Coordination and Coordination Variability in Human Movement
The proposed coordination pattern classification system provides valuable insights into continuous relative phase data by categorizing coordination as in-phase or anti-phase and identifying dominant segment rotations.
While traditional vector coding overlays can be ineffective for comparing multiple trials, colour mapping and profiling techniques highlighted distinct coordination differences between individuals in this study. This challenges assumptions of uniformity and the relevance of group data, which can mask individual variations. Colour mapping and profiling are powerful visual data representation methods for single-subject design studies. They effectively classify coordination pattern commonalities and differences between trials and individuals. The data visualization approaches used here have the potential to further understanding of injuries, exercise prescription, and rehabilitation. They demonstrate an intuitive way to present complex gait data for both clinicians and scientists. By emphasizing individual movement variability, these techniques elucidate implications for injury prevention, training specificity, and recovery while enabling the extraction of meaningful information from multivariate motion analysis data. Overall, our methodology simplifies the comparison of coordination variability while preserving individual characteristics.
What is Vector Coding?
Vector coding uses non-linear techniques to quantify movement coordination and its variability during goal-orientated tasks and has become prevalent in human movement research in recent years. Here is an introduction to vector coding and highlights of our work.
An angle–angle diagram (pictured below) is the plot of one angle as a function of another angle, which provides a qualitative illustration of the coordination patterns between body segments during movement. Recent developments in vector coding have provided a quantitative measure of the shape of the angle–angle diagram by using non-linear equations to calculate the vector orientation between adjacent data points (DOI: 10.1016/j.jbiomech.2013.12.032). The vector orientation can range between 0–360° and this circular variable is referred to as the coupling angle (Hamill et al., 2000).

The coupling angle is then assigned to a coordination pattern classification (CPC) system. We devised a new CPC system offering an interpretation of the coupling angle that details either in-phase (the two segments rotate in the same direction) or anti-phase coordination (the two segments rotate in opposite directions), along with an understanding of the direction of segmental rotations and which segment is the dominant mover at each point in time (DOI: 10.1016/j.jbiomech.2015.07.023). We then devised an approach to quantify segmental dominancy as a percentage at each instant in time. Since each quadrant of a unit circle is 100 gradian, the polar position of the coupling angle (in degrees) can be converted to gradian (this can represent a percentage value) e.g., 50 degrees = 56 gradian and therefore 56%.

We then introduced the phrase ‘coupling angle mapping’ to define the use of colour to map each coupling angle and the associated CPC over time (DOI: 10.1016/j.foot.2020.101678).

Since the use of colour alone is limited and does not provide information on the distribution of the coupling angle within each CPC across time, we combined two data reporting techniques to visualise the coordination pattern (colour) and profile segmental dominancy (data bars). Segmental dominancy profiling illustrates coupling angle distribution within a CPC over time.

The significance of segmental dominancy percentage can only be obtained if there is reference to segmental range of motion. Combining coupling angle mapping with segmental dominancy and Inter-Data Point range of motion (IDROM) profiling now provides an in-depth analysis of patterns of coordination and patterns of control between two segments. Colour mapping can also be applied to coordination variability (CAV) data to show the degree of variability over time.

Key References
Quantifying lumbar–pelvis coordination during gait using a modified vector coding technique
Needham, R., Naemi, R. and Chockalingam, N., 2014. Journal of biomechanics, 47(5), pp.1020-1026. https://doi.org/10.1016/j.jbiomech.2013.12.032
A new coordination pattern classification to assess gait kinematics when utilising a modified vector coding technique
Needham, R.A., Naemi, R. and Chockalingam, N., 2015. Journal of biomechanics, 48(12), pp.3506-3511. https://doi.org/10.1016/j.jbiomech.2015.07.023
Ankle Foot Orthoses: Standardisation of terminology.
Eddison N, Chockalingam N. Foot (Edinb). 2021 Mar;46:101702. doi: 10.1016/j.foot.2020.101702. Epub 2020 May 22.
Analysing patterns of coordination and patterns of control using novel data visualisation techniques in vector coding.
Needham, R.A., Naemi, R., Hamill, J. and Chockalingam, N., 2020. The Foot, p.101678. https://doi.org/10.1016/j.foot.2020.101678
If you would like to collabrate with us please contact Dr. Robert Needham by emailing r.needham@staffs.ac.uk.