Introduction to Sports Biomechanics: Analysing Human Movement ...

BOX 3.2 A CAUTIONARY TALE OF UNRELIABLE DATA
MORE ON MOVEMENT PATTERNS – THE GEOMETRY OF MOTION
As we have noted many times in this chapter, one of the major tasks for movement analysts is to
be able to analyse coordination patterns in sport. Of particular interest to some movement
analysts, including me, is the variability that we find in coordination patterns such as angle–angle
diagrams, when even a highly skilled performer repeats a movement – this variability is also
observed in movement patterns seen as a function of time. Of considerable interest to applied
researchers is whether we can accurately and reliably assess such movement variability in
competition.
To answer this question, some of my colleagues at the University of Otago and I carried out a
study to compare the reliability of estimating movement patterns in laboratory and simulated
field conditions. Both conditions were recorded using a digital video camera viewing perpendicular
to the sagittal plane of the runner in our laboratory, with good participant–clothing
and clothing–background contrasts. The difference between the two conditions was as follows.
In the ‘laboratory’ condition, the participant had reflective markers attached to his clothing to
improve identification of joint axes of rotation; in the ‘simulated field’ condition, no markers
were used because they cannot be in sports competitions. Although this is not the only difference
between laboratory and real field conditions, it is usually the most important one by far.
The participant ran five trials in each condition at the same speed on a treadmill with equal rest
periods between trials. From each trial, we selected three strides, from toe-off to toe-off, to be
digitised – this means that a human operator identified each marker on each video frame,
effectively manually tracking the markers or estimating the positions of the joints in the nomarker
condition. We also tracked the markers automatically using SIMI software (SIMI Reality
Motion Systems GmbH, Unterschliessheim, Germany; http://www.simi.com). Five marker trials
were auto-tracked using SIMI and manually tracked by four experienced movement analysts
on five consecutive days. The four human operators then digitised the five no-marker trials on
consecutive days.
Several sources of variability, or variance, are present in this design. The one of interest to
movement analysts is movement variability – the variance among trials. The next two sources
are due to variability within and among the human operators, respectively known as intraoperator
variability and inter-operator variability. The first of these, in our study, was the variance
across days and the second that across the four operators. There are other sources of
variance from the three two-factor and the one three-factor interactions between our three
main factors – trials, days and people. We then used a statistical technique known as analysis of
variance (ANOVA) to partition the variances between the three main effects and the several
interactions. The results for the marker and the no-marker conditions are summarised in the pie
charts of Figures 3.21(a) and (c) for the marker condition and Figures 3.21(b) and (d) for no
markers.
Small variances across repeated attempts by the same person (across days in our study) show
good intra-operator reliability; small variances across operators (across people) show good interoperator
reliability, sometimes known as objectivity. It should be clear from Figures 3.21(a) and (c)
that, when markers are used, movement variability (variance across trials) is by far the
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