Human Motion Analysis

Human Motion Analysis (HMA) is a technique which involves the objective quantification of human motion, including joint kinematics (e.g. joint angles), joint kinetics (e.g. external moments), temporal-spatial parameters (e.g. stride length), and muscle activity. This allows for a much more thorough objective quantification of how function has changed at both the unaffected and affected joints following surgical intervention.

There are various techniques for HMA, which all vary in terms of accuracy, precision, practicality and cost. The most common clinical application for HMA has been in the management of patients with walking disorders, causing gait analysis to become a routine part of patient management in certain centres.

Motion capture using opto-electronic stereophotogrammetry (MOCAP) is the most common method for quantifying both the kinematics and the kinetics (Fernandez et al., 2008). This method has previously been used at Cardiff University to quantify the function of OA subjects (Jones et al., 2006, Beynon et al., 2006, Metcalfe et al., 2013) and assess their post-surgical recovery (Jones et al., 2006, Jones and Holt, 2008, Watling, 2014, Whatling, 2009).

Joint kinematics are assessed during MOCAP by using markers which are tracked in 3D space by cameras. The Qualisys system at Cardiff University uses retroreflective markers, which reflect infrared light (IR) emitted by the cameras. Within each camera, there is also an IR sensor which captures this reflected light. If the motion analysis laboratory is free from other sources of IR light, then the cameras will only see the markers, hence the complex object classification algorithms seen in HMA within the computer vision field are not necessary. There will, however, also be some level of unwanted IR light sources and reflections within a laboratory. This is easily addressed using preventative methods, camera masking, or pixel intensity thresholds.

the position and orientation of the underlying bone. This provides clear, repeatable and clinically interpretable axis definitions to the segments. For example, the distal end of the tibia is often defined using the medial and lateral malleolus, and the proximal end using the femoral epicondyles (see Figure 2.6A).

As a person moves, the soft tissues are continually moving relative to the bone due to skin movement, muscle contraction, and inertial effects. This results in inaccuracies in the assumption that marker movement directly corresponds to bone movement. The anatomical landmarks used to define the segment axis system also happen to be prone to large levels of soft tissue artefact (STA) during motion. It is, therefore, common to use tracking markers, which are placed on the subject at locations with less STA, such as

Figure 2.6 A) Illustration of how markers (grey circles) on the femoral epicondyles and the

medial and lateral malleolus can be used to define an Anatomical Coordinate System (AC S) for the tibia during a static trial

B) Illustration of how the position of a rigid tracking cluster placed laterally on the tibia might be used to reduce errors due to soft tissue artefact. The tracking Marker Coordinate System (MCS)

is defined relative to the tibial ACS during the static trial. During motion trials, only the position and orientation of the tracking MCS need to be collected, and the position and orientation of the

the lateral shank and thigh (see Figure 2.6B). Generally, at least three tracking markers will be used per anatomical segment, which allows the creation of a tracking segment. The rotation of the tracking segments relative to one another does not produce a clinically interpretable joint angle. The position and orientation of each tracking segment relative to the corresponding anatomical segment is recorded during a static calibration trial. During the movement trials, it is assumed that the position and orientation between the tracking segment relative to the true anatomical segment axis remain constant, and anatomical segment orientation can, therefore, be inferred solely through measuring tracking marker segments.

For a thorough overview of the possible errors incurred during MOCAP, the reader is re- directed to a comprehensive four-part review (Cappozzo et al., 2005, Chiari et al., 2005, Leardini et al., 2005, Della Croce et al., 2005). In summary, as the technology involved in MOCAP has advanced, the methodological errors have quickly far outweighed the instrumental errors. The primary methodological errors are STA, as previously mentioned, and the failure to accurately model the anatomical axis of the bone using anatomical markers. The latter can be due factors such as marker placement error, high amounts of subcutaneous fat due over bony landmarks, or that elements of anatomic axes, such as the hip joint centre, cannot be palpated. STA is particularly high for the thigh and can result in rotational errors greater than 12 degrees in calculations of internal/external rotation and ab/adduction of the knee (Peters et al., 2010, Garling et al., 2007).

Kinetic data is calculated using a force plate/platform. These plates measure the equal and opposite Ground Reaction Force (GRF) caused by the foot in contact with the floor during motion. The human body is being modelled as a system of rigid links with six degrees of freedom at each joint (unless inverse kinematics are being applied). A free body diagram can be described to estimate the reaction forces and moments that act about these links. In addition to the consideration of the GRF, the mass and inertia of the

effects, the inertial properties of body segments can be estimated during inverse dynamic analysis. This involves the use of cadaveric data, such as that provided by De Leva (1996) which provides linear regressions of the centre of mass and the radius of gyration in each plane for segments relative to parameters, such as leg length.