Model-based approach for human kinematics reconstruction from markerless and marker-based motion analysis systems

Modeling tools related to the musculoskeletal system have been previously developed. However, the integration of the real underlying functional joint behavior is lacking and therefore available kinematic models do not reasonably replicate individual human motion. In order to improve our understanding of the relationships between muscle behavior, i.e. excursion and motion data, modeling tools must guarantee that the model of joint kinematics is correctly validated to ensure meaningful muscle behavior interpretation. This paper presents a model-based method that allows fusing accurate joint kinematic information with motion analysis data collected using either marker-based stereophotogrammetry (MBS) (i.e. bone displacement collected from reflective markers fixed on the subject's skin) or markerless single-camera (MLS) hardware. This paper describes a model-based approach (MBA) for human motion data reconstruction by a scalable registration method for combining joint physiological kinematics with limb segment poses. The presented results and kinematics analysis show that model-based MBS and MLS methods lead to physiologically-acceptable human kinematics. The proposed method is therefore available for further exploitation of the underlying model that can then be used for further modeling, the quality of which will depend on the underlying kinematic model.     

Implementation of controlling strategy in a biomechanical lower limb model with active muscles for coupling multibody dynamics and finite element analysis

Computational biomechanics for human body modeling has generally been categorized into two separated domains: finite element analysis and multibody dynamics. Combining the advantages of both domains is necessary when tissue stress and physical body motion are both of interest. However, the method for this topic is still in exploration. The aim of this study is to implement unique controlling strategies in finite element model for simultaneously simulating musculoskeletal body dynamics and in vivo stress inside human tissues. A finite element lower limb model with 3D active muscles was selected for the implementation of controlling strategies, which was further validated against in-vivo human motion experiments. A unique feedback control strategy that couples together a basic Proportion-Integration-Differentiation (PID) controller and generic active signals from Computed Muscle Control (CMC) method of the musculoskeletal model or normalized EMG singles was proposed and applied in the present model. The results show that the new proposed controlling strategy show a good correlation with experimental test data of the normal gait considering joint kinematics, while stress distribution of local lower limb tissue can be also detected in real-time with lower limb motion. In summary, the present work is the first step for the application of active controlling strategy in the finite element model for concurrent simulation of both body dynamics and tissue stress. In the future, the present method can be further developed to apply it in various fields for human biomechanical analysis to monitor local stress and strain distribution by simultaneously simulating human locomotion.     

A method for measurement of joint kinematics in vivo by registration of 3-D geometric models with cine phase contrast magnetic resonance imaging data

A new method is presented for measuring joint kinematics by optimally matching modeled trajectories of geometric surface models of bones with cine phase contrast (cine-PC) magnetic resonance imaging data. The incorporation of the geometric bone models (GBMs) allows computation of kinematics based on coordinate systems placed relative to full 3-D anatomy, as well as quantification of changes in articular contact locations and relative velocities during dynamic motion. These capabilities are additional to those of cine-PC based techniques that have been used previously to measure joint kinematics during activity. Cine-PC magnitude and velocity data are collected on a fixed image plane prescribed through a repetitively moved skeletal joint. The intersection of each GBM with a simulated image plane is calculated as the model moves along a computed trajectory, and cine-PC velocity data are sampled from the regions of the velocity images within the area of this intersection. From the sampled velocity data, the instantaneous linear and angular velocities of a coordinate system fixed to the GBM are estimated, and integration of the linear and angular velocities is used to predict updated trajectories. A moving validation phantom that produces motions and velocity data similar to those observed in an experiment on human knee kinematics was designed. This phantom was used to assess cine-PC rigid body tracking performance by comparing the kinematics of the phantom measured by this method to similar measurements made using a magnetic tracking system. Average differences between the two methods were measured as 2.82 mm rms for anterior/posterior tibial position, and 2.63 deg rms for axial rotation. An intertrial repeatability study of human knee kinematics using the new method produced rms differences in anterior/posterior tibial position and axial rotation of 1.44 mm and 2.35 deg. The performance of the method is concluded to be sufficient for the effective study of kinematic changes caused to knees by soft tissue injuries.     

Accuracy and repeatability of joint angles measured using a single camera markerless motion capture system

Markerless motion capture systems have developed in an effort to evaluate human movement in a natural setting. However, the accuracy and reliability of these systems remain understudied. Therefore, the goals of this study were to quantify the accuracy and repeatability of joint angles using a single camera markerless motion capture system and to compare the markerless system performance with that of a marker-based system. A jig was placed in multiple static postures with marker trajectories collected using a ten camera motion analysis system. Depth and color image data were simultaneously collected from a single Microsoft Kinect camera, which was subsequently used to calculate virtual marker trajectories. A digital inclinometer provided a measure of ground-truth for sagittal and frontal plane joint angles. Joint angles were calculated with marker data from both motion capture systems using successive body-fixed rotations. The sagittal and frontal plane joint angles calculated from the marker-based and markerless system agreed with inclinometer measurements by <0.5°. The systems agreed with each other by <0.5° for sagittal and frontal plane joint angles and <2° for transverse plane rotation. Both systems showed a coefficient of reliability <0.5° for all angles. These results illustrate the feasibility of a single camera markerless motion capture system to accurately measure lower extremity kinematics and provide a first step in using this technology to discern clinically relevant differences in the joint kinematics of patient populations.     

Optimality in forward dynamics simulations

This paper discusses a methodology and an algorithm for the analysis of dynamics of bio-mechanical systems, and in particular of optimal movement patterns between initial and target configurations. The basic formulation utilizes a finite element time discretization and establishes a large set of equations in displacements and forces. These are solved simultaneously for the whole time interval considered. The algorithm allows different optimization criteria for the movement, based on either the smoothness of the movement or the minimization of needed controls or control rates. It is primarily aimed at musculoskeletal simulations with either the joint resultant moments or the redundant muscular tensions as unknowns. Kinetic and kinematic constraints can be introduced for the obtained movement. Examples show that the obtained results are strongly dependent on the optimality criterion used. Systematic usage of the algorithm can improve knowledge about optimal motion planning.     

A Novel Application of Musculoskeletal Ultrasound Imaging

Ultrasound is an attractive modality for imaging muscle and tendon motion during dynamic tasks and can provide a complementary methodological approach for biomechanical studies in a clinical or laboratory setting. Towards this goal, methods for quantification of muscle kinematics from ultrasound imagery are being developed based on image processing. The temporal resolution of these methods is typically not sufficient for highly dynamic tasks, such as drop-landing. We propose a new approach that utilizes a Doppler method for quantifying muscle kinematics. We have developed a novel vector tissue Doppler imaging (vTDI) technique that can be used to measure musculoskeletal contraction velocity, strain and strain rate with sub-millisecond temporal resolution during dynamic activities using ultrasound. The goal of this preliminary study was to investigate the repeatability and potential applicability of the vTDI technique in measuring musculoskeletal velocities during a drop-landing task, in healthy subjects. The vTDI measurements can be performed concurrently with other biomechanical techniques, such as 3D motion capture for joint kinematics and kinetics, electromyography for timing of muscle activation and force plates for ground reaction force. Integration of these complementary techniques could lead to a better understanding of dynamic muscle function and dysfunction underlying the pathogenesis and pathophysiology of musculoskeletal disorders.     

Study of the joint configuration of the knee using a morpho-functional analysis

Skin marker motion analyses are the most widespread techniques to study human movements. Nevertheless, trajectories obtained through such methods are biased because of soft tissue artifacts and lead, consequently, to false collisions and dislocations when bone motion is under investigation. It's an open challenge to enhance kinematics curves particularly for the knee joint involved in the mechanics of gait. The kinematics of flexion/extension of the knee is classically modeled by a rotation around a fixed axis. However, the trend of current biomechanical studies is to improve this modeling by introducing a morphological knowledge such as ligament constraints. In this paper, we propose to highlight the morpho-functionnal link on this joint thanks to two contributions. The first one consists in proposing a method capable of extracting a kinematics of flexion/extension of the knee from a unique CT scan. This method is based on the determination of a mobile axis capable of keeping the information of rolling/sliding. The second one consists in a qualitative and quantitative temporal analysis of the position of the bones during the movement. We compare the results of the two kinematics (static and mobile axis) using original figures of articular coherence and an associated index.     

A model of handwriting

The research reported here is concerned with hand trajectory planning for the class of movements involved in handwriting. Previous studies show that the kinematics of human two-joint arm movements in the horizontal plane can be described by a model which is based on dynamic minimization of the square of the third derivative of hand position (jerk), integrated over the entire movement. We extend this approach to both the analysis and the synthesis of the trajectories occurring in the generation of handwritten characters. Several basic strokes are identified and possible stroke concatenation rules are suggested. Given a concise symbolic representation of a stroke shape, a simple algorithm computes the complete kinematic specification of the corresponding trajectory. A handwriting generation model based on a kinematics from shape principle and on dynamic optimization is formulated and tested. Good qualitative and quantitative agreement was found between subject recordings and trajectories generated by the model. The simple symbolic representation of hand motion suggested here may permit the central nervous system to learn, store and modify motor action plans for writing in an efficient manner.     

Bone position estimation from skin marker co-ordinates using global optimisation with joint constraints

Widespread use of gait or motion analysis in the diagnosis of patients with locomotor pathology and the subsequent planning and assessment of treatment has been limited because of its reliability, particularly in evaluating frontal and transverse plane components. This is because spatial reconstruction of the musculoskeletal system and calculation of its kinematics and kinetics via a skin marker-based multi-link model are subject to marker skin movement artefacts. Traditional methods treat each body segment separately without imposing joint constraints, resulting in apparent dislocations at joints predominantly because of skin movement artefacts. An optimisation method for the determination of the positions and orientations of multi-link musculoskeletal models from marker co-ordinates is presented. It is based on the minimisation of the weighted sum of squared distances between measured and model-determined marker positions. The model imposes joint constraints. Numerical experiments were performed to show that the new method is capable of eliminating joint dislocations and giving more accurate model position and orientation estimations. It is suggested that, with joint constraints and a global error compensation scheme, the effects of measurement errors on the reconstruction of the musculoskeletal system and subsequent mechanical analyses can be reduced globally. The proposed method minimises errors in axial rotation and ab/adduction at the joints and may extend the applicability of gait analysis to clinical problems.     

A computer vision based method for 3D posture estimation of symmetrical lifting

Work-related musculoskeletal disorders (WMSD) are commonly observed among the workers involved in material handling tasks such as lifting. To improve work place safety, it is necessary to assess musculoskeletal and biomechanical risk exposures associated with these tasks. Such an assessment has been mainly conducted using surface marker-based methods, which is time consuming and tedious. During the past decade, computer vision based pose estimation techniques have gained an increasing interest and may be a viable alternative for surface marker-based human movement analysis. The aim of this study is to develop and validate a computer vision based marker-less motion capture method to assess 3D joint kinematics of lifting tasks. Twelve subjects performing three types of symmetrical lifting tasks were filmed from two views using optical cameras. The joints kinematics were calculated by the proposed computer vision based motion capture method as well as a surface marker-based motion capture method. The joint kinematics estimated from the computer vision based method were practically comparable to the joint kinematics obtained by the surface marker-based method. The mean and standard deviation of the difference between the joint angles estimated by the computer vision based method and these obtained by the surface marker-based method was 2.31 ± 4.00°. One potential application of the proposed computer vision based marker-less method is to noninvasively assess 3D joint kinematics of industrial tasks such as lifting.     

The effect of cost function on optimum technique of the undersomersault on parallel bars

The undersomersault, or felge, to handstand on parallel bars has become an important skill in Men's Artistic Gymnastics as it forms the basis of many complex variations. To receive no deductions from the judges, the undersomersault must be performed without demonstrating the use of strength to achieve the final handstand position. Two male gymnasts each performed nine undersomersaults from handstand to handstand while data were recorded using an automatic motion capture system. The highest and lowest scoring trials of each gymnast, as determined by four international judges, were chosen for further analysis. Three optimization criteria were used to generate undersomersault technique during the swing phase of the skill using a computer simulation model: minimization of peak joint torques, minimization of horizontal velocity before release, and maximization of angular momentum. The techniques used by both gymnasts could be explained using the second optimization criterion which facilitated further skill development. The first optimization criterion generated a technique advocated for beginners where strength might be expected to be a limiting factor. The third optimization criterion resulted in a different type of undersomersault movement of greater difficulty according to the FIG Code of Points.     

Use of virtual, interactive, musculoskeletal system (VIMS) in modeling and analysis of shoulder throwing activity

Our purpose in this study was to apply the virtual, interactive, musculoskeletal system (VIMS) software for modeling and biomechanical analysis of the glenohumeral joint during a baseball pitching activity. The skeletal model was from VIMS library and muscle fiber attachment sites were derived from the visible human dataset. The muscular moment arms and function changes are mainly due to the large humeral motion involved during baseball pitching. The graphic animation of the anatomic system using VIMS software is an effective tool to model and visualize the complex anatomical structure of the shoulder for biomechanical analysis.     

Modeling and dynamic simulation of astronaut’s upper limb motions considering counter torques generated by the space suit

Extravehicular activity (EVA) is an inevitable task for astronauts to maintain proper functions of both the spacecraft and the space station. Both experimental research in a microgravity simulator (e.g. neutral buoyancy tank, zero-g aircraft or a drop tower/tube) and mathematical modeling were used to study EVA to provide guidance for the training on Earth and task design in space. Modeling has become more and more promising because of its efficiency. Based on the task analysis, almost 90% of EVA activity is accomplished through upper limb motions. Therefore, focusing on upper limb models of the body and space suit is valuable to this effort. In previous modeling studies, some multi-rigid-body systems were developed to simplify the human musculoskeletal system, and the space suit was mostly considered as a part of the astronaut body. With the aim to improve the reality of the models, we developed an astronauts’ upper limb model, including a torque model and a muscle-force model, with the counter torques from the space suit being considered as a boundary condition. Inverse kinematics and the Maggi-Kane’s method was applied to calculate the joint angles, joint torques and muscle force given that the terminal trajectory of upper limb motion was known. Also, we validated the muscle-force model using electromyogram (EMG) data collected in a validation experiment. Muscle force calculated from our model presented a similar trend with the EMG data, supporting the effectiveness and feasibility of the muscle-force model we established, and also, partially validating the joint model in kinematics aspect.     

Strategy quantification using body worn inertial sensors in a reactive agility task

Agility performance is often evaluated using time-based metrics, which provide little information about which factors aid or limit success. The objective of this study was to better understand agility strategy by identifying biomechanical metrics that were sensitive to performance speed, which were calculated with data from an array of body-worn inertial sensors. Five metrics were defined (normalized number of foot contacts, stride length variance, arm swing variance, mean normalized stride frequency, and number of body rotations) that corresponded to agility terms defined by experts working in athletic, clinical, and military environments. Eighteen participants donned 13 sensors to complete a reactive agility task, which involved navigating a set of cones in response to a vocal cue. Participants were grouped into fast, medium, and slow performance based on their completion time. Participants in the fast group had the smallest number of foot contacts (normalizing by height), highest stride length variance (normalizing by height), highest forearm angular velocity variance, and highest stride frequency (normalizing by height). The number of body rotations was not sensitive to speed and may have been determined by hand and foot dominance while completing the agility task. The results of this study have the potential to inform the development of a composite agility score constructed from the list of significant metrics. By quantifying the agility terms previously defined by expert evaluators through an agility score, this study can assist in strategy development for training and rehabilitation across athletic, clinical, and military domains.     

Spatial reconstruction of the human motion based on images of a single camera

The inverse dynamic analysis procedures used in the study of the human gait require that the kinematics of the supporting biomechanical model is known beforehand. The first step to obtain the kinematic data is the reconstruction of human spatial motion, i.e., the evaluation of the anatomic points positions that enables to uniquely define the position of all anatomical segments. In photogrammetry, the projection of each anatomical point is described by two linear equations relating its three spatial coordinates with the two coordinates of the projected point. The need for the image of two cameras arises from the fact that three equations are necessary to find the original spatial position of the anatomical point. It is shown here that the kinematic constraint equations associated with a biomechanical model can be used as the extra set of equations required for the reconstruction process, instead of the equations associated with the second camera. With this methodology, the system of equations arising from the point projections and biomechanical model kinematic constraints are solved simultaneously. Since the system of equations has multiple solutions for each image, a strategy based on the minimization of the cost function associated to the smoothness of the reconstructed motion is devised, leading to an automated computer procedure enabling a unique reconstruction.     

Automating human intuition for protein design

In the design of new enzymes and binding proteins, human intuition is often used to modify computationally designed amino acid sequences prior to experimental characterization. The manual sequence changes involve both reversions of amino acid mutations back to the identity present in the parent scaffold and the introduction of residues making additional interactions with the binding partner or backing up first shell interactions. Automation of this manual sequence refinement process would allow more systematic evaluation and considerably reduce the amount of human designer effort involved. Here we introduce a benchmark for evaluating the ability of automated methods to recapitulate the sequence changes made to computer‐generated models by human designers, and use it to assess alternative computational methods. We find the best performance for a greedy one‐position‐at‐a‐time optimization protocol that utilizes metrics (such as shape complementarity) and local refinement methods too computationally expensive for global Monte Carlo (MC) sequence optimization. This protocol should be broadly useful for improving the stability and function of designed binding proteins. Proteins 2014; 82:858–866. © 2013 Wiley Periodicals, Inc.