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Delsys Prize 2005 Winning Proposal

Discriminating between normal and abnormal EMG profiles in walking

Dr. A.L. Hof, University of Groningen, Center for Human Movement Sciences, Groningen, The Netherlands


A method is proposed to predict the normal EMG activity of human walking at any walking speed. The measured profile is compared to the predicted one and the deviation is calculated. In the deviation, temporal differences are taken heavily into account, while differences in amplitude, which are less relevant, are represented in a separate parameter. The method has already been proven useful in clinical research.


This research program consists of the following steps: (1) to define what the normal EMG profile is, (2) to see if some measured profile deviates from this normal profile and to quantify the deviation, and (3) to apply the method on clinical problems. Not completely surprising, the first step of this program consisted in collecting data of smoothed rectified EMGs of 14 muscles from a group of ten supposedly normal subjects [1]. This posed already a major difficulty: the EMG profile changes with walking speed. We were able to solve this problem by finding out that any EMG profile could be resolved in two sets of profiles, one constant and one increasing linearly with speed. Both of these profile sets are constant when presented on a relative timescale, from heel contact to heel contact. In this way the EMG profile can be predicted for any walking speed. Other groups have already shown that this approach is valid even for very low speeds, as seen sometimes in pathology[2]. Data on running will become available soon [3]. The second problem was addressed by a quadratic error measure. In the procedure temporal differences, which are very important, and differences in amplitude, which are to a high degree determined by electrode placement and muscle anatomy, are treated separately [4]. In our clinical gait analysis laboratory this method is in daily use. It enables to quickly detect abnormalities in EMG activation, e.g. in children with cerebral palsy. To interpret these findings in terms of function or dysfunction remains a difficult task, in which many other aspects of the case need to be taken into consideration. Another application refers to patients with a rupture of the anterior cruciate ligament [5]. After the accident part of the patients learn themselves to cope with the deficit and to function more or less normally: “copers”. Others don’t, even after a year or more, and often apply for a reconstruction of the ligament, “non-copers”. It turned out that in this case the “normals”, the coper group, showed an “abnormal” EMG: an activity in one or more of the hamstring muscles during stance. As it seems, the force from the active hamstrings helps to stabilize the knee. The innovative aspect of this project is in the method of reconstructing the rectified EMG profile at any speed and in the algorithm for detecting and quantifying the degree of abnormality. These methods can be expected to be useful for clinical research. On a more fundamental level, they give support to the Central Program Generator theory for human locomotion.


a) Averaged EMG profiles for GM muscle in walking at speeds of 0.75, 1.00, 1.25, 1.50, 1.75 m.s-1 (from bottom to top).

b) Average EMG profile at a single percentage of stride as a fuction of normalised walking speed. The percentages are: o- 30% , x- 40% , Ñ- 46% . EMG values are either independent of speed, at 30% and 46%, or increase linearly with speed.

c) Functions f0(p) (thick line) and f1(p) (dashed) for GM. The function f1(p) can be interpreted as the slope of the lines in Figure b, for every stride percentage p. The function f0(p) consists of the intercepts of these lines at the normalised speed 0.16, arrows in Figure b.

d) Quality of the fit of the two estimated profiles. Solid line: average profile of GM at 1.25 m.s-1. Dashed line: estimation from f0(p) and f1(p), eq. (4). Dotted line: estimation from F0 and F1, eq. (7). Note that both the F0 (1) and the F1 (1) functions used for the GM profile have been derived from the SO profiles.

Avg EMG profiles for GM muscle in walking  Avg EMG profile at a single percentage of stride

functions at normalized speeds  quality of fit of estimated profiles


1.Hof AL, Elzinga H, Grimmius W, Halbertsma JPK. Speed dependency of averaged EMG profiles in walking. Gait & Posture 2002; 16:78-86.

2.Otter AR den, Geurts ACH, Mulder T, Duysens J. Speed related changes in muscle activity from normal to very slow walking speeds 56. Gait & Posture 2004; 19:270-278.

3.Gazendam M, Hof AL. Speed dependence of averaged EMG profiles in running. Differences with walking. (MA thesis, 2004);

4.Hof AL, Elzinga H, Grimmius W, Halbertsma JPK. Detection of non-standard EMG profiles in walking. Gait & Posture 2005; 21:171-177.

5.Boerboom AL, Hof AL, Halbertsma JPK, Raaij J.J.A.M.van, Schenk W, Diercks R.L., Horn J.R.van. Atypical hamstrings electromyographic activity as a compensatory mechanism in anterior cruciate ligament deficiency. Knee Surg Sports Traumatol Arthrosc 2001; 9:211-216

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