1st Joint ESMAC-GCMAS Meeting - Análise de Marcha

9. Reduced average dorsiflexion in stance
10. Increased average foot adduction
Comparison of visual and automatic identification of kinematic gait abnormalities revealed an
average of 74% agreement across the six case studies.
Inter-day repeatability of visual assessment of gait patterns was also determined and revealed
comparable agreement of 76%, when selecting abnormalities from a pre-defined list.
Discussion
Automatic identification of abnormalities
produced more consistent results than
visual observation alone. This was
particularly apparent for variables
represented by averages and ranges, rather
than maximums and minimums.
Differences less than 1.5 standard
deviations away from normal were also
more consistently detected by the
automated system than by visual
observation. However, overall abnormal
kinematic patterns were not always
reflected in the specific variable studied.
Improvement in consistency of
interpretation was observed, however
reviewing of the kinematic graphs is still
necessary, in combination with the
automated output, to determine which
deviations are clinically relevant and to
explain the significance of each of the
findings. For example, both positive and
negative differences between the healthy
mean and case study results were detected
with the automated system. However, it is
frequently the case that only change in one
direction is meaningful and suggests the
necessity of intervention. Care is also
required to ensure that other abnormalities,
which are not included in the automatic
list, are also identified. Work is ongoing
to include kinetic and EMG variables in
this analysis, and to produce a finite list of
potential gait deviations on which to base
consistent interpretation of results.
References
[1] Noonan, K.J., et al., (2003), J Pediatr Orthop, 23, 279-87
Figure 1. Example kinematic graphs from the right
leg (blue line) of a child with CP (diplegia). The
green band represents mean graphs from the healthy
children +/- 1SD
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