Here’s something I’ve meant to share for some time.
Below are two graphs that I prepared for some teaching I was doing in Melbourne last August. I downloaded the data that Mike Schwartz has been so kind as to make available from his study looking at the changes in gait pattern of children when they walk at different speeds (Schwartz et al., 2008). I then formatted the sagittal plane graphs as we normally do (except that I’ve started plotting the two standard deviation range in a different shade of grey to the one standard deviation range to remind us that we often under-estimate the spread of our reference data). Data is time normalised to the gait cycle and plotted on graphs of fixed aspect ratio (3:4 in this case). All looks quite unremarkable with fairly modest changes in kinematics with walking speed.
But then I realised that the slower walkers have a longer cycle time and the data should really be stretched to make comparisons as to how children are waking in real time. Slow walkers take a lot longer to complete a gait cycle than fast walkers and the data should really be plotted on wider graphs to allow comparison of what is happening over the same time period.
If we plot the data like this we see just how different the data really are. I’ve not absorbed the full effect or implications of this but think about the slope of the knee flexion curve in second double support and toe off which many clinicians associate with rectus femoris (mal)function. If the rectus is inhibiting knee flexion then they expect the slope to be reduced. But look at the difference between the real gradient in the lower graphs and the apparent gradient in the conventional (upper graphs). How can we possibly interpret this phenomenon from the conventional graphs?
It ‘s not clear what we can do about this. Plotting the graphs the way we do allows comparison of like with like (even if we might lose something by forcing the comparison). We often use graphs to compare outcome after intervention. How would we do this sensibly if the graphs are different shapes?
Anyone got any ideas how we can properly represent the slope data without losing the power of the straight forward comparisons we get from sticking to the tried and tested conventions for plotting data?
Schwartz, M. H., Rozumalski, A., & Trost, J. P. (2008). The effect of walking speed on the gait of typically developing children. J Biomech, 41(8), 1639-1650.
This is quite a simple post with a tutorial screencast of how to format gait graphs nicely in Excel. For a long time I just didn’t think this was possible but you can see from the image below that it is! The screencast is the simplest example of a range of tools we are developing to support students who enrol on ourmaster’s degree programme in clinical gait analysis which starts in September as part of the EU funded CMAster project.
The main thing that makes plotting the graphs like this possible is that you can select different series within the same chart to have different chart types. I suspect that this feature may not be available in early versions of Excel but don’t know when it was introduced – this graph was generated in Excel 2010 on a PC. If you are good at working with charts in Excel then this is all you really need to know and watching the screencast will only waste another 20 minutes of your life. If you are not then I suggest you just watch the screencast and I’ll explain things a bit more slowly.
One top tip I’ll offer – if you want to create an array of graphs make sure that your formatting is correct on the first graph before you start copying and pasting. If you find a mistake later on you’ll have to correct it on each graph separately.
I’ve said in the screencast that I’ll produce another one to show how to add in the timing data. The only way I know to do this is a little bit messy. Does anyone know a nice straightforward way?
(Note that the screencast is recorded in reasonably high definition but you may have to use full screen display and increase the resolution with the little cog icon at the bottom left of the video to appreciate this.)
Another comment from CMAS. I think it was Alison Richardson who was presenting at one point and remarked, “but of course we can’t tell where the foot is from the graphs”. How true? and why not? Conventionally in clinical gait analysis we plot where the pelvis is in relation to the lab, then the hip, knee and ankle joints. In theory if you know all this information you can work out the orientation of the foot. I don’t know anyone, however, who has developed the knack of adding all those angles up in their head to work this out. In understanding how the foot is contributing to that pattern I think Perry’s concept of foot rockers is key – is the limb pivoting primarily around the heel, the ankle or the MTP joint? Yet, despite what you hear in many discussions about gait data, it’s virtually impossible to tell from the graphs which rocker is active at any given time.
So why don’t we plot out foot orientation? We calculate the equivalent in the transverse plane and call it foot progression. I think it would make all our lives considerably easier if we added an extra graph at the foot of the sagittal plane data. Given that the pitch of a shoe is how much it tilts the foot forwards perhaps we should refer to this a “foot pitch”.
I’ve shown you what the sagittal graphs would then look like. I don’t suggest using the colours on the foot pitch graph – they are only there to show you how easily you can pick out the three rockers. During the red phase of stance the foot is pivoting about the heel – first rocker. During the white phase the foot is flat on the ground – second rocker. During the blue phase the foot is pivoting about the MTP joint (or toe) – third rocker (or third and fourth rockers if you want to use Perry and Burnfield’s most recent terminology (2010). Notice that end of first rocker does not coincide with opposite foot off but is completed appreciably earlier. Many people don’t appreciate just how early third rocker starts either.
Perry, J., & Burnfield, J. M. (2010). Gait analysis: normal and pathological function (2nd ed.). Pomona, California: Slack.