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

Abstract

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 subject's 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 degrees (+/-3.9 degrees ) and 2.0 degrees (+/-3.0 degrees ), while for knee and hip adduction they are respectively 2.0 degrees (+/-2.3 degrees ) and 1.1 degrees (+/-1.7 degrees ). 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.

2D Markerless Gait Analysis

“…Table 1 shows the comparative results in terms of mean (m e ) and standard deviation (σ e ) of joint's angles compared to the ground truth over the gait cycle. The results, with a mean value of 2.63±2.61, are particularly encouraging and present same magnitude to the ones obtained with 3D markerless systems or 2D complex model based ones [9] [12]. The image resolution and the silhouette's height reduce the algorithm performance, as predictable.…”

“…Since the video processing approach does not rely on the use of markers, this research area has been addressed as "markerless" [8]. Some approaches found in literature are based on the visual hull reconstruction and an a priori 3D human model by using a large number of video cameras [9][10]. However, in clinical gait analysis, since the majority of gait pathological knee, ankle and foot dysfunctions occur in the sagittal plane [11], markerless lower limbs' motion estimation literature is generally 2D oriented.…”

Bone Pose Estimation in the Presence of Soft Tissue Artifact Using Triangular Cosserat Point Elements

“…Then, the positive polar decomposition theorem can be used to decompose F into its rotation tensor R and stretch tensor M which is a positive-definite symmetric tensor  F RM (18) Different PS methods used for pose estimation 32,34,37 demonstrated that the rotation matrix Q which satisfies (15) under the constraint condition in (16) is …”

Parallelization strategies for markerless human motion capture

“…Therefore, Deutscher and Reid proposed the Annealed Particle Filter (APF) [13], which combines the ideas of the Condensation and the Annealed search so as to improve the tracking results. Corazza et al propose also a custom version of adapted fast simulated annealing [8] for body tracking using as input data a visual hull reconstruction and an a priori model of the subject. Another popular approach for tracking articulated objects is the use of Partitioned Sampling (PS) [27].…”