Visual3D has two distinctive approaches to computing the position and orientation of a segment. The first approach is the Six Degrees of Freedom method, which determines the position and orientation of segments independently from one another based solely upon measurements of that segment. The second approach is the Inverse Kinematics method, where segments form a hierarchical linked chain with joint properties that define the “connection” between segments.

The Six Degrees of Freedom method (6 DOF) is also referred to as Segment Optimization, since segment positions and orientations are solved for on a per-segment basis.

For marker-based motion capture, a set of 3 or more markers attached to a rigid segment is used to track the movement of the segment and at each frame of data the pose (position and orientation) of the segment is estimated.

The method's name refers to the fact that each segment (or each joint) is considered to have 6 variables that describe its pose (3 variable describe the position of the origin, 3 variables describe the rotation about each of the principal axes of the segment)

An alternative to the 6 DOF solution is to define joints (e.g. explicitly state which segments are connected by a joint) and to specify the properties of all joints. Because the targets used to track the segments are often subject to measurement error and soft tissue artifact, motion about some of the degrees of freedom maybe much larger than the motion that would be realistically possible.

The Inverse Kinematics method (IK) is also referred to as Global Optimization, since segment positions and orientations are solved in order to minimize a global loss function while respecting these joint constraints.

Lu and O’Connor described a global optimization process where physically realistic joint constraints can be added to the model to minimize the effect of the soft tissue and measurement error [1]. Lu and O’Connor termed this process Global Optimization while others inside the biomechanics community prefer the term “Inverse Kinematics.” Visual3D uses the term Inverse Kinematics but the underlying calculations are based upon the Lu and O’Connor technique.

Many practitioners are interested in the questions: How close are the 6 DOF and IK methods in terms of data and which one is correct?

These questions are almost impossible to answer because the correct answers will vary tremendously due to difference in the quality of the lab data, the type of movement, the amount and type of soft-tissue movement and the validity of the joint constraints used for the IK method.

Lu and O'Connor found that IK (which they refer to as Global Optimization) produced better results than 6 DOF (which they refer to as Segmental Optimization) [1]. However Lu and O'Connor's test was based on a simple model of soft-tissue error and assumed the joint constraints were an accurate representation of the subject (all joints were ball joints).

In general, visual inspection of the data in Visual3D will give you a good clue of whether IK is useful or not. If when looking at the data in Visual3D you see a lot of joints disarticulating, then IK will generally be a good idea. (For example in upper extremity motion such as in baseball pitching and golf, you often see the elbow model blow apart and IK helps this data model considerably.)

• For PowerPoint (.pptx) lecture notes on pose estimation, including 6 DOF and Inverse Kinematics (e.g. Segment Optimization and Global Optimization), click here.

• Allan Carman has prepared an interesting expose on marker sets and pose estimation algorithms, here is the link to the Biomech-L forum.
• While these opinions are Allan Carman's, not HAS-Motion's, we highly recommend reading the information. If you have any questions, contact HAS-Motion's customer support.

• A. Leardini, C. Belvedere, F. Nardini, N. Sancisi, M. Conconi, and V. Parenti-Castelli, “Kinematic models of lower limb joints for musculo-skeletal modelling and optimization in gait analysis,” J Biomech, vol. 62, pp. 77–86, Sep. 2017, doi: 10.1016/j.jbiomech.2017.04.029.
  • Abstract: Kinematic models of lower limb joints have several potential applications in musculoskeletal modelling of the locomotion apparatus, including the reproduction of the natural joint motion. These models have recently revealed their value also for in vivo motion analysis experiments, where the soft-tissue artefact is a critical known problem. This arises at the interface between the skin markers and the underlying bone, and can be reduced by defining multibody kinematic models of the lower limb and by running optimization processes aimed at obtaining estimates of position and orientation of relevant bones. With respect to standard methods based on the separate optimization of each single body segment, this technique makes it also possible to respect joint kinematic constraints. Whereas the hip joint is traditionally assumed as a 3 degrees of freedom ball and socket articulation, many previous studies have proposed a number of different kinematic models for the knee and ankle joints. Some of these are rigid, while others have compliant elements. Some models have clear anatomical correspondences and include real joint constraints; other models are more kinematically oriented, these being mainly aimed at reproducing joint kinematics. This paper provides a critical review of the kinematic models reported in literature for the major lower limb joints and used for the reduction of soft-tissue artefact. Advantages and disadvantages of these models are discussed, considering their anatomical significance, accuracy of predictions, computational costs, feasibility of personalization, and other features. Their use in the optimization process is also addressed, both in normal and pathological subjects.
• A. Schmitz, F. L. Buczek, D. Bruening, M. J. Rainbow, K. Cooney, and D. Thelen, “Comparison of hierarchical and six degrees-of-freedom marker sets in analyzing gait kinematics,” Comput Methods Biomech Biomed Engin, vol. 19, no. 2, pp. 199–207, 2016, doi: 10.1080/10255842.2015.1006208.
  • Abstract: The objective of this study was to determine how marker spacing, noise, and joint translations affect joint angle calculations using both a hierarchical and a six degrees-of-freedom (6DoF) marker set. A simple two-segment model demonstrates that a hierarchical marker set produces biased joint rotation estimates when sagittal joint translations occur whereas a 6DoF marker set mitigates these bias errors with precision improving with increased marker spacing. These effects were evident in gait simulations where the 6DoF marker set was shown to be more accurate at tracking axial rotation angles at the hip, knee, and ankle.

[1] T. W. Lu and J. J. O’Connor, “Bone position estimation from skin marker co-ordinates using global optimisation with joint constraints,” J Biomech, vol. 32, no. 2, pp. 129–134, Feb. 1999, doi: 10.1016/s0021-9290(98)00158-4.