If joints do no longer work as usual, humans tend to compensate this by unconsciously adapting their motions. In the case of knee arthrosis, or excessive joint wear, they shift the weight to the healthy leg. This relieves the worn knee joint, but also delays the pain that would indicate the start of arthrosis. Based on a computer-supported gait analysis, researchers are developing an early warning system for routine prevention.

To record motions in detail, scientists attach a total of 39 markers to the body of the test subject. (Willibald Müller, KIT)
Researchers are working on compiling a catalog of human motion patterns. Deviations in execution are described mathematically by the probability of their occurrence. In parallel, sports scientists also collect motion data of patients who are already suffering from knee arthrosis. With them, the common features of motion sequences are observed.

For motions to be analyzed mathematically on the computer, the scientists first have to image them digitally. For this purpose, they attach 39 markers to the body of the test person using adhesive tape. When the test person moves under infrared light, the light is reflected by the markers and recorded by cameras. On the computer, the joint markers appear as image points. In addition, the values of two force measurement plates are recorded. If the test person walks across these plates, they measure when and where a foot touches the plate and which forces act between the floor and the test person. Light barriers in front of and behind the force measurement plates measure the average speed.

Using these values, calculation models can identify various motion patterns. They can determine whether somebody walks or runs, and whether he moves on plane ground or climbs a slope. The system can distinguish persons by their gait alone. However, this only works if the scientists have “trained” their computer models accordingly.