with very little training and simple, quick (~15 s) calibration. Thus, our model is potentially a practical and effective control platform for multifunctional myoelectric prostheses that could restore more life-like hand function for individuals with upper limb amputation.

Lumped-parameter electromyogram-driven musculoskeletal hand model: A potential platform for real-time prosthesis control

Simple, lumped-parameter musculoskeletal models may be more adaptable and practical for clinical real-time control applications, such as prosthesis control. In this study, we determined whether a lumped-parameter, EMG-driven musculoskeletal model with four muscles could predict wrist and metacarpophalangeal (MCP) joint flexion/extension. Forearm EMG signals and joint kinematics were collected simultaneously from 5 able-bodied (AB) subjects. For one subject with unilateral transradial amputation (TRA), joint kinematics were collected from the sound arm during bilateral mirrored motion. Twenty-two model parameters were optimized such that joint kinematics predicted by EMG-driven forward dynamic simulation closely matched measured kinematics. Cross validation was employed to evaluate the model kinematic predictions using Pearson׳s correlation coefficient (r). Model predictions of joint angles were highly to very highly positively correlated with measured values at the wrist (AB mean r=0.94, TRA r=0.92) and MCP (AB mean r=0.88, TRA r=0.93) joints during single-joint wrist and MCP movements, respectively. In simultaneous multi-joint movement, the prediction accuracy for TRA at the MCP joint decreased (r=0.56), while r-values derived from AB subjects and TRA wrist motion were still above 0.75. Though parameters were optimized to match experimental sub-maximal kinematics, passive and maximum isometric joint moments predicted by the model were comparable to reported experimental measures. Our results showed the promise of a lumped-parameter musculoskeletal model for hand/wrist kinematic estimation. Therefore, the model might be useful for EMG control of powered upper limb prostheses, but more work is needed to demonstrate its online performance.

Simple EMG-driven musculoskeletal model enables consistent control performance during path tracing tasks

Consistent, robust performance is critical for the utility and user-acceptance of neurally-controlled powered upper limb prostheses. We preliminarily evaluated the performance consistency of an electromyography (EMG)-driven controller based on a two degree-of-freedom musculoskeletal hand model, whose simplified structure is more practical for real-time prosthesis control than existing, complex models. Parameters of four virtual muscles were computed by numerical optimization from an able-bodied subject's kinematic and EMG data collected during wrist and metacarpophalangeal (MCP) flexion/extension movements. The subject attempted to trace a series of paths of different complexity (straight and curved) with the fingertip of a virtual hand displayed on a computer screen; the straight-path tracing tasks were repeated on a second test day to evaluate performance consistency over time. The subject's tracing accuracy during the tasks was consistent both between tasks of varying complexity (i.e. straight vs curved) and between test days when tracing the straight paths. Additionally, task duration, straightness, and smoothness did not significantly differ between the two straight-path test days. The consistent performance between days was achieved even with a very short (~15 seconds) calibration period to re-normalize EMG. The subject also coordinated movements of the wrist and MCP joints simultaneously during the task, much like with healthy, intact limb movement. Our promising results suggest that a musculoskeletal model-based controller may provide consistent and effective performance across a range of operating conditions, making it potentially practical for prosthesis control. Further research is needed to determine whether musculoskeletal model-based control (1) is effective for executing real-world tasks, and (2) can be extended to populations with neuromuscular impairment (e.g. amputation).

A Biomechanical Model of the Scapulothoracic Joint to Accurately Capture Scapular Kinematics during Shoulder Movements

The complexity of shoulder mechanics combined with the movement of skin relative to the scapula makes it difficult to measure shoulder kinematics with sufficient accuracy to distinguish between symptomatic and asymptomatic individuals. Multibody skeletal models can improve motion capture accuracy by reducing the space of possible joint movements, and models are used widely to improve measurement of lower limb kinematics. In this study, we developed a rigid-body model of a scapulothoracic joint to describe the kinematics of the scapula relative to the thorax. This model describes scapular kinematics with four degrees of freedom: 1) elevation and 2) abduction of the scapula on an ellipsoidal thoracic surface, 3) upward rotation of the scapula normal to the thoracic surface, and 4) internal rotation of the scapula to lift the medial border of the scapula off the surface of the thorax. The surface dimensions and joint axes can be customized to match an individual’s anthropometry. We compared the model to “gold standard” bone-pin kinematics collected during three shoulder tasks and found modeled scapular kinematics to be accurate to within 2mm root-mean-squared error for individual bone-pin markers across all markers and movement tasks. As an additional test, we added random and systematic noise to the bone-pin marker data and found that the model reduced kinematic variability due to noise by 65% compared to Euler angles computed without the model. Our scapulothoracic joint model can be used for inverse and forward dynamics analyses and to compute joint reaction loads. The computational performance of the scapulothoracic joint model is well suited for real-time applications; it is freely available for use with OpenSim 3.2, and is customizable and usable with other OpenSim models.

An optimized proportional-derivative controller for the human upper extremity with gravity

When Functional Electrical Stimulation (FES) is used to restore movement in subjects with spinal cord injury (SCI), muscle stimulation patterns should be selected to generate accurate and efficient movements. Ideally, the controller for such a neuroprosthesis will have the simplest architecture possible, to facilitate translation into a clinical setting. In this study, we used the simulated annealing algorithm to optimize two proportional-derivative (PD) feedback controller gain sets for a 3-dimensional arm model that includes musculoskeletal dynamics and has 5 degrees of freedom and 22 muscles, performing goal-oriented reaching movements. Controller gains were optimized by minimizing a weighted sum of position errors, orientation errors, and muscle activations. After optimization, gain performance was evaluated on the basis of accuracy and efficiency of reaching movements, along with three other benchmark gain sets not optimized for our system, on a large set of dynamic reaching movements for which the controllers had not been optimized, to test ability to generalize. Robustness in the presence of weakened muscles was also tested. The two optimized gain sets were found to have very similar performance to each other on all metrics, and to exhibit significantly better accuracy, compared with the three standard gain sets. All gain sets investigated used physiologically acceptable amounts of muscular activation. It was concluded that optimization can yield significant improvements in controller performance while still maintaining muscular efficiency, and that optimization should be considered as a strategy for future neuroprosthesis controller design.

Real-time simulation of three-dimensional shoulder girdle and arm dynamics

Electrical stimulation is a promising technology for the restoration of arm function in paralyzed individuals. Control of the paralyzed arm under electrical stimulation, however, is a challenging problem that requires advanced controllers and command interfaces for the user. A real-time model describing the complex dynamics of the arm would allow user-in-the-loop type experiments where the command interface and controller could be assessed. Real-time models of the arm previously described have not included the ability to model the independently controlled scapula and clavicle, limiting their utility for clinical applications of this nature. The goal of this study therefore was to evaluate the performance and mechanical behavior of a real-time, dynamic model of the arm and shoulder girdle. The model comprises seven segments linked by eleven degrees of freedom and actuated by 138 muscle elements. Polynomials were generated to describe the muscle lines of action to reduce computation time, and an implicit, first-order Rosenbrock formulation of the equations of motion was used to increase simulation step-size. The model simulated flexion of the arm faster than real time, simulation time being 92% of actual movement time on standard desktop hardware. Modeled maximum isometric torque values agreed well with values from the literature, showing that the model simulates the moment-generating behavior of a real human arm. The speed of the model enables experiments where the user controls the virtual arm and receives visual feedback in real time. The ability to optimize potential solutions in simulation greatly reduces the burden on the user during development.

Computational models of upper-limb motion during functional reaching tasks for application in FES-based stroke rehabilitation

Functional electrical stimulation (FES) has been shown to be an effective approach to upper-limb stroke rehabilitation, where it is used to assist arm and shoulder motion. Model-based FES controllers have recently confirmed significant potential to improve accuracy of functional reaching tasks, but they typically require a reference trajectory to track. Few upper-limb FES control schemes embed a computational model of the task; however, this is critical to ensure the controller reinforces the intended movement with high accuracy. This paper derives computational motor control models of functional tasks that can be directly embedded in real-time FES control schemes, removing the need for a predefined reference trajectory. Dynamic models of the electrically stimulated arm are first derived, and constrained optimisation problems are formulated to encapsulate common activities of daily living. These are solved using iterative algorithms, and results are compared with kinematic data from 12 subjects and found to fit closely (mean fitting between 63.2% and 84.0%). The optimisation is performed iteratively using kinematic variables and hence can be transformed into an iterative learning control algorithm by replacing simulation signals with experimental data. The approach is therefore capable of controlling FES in real time to assist tasks in a manner corresponding to unimpaired natural movement. By ensuring that assistance is aligned with voluntary intention, the controller hence maximises the potential effectiveness of future stroke rehabilitation trials.

Benchmarking of dynamic simulation predictions in two software platforms using an upper limb musculoskeletal model

Several opensource or commercially available software platforms are widely used to develop dynamic simulations of movement. While computational approaches are conceptually similar across platforms, technical differences in implementation may influence output. We present a new upper limb dynamic model as a tool to evaluate potential differences in predictive behavior between platforms. We evaluated to what extent differences in technical implementations in popular simulation software environments result in differences in kinematic predictions for single and multijoint movements using EMG- and optimization-based approaches for deriving control signals. We illustrate the benchmarking comparison using SIMM-Dynamics Pipeline-SD/Fast and OpenSim platforms. The most substantial divergence results from differences in muscle model and actuator paths. This model is a valuable resource and is available for download by other researchers. The model, data, and simulation results presented here can be used by future researchers to benchmark other software platforms and software upgrades for these two platforms.

Implanted neuroprosthesis for restoring arm and hand function in people with high level tetraplegia

OBJECTIVE

To develop and apply an implanted neuroprosthesis to restore arm and hand function to individuals with high level tetraplegia.

DESIGN

Case study.

SETTING

Clinical research laboratory.

PARTICIPANTS

Individuals with spinal cord injuries (N=2) at or above the C4 motor level.

INTERVENTIONS

The individuals were each implanted with 2 stimulators (24 stimulation channels and 4 myoelectric recording channels total). Stimulating electrodes were placed in the shoulder and arm, being, to our knowledge, the first long-term application of spiral nerve cuff electrodes to activate a human limb. Myoelectric recording electrodes were placed in the head and neck areas.

MAIN OUTCOME MEASURES

Successful installation and operation of the neuroprosthesis and electrode performance, range of motion, grasp strength, joint moments, and performance in activities of daily living.

RESULTS

The neuroprosthesis system was successfully implanted in both individuals. Spiral nerve cuff electrodes were placed around upper extremity nerves and activated the intended muscles. In both individuals, the neuroprosthesis has functioned properly for at least 2.5 years postimplant. Hand, wrist, forearm, elbow, and shoulder movements were achieved. A mobile arm support was needed to support the mass of the arm during functional activities. One individual was able to perform several activities of daily living with some limitations as a result of spasticity. The second individual was able to partially complete 2 activities of daily living.

CONCLUSIONS

Functional electrical stimulation is a feasible intervention for restoring arm and hand functions to individuals with high tetraplegia. Forces and movements were generated at the hand, wrist, elbow, and shoulder that allowed the performance of activities of daily living, with some limitations requiring the use of a mobile arm support to assist the stimulated shoulder forces.

The lacertus fibrosus of the biceps brachii muscle: an anatomical study

PURPOSE

The lacertus fibrosus (LF) is involved in various surgical procedures. However, the anatomy, morphometry, topography and biomechanical involvements of LF are not clear. The purpose of this study was to determine the anatomical and morphometric variations of LF, and to correlate this with anthropometric and morphometric measurements of the upper limb. Furthermore, the presence or absence of a deep layer of LF was verified using forearm cross-sections and dissections.

METHODS

This anatomical study was performed by observation of dissections and transverse sections obtained from 50 cadavers. Morphometric analyses [length and width of LF and biceps tendon, stature, length of upper limb, forearm, bi-epicondylar width, forearm perimeter, biceps brachii muscle perimeter (BBm)] were also performed.

RESULTS

The results demonstrated that there was no significant correlation between LF morphology and morphometric upper limb measurements. The deep layer of LF was observed in all specimens.

CONCLUSION

Results of this paper indicate that the LF presents individual characteristics such as length and width. The deeper layer of LF was observed on all specimens. The possible role of LF in force transmission during flexion, BBm moment arm adjustment and supination reduction is discussed in view of these results.

Speaking and cognitive distractions during EEG-based brain control of a virtual neuroprosthesis-arm

Background

Brain-computer interface (BCI) systems have been developed to provide paralyzed individuals the ability to command the movements of an assistive device using only their brain activity. BCI systems are typically tested in a controlled laboratory environment were the user is focused solely on the brain-control task. However, for practical use in everyday life people must be able to use their brain-controlled device while mentally engaged with the cognitive responsibilities of daily activities and while compensating for any inherent dynamics of the device itself. BCIs that use electroencephalography (EEG) for movement control are often assumed to require significant mental effort, thus preventing users from thinking about anything else while using their BCI. This study tested the impact of cognitive load as well as speaking on the ability to use an EEG-based BCI.

Findings

Six participants controlled the two-dimensional (2D) movements of a simulated neuroprosthesis-arm under three different levels of cognitive distraction. The two higher cognitive load conditions also required simultaneously speaking during BCI use. On average, movement performance declined during higher levels of cognitive distraction, but only by a limited amount. Movement completion time increased by 7.2%, the percentage of targets successfully acquired declined by 11%, and path efficiency declined by 8.6%. Only the decline in percentage of targets acquired and path efficiency were statistically significant (p < 0.05).

Conclusion

People who have relatively good movement control of an EEG-based BCI may be able to speak and perform other cognitively engaging activities with only a minor drop in BCI-control performance.

Real-time control of an interactive impulsive virtual prosthesis

An interactive virtual dynamic environment for testing control strategies for neural machine interfacing with artificial limbs offers several advantages. The virtual environment is low-cost, easily configured, and offers a wealth of data for post-hoc analysis compared with real physical prostheses and robots. For use with prosthetics and research involving amputee subjects it allows the valuable time with the subject to be spent in experiments rather than fixing hardware issues. The usefulness of the virtual environment increases as the realism of the environment increases. Most tasks performed with limbs require interactions with objects in the environment. To simulate these tasks the dynamics of frictional contact, in addition to inertial limb dynamics must be modeled. Here, subjects demonstrate real-time control of an eight degree-of-freedom virtual prosthesis while performing an interactive box-and-blocks task. With practice, four nonamputee subjects and one shoulder disarticulation subject were able to successfully transfer blocks in the virtual environment at an average rate of just under two blocks per minute. The virtual environment is configurable in terms of the virtual arm design, control strategy, and task.

A real-time system for biomechanical analysis of human movement and muscle function

Mechanical analysis of movement plays an important role in clinical management of neurological and orthopedic conditions. There has been increasing interest in performing movement analysis in real-time, to provide immediate feedback to both therapist and patient. However, such work to date has been limited to single-joint kinematics and kinetics. Here we present a software system, named human body model (HBM), to compute joint kinematics and kinetics for a full body model with 44 degrees of freedom, in real-time, and to estimate length changes and forces in 300 muscle elements. HBM was used to analyze lower extremity function during gait in 12 able-bodied subjects. Processing speed exceeded 120 samples per second on standard PC hardware. Joint angles and moments were consistent within the group, and consistent with other studies in the literature. Estimated muscle force patterns were consistent among subjects and agreed qualitatively with electromyography, to the extent that can be expected from a biomechanical model. The real-time analysis was integrated into the D-Flow system for development of custom real-time feedback applications and into the gait real-time analysis interactive lab system for gait analysis and gait retraining.

Clinical applications of musculoskeletal modelling for the shoulder and upper limb

Musculoskeletal models have been developed to estimate internal loading on the human skeleton, which cannot directly be measured in vivo, from external measurements like kinematics and external forces. Such models of the shoulder and upper extremity have been used for a variety of purposes, ranging from understanding basic shoulder biomechanics to assisting in preoperative planning. In this review, we provide an overview of the most commonly used large-scale shoulder and upper extremity models and categorise the applications of these models according to the type of questions their users aimed to answer. We found that the most explored feature of a model is the possibility to predict the effect of a structural adaptation on functional outcome, for instance, to simulate a tendon transfer preoperatively. Recent studies have focused on minimising the mismatch in morphology between the model, often derived from cadaver studies, and the subject that is analysed. However, only a subset of the parameters that describe the model's geometry and, perhaps most importantly, the musculotendon properties can be obtained in vivo. Because most parameters are somehow interrelated, the others should be scaled to prevent inconsistencies in the model's structure, but it is not known exactly how. Although considerable effort is put into adding complexity to models, for example, by making them subject-specific, we have found little evidence of their superiority over current models. The current trend in development towards individualised, more complex models needs to be justified by demonstrating their ability to answer questions that cannot already be answered by existing models.

Musculoskeletal shoulder models: A technical review and proposals for research foci

Musculoskeletal shoulder models allow non-invasive prediction of parameters that cannot be measured, particularly the loading applied to morphological structures and neurological control. This insight improves treatment and avoidance of pathology and performance evaluation and optimisation. A lack of appropriate validation and knowledge of model parameters’ accuracy may cause reduced clinical success for these models. Instrumented implants have recently been used to validate musculoskeletal models, adding important information to the literature. This development along with increasing prevalence of shoulder models necessitates a fresh review of available models and their utility. The practical uses of models are described. Accuracy of model inputs, modelling techniques and model sensitivity is the main technical review undertaken. Collection and comparison of these parameters are vital to understanding disagreement between model outputs. Trends in shoulder modelling are highlighted: validation through instrumented prostheses, increasing openness and strictly constrained, optimised, measured kinematics. Future directions are recommended: validation through focus on model sub-sections, increased subject specificity with imaging techniques determining muscle and body segment parameters and through different scaling and kinematics optimisation approaches.

Prediction of imagined single-joint movements in a person with high-level tetraplegia

Cortical neuroprostheses for movement restoration require developing models for relating neural activity to desired movement. Previous studies have focused on correlating single-unit activities (SUA) in primary motor cortex to volitional arm movements in able-bodied primates. The extent of the cortical information relevant to arm movements remaining in severely paralyzed individuals is largely unknown. We record intracortical signals using a microelectrode array chronically implanted in the precentral gyrus of a person with tetraplegia, and estimate positions of imagined single-joint arm movements. Using visually guided motor imagery, the participant imagined performing eight distinct single-joint arm movements, while SUA, multispike trains (MSP), multiunit activity, and local field potential time (LFPrms), and frequency signals (LFPstft) were recorded. Using linear system identification, imagined joint trajectories were estimated with 20-60% variance explained, with wrist flexion/extension predicted the best and pronation/supination the poorest. Statistically, decoding of MSP and LFPstft yielded estimates that equaled those of SUA. Including multiple signal types in a decoder increased prediction accuracy in all cases. We conclude that signals recorded from a single restricted region of the precentral gyrus in this person with tetraplegia contained useful information regarding the intended movements of upper extremity joints.

Using virtual reality driving simulators in persons with spinal cord injury: three screen display versus head mounted display

PURPOSE

Virtual reality (VR) is a relatively new technology that is currently utilized in a wide variety of settings to test and train individuals in specialized skills. This study examines methods for improving driver retraining protocols for persons with spinal cord injury (SCI).

METHOD

We compared a VR driving simulator, under two different display conditions, a head mounted display (HMD) and a three screen display (TSD) to identify the best method for retraining driving skills following SCI.

RESULTS

Although there was minimal evidence for driving performance difficulties in the HMD condition relative to the TSD condition (e.g. greater number of times for being off course and longer stopping latencies for the HMD condition), rates of simulator sickness did not differ between display conditions.

CONCLUSIONS

Taken together, findings suggest that both the HMD and the TSD are reasonable simulator options for driver retraining in SCI.

Passive elastic properties of the rat ankle

Passive properties of muscles and tendons, including their elasticity, have been suggested to influence motor control. We examine here the potential role of passive elastic muscle properties at the rat ankle joint, focusing on their potential to specify an equilibrium position of the ankle. We measured the position-dependent passive torques at the rat ankle before and after sequential cuts of flexor (a.k.a. dorsiflexor) and extensor (a.k.a. plantarflexor) ankle muscles. We found that there was a passive equilibrium position of the ankle that shifted systematically with the cuts, demonstrating that the passive torques produced by ankle flexor and extensor muscles work in opposition in order to maintain a stable equilibrium. The mean equilibrium position of the intact rat ankle ranged from 9.3° to 15.7° in extension relative to the orthogonal position, depending on the torque metric. The mean shift in equilibrium position due to severing extensors ranged from 4.4° to 7.7°, and the mean shift due to severing flexors was smaller, ranging from 0.9° to 2.5°. The restoring torques generated by passive elasticity are large enough (approximately 1.5-5 mNm for displacements of 18° from equilibrium) to affect ankle movement during the swing phase of locomotion, and the asymmetry of larger extension vs. flexion torques is consistent with weight support, demonstrating the importance of accounting for passive muscle properties when considering the neural control of movement.

Analysis of elbow-joints misalignment in upper-limb exoskeleton

This paper presents advantages of introducing elbow-joints misalignments in an exoskeleton for upper limb rehabilitation. Typical exoskeletons are characterized by axes of the device as much as possible aligned to the rotational axes of human articulations. This approach leads to advantages in terms of movements and torques decoupling, but can lead to limitations nearby the elbow singular configuration. A proper elbow axes misalignment between the exoskeleton and the human can improve the quality of collaborative rehabilitation therapies, in which a correct torque transmission from human articulations to mechanical joints of the device is required to react to torques generated by the patient.

Control of a time-delayed 5 degrees of freedom arm model for use in upper extremity functional electrical stimulation

The goal of this work is to design a controller for a functional electrical stimulation (FES) neuroprosthesis aimed at restoring shoulder and elbow function in individuals who have suffered a high-level cervical (C3-C4) spinal cord injury (SCI). The controller is a mathematical algorithm that coordinates the electrical stimulations applied to the paralyzed muscles such that the arm closely tracks a given desired trajectory. An issue that so far has received little attention is that of time-delays. These delays arise from two sources: (1) the muscle excitation-activation dynamics (10-30 ms) and (2) the sampling of the electrical stimulation (80 ms at the typical 12 Hz stimulation frequency). Using a 5 degrees of freedom (5 DOF) arm model we designed and evaluated a novel controller capable of maintaining stable and accurate tracking performance in the presence of time-delays. For a desired trajectory consisting of 10 randomized reaches, the controller achieved excellent tracking performance as measured by the root-mean-square error (RMSE) between the desired and simulated joint angles (RMSE= [1.48°; 0.81°; 2.14°; 3.11°; 2.29°]).

Estimation of musculoskeletal models from in situ measurements of muscle action in the rat hindlimb

Musculoskeletal models are often created by making detailed anatomical measurements of muscle properties. These measurements can then be used to determine the parameters of canonical models of muscle action. We describe here a complementary approach for developing and validating muscle models, using in situ measurements of muscle actions. We characterized the actions of two rat hindlimb muscles: the gracilis posticus (GRp) and the posterior head of biceps femoris (BFp; excluding the anterior head with vertebral origin). The GRp is a relatively simple muscle, with a circumscribed origin and insertion. The BFp is more complex, with an insertion distributed along the tibia. We measured the six-dimensional isometric forces and moments at the ankle evoked from stimulating each muscle at a range of limb configurations. The variation of forces and moments across the workspace provides a succinct characterization of muscle action. We then used this data to create a simple muscle model with a single point insertion and origin. The model parameters were optimized to best explain the observed force-moment data. This model explained the relatively simple muscle, GRp, very well (R(2)>0.85). Surprisingly, this simple model was also able to explain the action of the BFp, despite its greater complexity (R(2)>0.84). We then compared the actions observed here with those predicted using recently published anatomical measurements. Although the forces and moments predicted for the GRp were very similar to those observed here, the predictions for the BFp differed. These results show the potential utility of the approach described here for the development and refinement of musculoskeletal models based on in situ measurements of muscle actions.

Continuous neuronal ensemble control of simulated arm reaching by a human with tetraplegia

Functional electrical stimulation (FES), the coordinated electrical activation of multiple muscles, has been used to restore arm and hand function in people with paralysis. User interfaces for such systems typically derive commands from mechanically unrelated parts of the body with retained volitional control, and are unnatural and unable to simultaneously command the various joints of the arm. Neural interface systems, based on spiking intracortical signals recorded from the arm area of motor cortex, have shown the ability to control computer cursors, robotic arms and individual muscles in intact non-human primates. Such neural interface systems may thus offer a more natural source of commands for restoring dexterous movements via FES. However, the ability to use decoded neural signals to control the complex mechanical dynamics of a reanimated human limb, rather than the kinematics of a computer mouse, has not been demonstrated. This study demonstrates the ability of an individual with long-standing tetraplegia to use cortical neuron recordings to command the real-time movements of a simulated dynamic arm. This virtual arm replicates the dynamics associated with arm mass and muscle contractile properties, as well as those of an FES feedback controller that converts user commands into the required muscle activation patterns. An individual with long-standing tetraplegia was thus able to control a virtual, two-joint, dynamic arm in real time using commands derived from an existing human intracortical interface technology. These results show the feasibility of combining such an intracortical interface with existing FES systems to provide a high-performance, natural system for restoring arm and hand function in individuals with extensive paralysis.

Muscle parameters for musculoskeletal modelling of the human neck

BACKGROUND

To study normal or pathological neuromuscular control, a musculoskeletal model of the neck has great potential but a complete and consistent anatomical dataset which comprises the muscle geometry parameters to construct such a model is not yet available.

METHODS

A dissection experiment was performed on the left side of one 50th percentile male embalmed specimen. Geometrical data including muscle attachment sites were digitized using an Optotrak measurement system and laser diffraction was used to determine muscle sarcomere lengths. Bony landmarks were recorded and joint centres of rotation between different vertebrae were estimated using literature data.

FINDINGS

A total of 34 muscle parts of the neck were divided in 129 elements per body side. Muscle attachment sites, mass, physiological cross sectional area, fibre length, tendon length and optimal fibre length for each element are supplied as digital annexes to the paper. Results are coherent with other studies and new data are provided for several smaller muscles not reported elsewhere.

INTERPRETATION

Implementation of this dataset into a neck model is likely to improve the estimation of muscle forces and thus increase the model validity; this makes future neck models more suitable for the use as clinical tools.