gait graphs

Gait graphs for beginners

I’m teaching about gait to the undergraduate physios next week. Its the first lecture I’ve given at this level trying to emphasise the approach I’ve developed in the Why we walk the way we do videos. The colleague who’s delivering the previous lecture – which included a first introduction to gait graphs – wanted to use the same format as I use which started a conversation about what aspects of walking we’d like those graphs to emphasize.

Knee graph

I’m pretty keen on fixed aspect ratios and scaling so that you can forget about those issues when you are actually looking at data – so we’ve fixed that.  We wanted also wanted to reinforce the terminology for different phases that I’ve described in a previous post – so we’ve put those abbreviated names across the top.

I also like to represent the continuity of the gait cycle – it amazes me how many people I come across who don’t seem to realise that point on the far left of the graph is the same as the point on the far right hand side (give or take a little stride to stride variability). It’s also not uncommon to spot data in the literature where values of gait variables at 0% and 100% are different but not commented upon. Various people in the past have tried plotting more than a single stride to try and emphasize continuity. I know Jurg Baumann was an advocate of this but can’t find easily get my hands on a sample. At Hof also used it – his 2002 paper on the speed dependence of EMG profiles is an example – but it has never really caught on. In this format I’ve tried to capture the point by allowing the gait curves to fade away to nothing outside the graph. It’s a bit messy if you’ve got a whole array of graphs but I kind of like it in the context of an introduction at this level.

I’m also very keen on getting students to appreciate what the right leg is doing plotted on the same time scale as the left leg. I know this insenses people who are paranoid about the importance of symmetry in gait but it’s a hell of a lot easier to explain the biomechanics if you look at the data this way. It’s unconventional of course so I’ve chosen to represent this as a much fainter line.

There was another question mark over the hip angle. As gait analysts most of us assume that this should be measured relative to a pelvic axis represented by the line from PSIS to ASIS and thus biasing the hip graph towards flexion. In assessing gait by observation, however, physios almost always consider the angle with the vertical which might be more relevant for daily practice. In the end we decided to stick with the gait analysis approach and just make sure we explain this very clearly.

Anyone got any additional features they like to add?

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Hof, A. L., Elzinga, H., Grimmius, W., & Halbertsma, J. P. (2002). Speed dependence of averaged EMG profiles in walking. Gait Posture, 16(1), 78-86.

Making attractive gait graphs in Polygon

Not a proper post this one and only of interest to Vicon users but, prompted by one of the students on our Masters Programme in Clinical Gait Analysis,  I’ve created a video to illustrate how to create nice gait graphs within Polygon. It also shows how you can export these easily to Word and the data to Excel (then you can look at an earlier post to see how to create yet another set of graphs!) .  I’ve used Polygon 4 to create the video but I know there are still many Polygon 3 users out there. The interface looks a little different but the basic process is exactly the same. The main difference is that there is no Attributes panel in Polygon 3. In most cases you have to right click on an object (graph, graph axis etc) and select one of the options on the menu that then appears.

 

Who first thought of a gait graph?

Quite out of the blue Jenny Kent from Headley Court asks if I know where the gait graphs we know today come from. She was particularly interested in where the idea of time normalising data to the gait cycle originated. I have to admit I just don’t know.

Braune and Fischer, working at the end of the 19th century, certainly plotted a number of gait variables against time, most for swing but a few for more than a gait cycle. All the graphs I can see though plot these against time rather than a percentage of the gait cycle and the data for more than a gait cycle doesn’t appear to be plotted in relation to the gait events at all.

The first group that I can find that present variables on graphs with the time axis labelled as % gait cycle is Inman’s group working in Berkeley in the early 1950s.

Inman time normalisation

Data scanned a long time ago from one of the outputs of the Berkeley group – not sure which.

Can anyone provide any earlier examples?

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This made me think about other features of our standard gait graphs. Who first proposed plotting data from a patient against normative reference data depicted as a mean and range based on the standard deviation?

I remember that when the Vicon Clinical Manager software came out in 1992 that it assumed that all data was normalised to the gait cycle (the data was actually stored in a .gcd file on this assumption). The software only allowed three traces to be plotted on any graph so the common practice was to plot the mean of the reference data along as one right and one left side trace for each patient. I think the practice of plotting several (three!) traces from each side separately to assess measurement variability probably dated to this time as well. I don’t remember the standard deviations being plotted but this may just be my memory (the standard deviation values could certainly be stored in the .gcd file).

I also remember being impressed by teaching material from Newington and Gillette Hospitals (Gage, Davis and Ounpuu) which plotted the standard deviation ranges from quite an early stage. Looking up some of their early papers I find that  Sylvia’s 1995 paper contains sample patient data plotted against the standard deviation ranges. (Unfortunately the quality of this figure in the .pdf file I have is too poor to be worth reproducing here).

Sylvia moved to Newington from Waterloo so I wondered how David Winter had plotted his data. Sure enough in the final chapter of The Biomechanics and Motor Control of Human Walking (1991) entitled “Assessment of pathological gait” are a series of graphs showing gait variables from a patient with a knee replacement plotted against the mean and standard deviation from a reference population. (This book was an adaptation of an earlier one form 1987 which I don’t have access to and I’d be interested to know if these graphs were included in that as well).

 winter gait graphs

I’d like to suggest that this might be the earliest example of gait graph as we use them today – or has anyone got any earlier examples?

Of course tracing ideas back like this is a slightly ridiculous activity because such graphs  often appear in publications only after having been used more generally for a considerable period. Just because they first appear in print from one team does not necessarily mean that they originated there!

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Braune, W., & Fischer, O. (1987). The Human Gait (P. Maquet & R. Furlong, Trans.). Berlin ; New York: Springer-Verlag.

Klopsteg, P. E., & Wilson, P. D. (1954). Human Limbs and their Substitutes. New York: McGraw-Hill.

Ounpuu, O., Davis, R., & Deluca, P. (1996). Joint kinetics: Methods, interpretation and treatment decision-making in children with cerebral palsy and myelomeningocele. Gait and Posture, 4, 62-78.

Winter, D. (1991). The biomechanics and motor control of human gait: Normal, Elderly and Pathological (2nd ed.). Waterloo:: Waterloo Biomechanics.

Stretching time

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.

Different speeds time normalised

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.

Different speeds not time normalised

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?

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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.

Making nice gait graphs in Excel

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.

Nice gait graph

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.)