Just a minute

During a meeting of the CMAS standards meeting last week there was some discussion about how repeatable our measurements need to be. I was struck by  a comment from Rosie Richards from the Royal National Orthopaedic Hospital at Stanmore that six degrees is the angle represented by one minute on a clock (apparently the idea originally came from her colleague Matt Thornton). Her point was that this doesn’t feel like a very big angle and that if we are are working to this sort of accuracy then we are doing pretty well. I’d agree with her and think if there is ever any discussion of just how accurate gait analysis is then using this as an illustration is really powerful.

Corn Exchange clock, Bristol. This clock actually has two second hands. The red one records GMT and the black one the local time in Bristol which is 190 km west of London and thus nine seconds behind! (C) Rick Crowley, Creative commons licence.

The evidence supports this. In our systematic review, Jenny McGinley and I suggested that measurement variability of more than 5 degrees was concerning and showed that most repeatability studies for most joint angles report variability of less than this. They are thus also, of course, within the one minute limit as well.

It’s also interesting to note that the variability within normal gait is generally less than 6 degrees. I’ve tabulated the standard deviations from our recent comparison of normative data below. Hip rotation at one centre pushes above the limit (but this is almost certainly a consequence of measurement error). The only other variable that exceeds this is foot progression (which I’ll return to below). This should be of interest to those who think that they should be able to use differences in gait pattern as a biometric to identify people. To do this successfully would require variability within the 1 minute limit to distinguish between people.  Personally, I think this is a big ask from the CCTV camera footage that the biometricians would like to base their analysis upon.

Average standard deviations across gait cycles for different gait variables

This doesn’t mean we should be  complacent, however. In the figure below I’ve compared Verne  in the average normal pose at the instant of foot contact (grey outline) and then increased his leading hip flexion by 6 degrees (and adjusted the trailing foot pitch to bring the foot into contact with the ground again while all the other joint angles remain the same). You can see that this has increased step length by over 10%. If there was an additional 6 degree increase in trailing hip extension as well then this would double. The additive effect of such variability may help explain why foot progression in the table above is a little higher than the other measures in that it can be considered as a combination of the transverse plane rotations at pelvis, hip, knee and ankle rather than a “single” joint angle.

Effect on step length of increasing leading hip flexion by six degrees

In summary the one minute limit seems an extremely useful way of describing how accurate our measurement systems are and we should take considerable confidence from this. On the other hand we shouldn’t be complacent as variability of this level in specific joint parameters can have quite substantial impacts on the biomechanics of walking.

Readers outside the UK may not fully appreciate the title to this blog which is a reference to one of the oldest comedy shows on BBC radio which has been broadcast regularly since 1967. It is one of the purest and most exuberant celebrations of the English language that I know. Episodes are not being broadcast at present but when they are they can be listened to internationally (I think) through the BBC i-player


I know him by his gait

I was asked to be an expert witness for a court case last week. There was some video footage from a CCTV camera of an individual walking across a street from some distance away and the question I was being asked to provide an opinion on was, “Is it possible to identify the individual on the basis of his gait pattern?“.

There are, of course a number of University departments working on related issues (that within Computer Vision at the University of Southampton is a good example) and some evidence of commercial interest.  Every so often the concept bubbles into the popular science magazines. (There is now even an Android app that claims to be able to identify a person from the accelerometer data from a smartphone carried in their pocket while they are walking, but that’s another story).

I’ve generally been rather dismissive of these claims. I strip people down to a pair of shorts, stick retro-reflective markers over anatomical landmarks, ask the person to move in a particular fashion along a clearly marked walkway and then capture the movements with ten extremely high resolution cameras pointing directly at them. It often amazes me how little evidence there is of difference from normative reference data even for individuals with quite marked pathology. If I can’t detect such clear differences under such standardised conditions using such specialised equipment how can anyone suggest that they can recognise a healthy  individual, presumably with a gait pattern within the normal range, on the basis of a video image of them walking down the street fully clothed?

And yet in Shakespeare’s Julius Caesar, Cassius says to Casca when he sees a figure approaching, “Tis Cinna. I do know him by his gait“.  In Melbourne my office was by a corridor and it was generally possible to identify which of my colleagues was approaching along it by the sound of their footsteps. Our common experience is that we do recognise people at least partly by their gait. If gait patterns are so characteristic why is it so difficult to pick up abnormality in clinical gait analysis.

I suspect the answer is partly that gait is so varied and  characteristic. There is much more variability in normal walking than we appreciate. The creation myth of clinical gait analysis is that there is a well-defined pattern of normal walking and that our patients exhibit patterns that differ from this. The longer I think about this idea the less I believe it is true. When we tidy up our normative data by only plotting one standard deviation limits we get reasonably tight normal ranges but this is at the expense of excluding a third of the data (the +/- 1 SD limits only include 67% of the data by definition). If we plot two standard deviations, which represent 95% of the data, then we get much larger bars. Maximum knee extension in stance, for example varies between 5° of hyperextension and 18° of flexion across the healthy population (see figure below).

Knee 2 sd

The reason why it is so difficult to identify gait abnormality among our patients is at least partly because the normal variability between individuals is so large. Maybe on this basis gait as a biometric identifier is not quite so fanciful (although I still have reservations as to whether it will ever work on the basis of CCTV footage recorded in town centres or airports). Perhaps more importantly,  should be studying the characteristics of inter-person variation in gait patterns more closely in order to understand normal walking. In amongst all that variability are there specific characteristics that are invariant? If there are what does that tell us about the requirements of healthy walking? Gait variability within individuals is now seen as providing information about stability and by extension to falls risk (e.g. Callisaya et al. 2011). Maybe we should be paying more attention to gait variability between individuals.

PS Of course the other important factor in recognising people by their gait in every day life is the wide range of information we use to do so. When I recognised people by their footsteps from my office it was probably more to do with the sound that different footwear made as it was to do with temporal-spatial characteristics. One particularly famous CP surgeon was easily identifiable – partly from a mild asymmetry in his footfall pattern but more importantly from the characteristic jangle of coins and or keys in his pocket.