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

Abstract

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 body's 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.

Figure 1

(a) Selected background images (top) and separated subject data (bottom). (b) Camera configuration, video sequences with separated subject data, and selected visual hulls.

Figure 2

(a) Volume values of visual hulls as a function of position and pose in the viewing volume. (b) Average, min and max volume values across the viewing volume as a function of number of cameras. The dotted line indicates the human form's volume.

Figure 3

(a) Laser scan. (b) Body segments. (c) Joint centers.

Figure 4

Articulated body matched to visual hulls. (a) Human body segments. (b) Kinematic chain.

Figure 5

Motion graphs for (a) knee flexion and (b) knee abduction angles (gray = marker-based; black = markerless).