Lars Mündermann

A Markerless Motion Capture System to Study Musculoskeletal Biomechanics: Visual Hull and Simulated Annealing Approach

Human motion capture is frequently used to study musculoskeletal biomechanics and clinical problems, as well as to provide realistic animation for the entertainment industry. The most popular technique for human motion capture uses markers placed on the skin, despite some important drawbacks including the impediment to the motion by the presence of skin markers and relative movement between the skin where the markers are placed and the underlying bone. The latter makes it difficult to estimate the motion of the underlying bone, which is the variable of interest for biomechanical and clinical applications. A model-based markerless motion capture system is presented in this study, which does not require the placement of any markers on the subjects body. The described method is based on visual hull reconstruction and an a priori model of the subject. A custom version of adapted fast simulated annealing has been developed to match the model to the visual hull. The tracking capability and a quantitative validation of the method were evaluated in a virtual environment for a complete gait cycle. The obtained mean errors, for an entire gait cycle, for knee and hip flexion are respectively 1.5° (±3.9°) and 2.0° (±3.0°), while for knee and hip adduction they are respectively 2.0° (±2.3°) and 1.1° (±1.7°). Results for the ankle and shoulder joints are also presented. Experimental results captured in a gait laboratory with a real subject are also shown to demonstrate the effectiveness and potential of the presented method in a clinical environment.

The evolution of methods for the capture of human movement leading to markerless motion capture for biomechanical applications

Over the centuries the evolution of methods for the capture of human movement has been motivated by the need for new information on the characteristics of normal and pathological human movement. This study was motivated in part by the need of new clinical approaches for the treatment and prevention of diseases that are influenced by subtle changes in the patterns movement. These clinical approaches require new methods to measure accurately patterns of locomotion without the risk of artificial stimulus producing unwanted artifacts that could mask the natural patterns of motion. Most common methods for accurate capture of three-dimensional human movement require a laboratory environment and the attachment of markers or fixtures to the bodys segments. These laboratory conditions can cause unknown experimental artifacts. Thus, our understanding of normal and pathological human movement would be enhanced by a method that allows the capture of human movement without the constraint of markers or fixtures placed on the body. In this paper, the need for markerless human motion capture methods is discussed and the advancement of markerless approaches is considered in view of accurate capture of three-dimensional human movement for biomechanical applications. The role of choosing appropriate technical equipment and algorithms for accurate markerless motion capture is critical. The implementation of this new methodology offers the promise for simple, time-efficient, and potentially more meaningful assessments of human movement in research and clinical practice. The feasibility of accurately and precisely measuring 3D human body kinematics for the lower limbs using a markerless motion capture system on the basis of visual hulls is demonstrated.

Markerless Motion Capture through Visual Hull, Articulated ICP and Subject Specific Model Generation

An approach for accurately measuring human motion through Markerless Motion Capture (MMC) is presented. The method uses multiple color cameras and combines an accurate and anatomically consistent tracking algorithm with a method for automatically generating subject specific models. The tracking approach employed a Levenberg-Marquardt minimization scheme over an iterative closest point algorithm with six degrees of freedom for each body segment. Anatomical consistency was maintained by enforcing rotational and translational joint range of motion constraints for each specific joint. A subject specific model of the subjects was obtained through an automatic model generation algorithm (Corazza et al. in IEEE Trans. Biomed. Eng., 2009) which combines a space of human shapes (Anguelov et al. in Proceedings SIGGRAPH, 2005) with biomechanically consistent kinematic models and a pose-shape matching algorithm. There were 15 anatomical body segments and 14 joints, each with six degrees of freedom (13 and 12, respectively for the HumanEva II dataset). The overall method is an improvement over (Mündermann et al. in Proceedings of CVPR, 2007) in terms of both accuracy and robustness. Since the method was originally developed for ≥8 cameras, the method performance was tested both (i) on the HumanEva II dataset (Sigal and Black, Technical Report CS-06-08, 2006) in a 4 camera configuration, (ii) on a series of motions including walking trials, a very challenging gymnastic motion and a dataset with motions similar to HumanEva II but with variable number of cameras.

Accurately measuring human movement using articulated ICP with soft-joint constraints and a repository of articulated models

A novel approach for accurate markerless motion capture combining a precise tracking algorithm with a database of articulated models is presented. The tracking approach employs an articulated iterative closest point algorithm with soft-joint constraints for tracking body segments in visual hull sequences. The database of articulated models is derived from a combination of human shapes and anthropometric data, contains a large variety of models and closely mimics variations found in the human population. The database provides articulated models that closely match the outer appearance of the visual hulls, e.g. matches overall height and volume. This information is paired with a kinematic chain enhanced through anthropometric regression equations. Deviations in the kinematic chain from true joint center locations are compensated by the soft-joint constraints approach. As a result accurate and a more anatomical correct outcome is obtained suitable for biomechanical and clinical applications. Joint kinematics obtained using this approach closely matched joint kinematics obtained from a marker based motion capture system.

Predicting changes in knee adduction moment due to load-altering interventions from pressure distribution at the foot in healthy subjects

The purpose of this pilot study of healthy subjects was to determine if changes in foot pressure patterns associated with a lateral wedge can predict the changes in the knee adduction moment. We tested two hypotheses: (1) increases or decreases in the knee adduction moment and ankle eversion moment due to load-altering footwear interventions can be predicted from foot pressure distribution and (2) changes in magnitude of the knee adduction moment and ankle eversion moment due to lateral wedges can be predicted from pressure distribution at the foot during walking. Fifteen healthy adults performed walking trials in three shoes: 0 degrees , 4 degrees , and 8 degrees laterally wedged. Maximum heel pressure ratio, first peak knee adduction moment, and peak ankle eversion moment were assessed using a pressure mat, motion capture system, and force plate. Increases or decreases in the knee adduction moment and ankle eversion moment were predicted well from foot pressure distribution. However, the magnitude of the pressure change did not predict the magnitude of the peak knee adduction moment change or peak ankle eversion moment change. Factors such as limb alignment or trunk motion may affect the knee adduction moment and override a direct relationship between the pressure distribution at the shoe-ground interface and the load distribution at the knee. However, changes (increases or decreases) in the peak knee adduction moment due to load-altering footwear interventions predicted from pressure distribution during walking can be important when evaluating these types of interventions from a clinical perspective.

Automatic Generation of a Subject-Specific Model for Accurate Markerless Motion Capture and Biomechanical Applications

A novel approach for the automatic generation of a subject-specific model consisting of morphological and joint location information is described. The aim is to address the need for efficient and accurate model generation for markerless motion capture (MMC) and biomechanical studies. The algorithm applied and expanded on previous work on human shapes space by embedding location information for ten joint centers in a subject-specific free-form surface. The optimal locations of joint centers in the 3-D mesh were learned through linear regression over a set of nine subjects whose joint centers were known. The model was shown to be sufficiently accurate for both kinematic (joint centers) and morphological (shape of the body) information to allow accurate tracking with MMC systems. The automatic model generation algorithm was applied to 3-D meshes of different quality and resolution such as laser scans and visual hulls . The complete method was tested using nine subjects of different gender, body mass index (BMI), age, and ethnicity. Experimental training error and cross-validation errors were 19 and 25 mm, respectively, on average over the joints of the ten subjects analyzed in the study.

Most favorable camera configuration for a shape-from-silhouette markerless motion capture system for biomechanical analysis

The most common methods for accurate capture of three-dimensional human motion require a laboratory environment and the attachment of markers or fixtures to the body segments. These laboratory conditions can cause unknown experimental artifacts. Thus our understanding of normal and pathological human movement would be enhanced by a method that allows capture of human movement without the constraint of markers or fixtures placed on the body. Markerless methods are not widely available because the accurate capture of human movement without markers is technically challenging. A reported method of constructing a bodys visual hull using shape-from-silhouette (SFS) offers an attractive approach. However, to date the influence of camera placement and number of cameras on construction of visual hulls for biomechanical analysis is largely unknown. The purpose of this study was to evaluate the accuracy of SFS construction of a human form for biomechanical analysis dependent on camera placement and number of cameras. Visual hull construction was sensitive to camera placement and the subjects pose. Uniform camera distributions such as circular and hemispherical camera arrangements provided most favorable results. Setups with less than 8 cameras yielded largely inaccurate visual hull constructions and great fluctuations for different poses and positions across a viewing volume, while setups with 16 and more cameras provided good volume estimations and consistent results for different poses and positions across the viewing volume.

Amplitude and phasing of trunk motion is critical for the efficacy of gait training aimed at reducing ambulatory loads at the knee.

The purpose of this study was to determine the contribution of changes in amplitude and phasing of medio-lateral trunk sway to a change in the knee adduction moment when walking with increased medio-lateral trunk sway. Kinematic and kinetic data of walking trials with normal and with increased trunk sway were collected for 19 healthy volunteers using a standard motion analysis system. The relationship between the change in first peak knee adduction moment (ΔKAM) and change in trunk sway amplitude (ΔSA; difference between maximum contralateral trunk lean and maximum ipsilateral trunk lean) and phasing (SP; time of heel-strike relative to time of maximum contralateral and time of maximum ipsilateral trunk lean) was determined using nonlinear regression analysis. On average, subjects increased their SA by 9.7 ± 3.6 deg (P < 0.001) with an average SP of 98.8 ± 88.8 ms resulting in an average reduction in the first peak knee adduction moment of -55.2 ± 30.3% (P < 0.001). 64.3% of variability in change in peak knee adduction moment with the increased trunk sway condition was explained by both differences in SA and SP, and the relationship among these parameters was described by the regression equation ΔKAM = 27.220-4.128 [middle dot] ΔSA-64.785 [middle dot] cos(SP). Hence, not only the amplitude but also the phasing of trunk motion is critical. Not only lower limb movement but also lumbar and thoracic lateral flexion should be considered in the decision making process for an optimal intervention aimed at reducing the load on the medial compartment of the knee during walking. However, these promising findings originated from studies on healthy subjects and their relevance for gait training interventions in patients with presumably painful knee osteoarthritis remains to be determined.

Measuring human movement for biomechanical applications using markerless motion capture

Modern biomechanical and clinical applications require the accurate capture of normal and pathological human movement without the artifacts associated with standard marker-based motion capture techniques such as soft tissue artifacts and the risk of artificial stimulus of taped-on or strapped-on markers. In this study, the need for new markerless human motion capture methods is discussed in view of biomechanical applications. Three different approaches for estimating human movement from multiple image sequences were explored. The first two approaches tracked a 3D articulated model in 3D representations constructed from the image sequences, while the third approach tracked a 3D articulated model in multiple 2D image planes. The three methods are systematically evaluated and results for real data are presented. The role of choosing appropriate technical equipment and algorithms for accurate markerless motion capture is critical. The implementation of this new methodology offers the promise for simple, time-efficient, and potentially more meaningful assessments of human movement in research and clinical practice.

Conditions that influence the accuracy of anthropometric parameter estimation for human body segments using shape-from-silhouette

Anthropometric parameters are fundamental for a wide variety of applications in biomechanics, anthropology, medicine and sports. Recent technological advancements provide methods for constructing 3D surfaces directly. Of these new technologies, visual hull construction may be the most cost-effective yet sufficiently accurate method. However, the conditions influencing the accuracy of anthropometric measurements based on visual hull reconstruction are unknown. The purpose of this study was to evaluate the conditions that influence the accuracy of 3D shape-from-silhouette reconstruction of body segments dependent on number of cameras, camera resolution and object contours. The results demonstrate that the visual hulls lacked accuracy in concave regions and narrow spaces, but setups with a high number of cameras reconstructed a human form with an average accuracy of 1.0 mm. In general, setups with less than 8 cameras yielded largely inaccurate visual hull constructions, while setups with 16 and more cameras provided good volume estimations. Body segment volumes were obtained with an average error of 10% at a 640x480 resolution using 8 cameras. Changes in resolution did not significantly affect the average error. However, substantial decreases in error were observed with increasing number of cameras (33.3% using 4 cameras; 10.5% using 8 cameras; 4.1% using 16 cameras; 1.2% using 64 cameras).