Researchers at the University of Bath have been working with GB skeleton athletes to develop a new type of motion capture technology that can accurately track the athlete during the push start phase of performance.
Skeleton is a winter sport where athletes rapidly accelerate on ice while pushing a sled before launching forwards on to it and navigating the corners of the track at speeds of up to 90 mph. Improvements of fractions of a second made at the start can make all the difference at the finish line. Therefore, it is useful for both athletes and their coaching team to be able to monitor the performance of athletes during this start phase and how they respond to training.
Standard methods of optical motion capture, using multiple reflective markers on the athlete and the sled to measure their movement in 3D space, are time consuming to set up and can interfere with the athlete’s natural performance.
To overcome this, researchers at the University of Bath’s motion research center, have developed a non-invasive markerless system using computer vision and deep learning methods to measure velocity and estimate poses by identifying body landmarks from regular image data.
The method was used at the University’s push-track training facility, a concrete slope with straight metal rails that allows athletes to train off-season using a wheeled practice sled.
The researchers used a set-up of nine cameras along both sides of the push-track for the markerless system and compared measurements with those obtained using the conventional 15-cam-era marker-based system.
They tested the system on 12 athletes for 33 push trials and found that there was very good agreement in the data from both systems (measured sled and athlete velocities were within 0.015 and 0.029 m/s, respectively), validating the use of the markerless method as a non-invasive and accurate alternative to the traditional marker-based system.
Dr. Laurie Needham said: “Our latest computer vision system allows us to break out of the laboratory and take biomechanics into the wild. The non-invasive nature of this approach not only means that we can capture push start information without interfering with the athlete’s training session, but we can do so in way that conforms with the current need for social distancing.”
According to the researchers, conventional (marker-based) technologies, which are used every day in their laboratory research, are not feasible in many elite sports training and competition environments. So, the future of sports biomechanics lies in finding accurate and unobtrusive markerless solutions. This system can provide information about British skeleton athletes’ start performances that was previously inaccessible to them and their coaches.
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