A new algorithm detects “big air” jumps and other types of jumps in skiing automatically and in real time. This new method could improve training and performance assessment in the professional and recreational sectors.

Jumps are an important performance and assessment criterion in skiing, freestyle, snowboarding, ski jumping, and figure skating. A detailed insight is therefore essential for both athletes and coaches. The established methodology for jump detection is based on measuring tapes, laser-based distance calculation, force plates, pressure sensors, and video analysis. However, these methods can only be transferred to measurements directly on the slope to a limited extent. That is why we rely on minimally invasive on-body sensor technology that is worn directly on the body: Inertial measurement units (IMUs) attached to the ski boot record acceleration data and enable precise jump detection with minimal interference. An evaluation algorithm specially developed for alpine skiing provides a precise real-time analysis. It was intensively tested in practical trials directly on the slopes.

Athletes can use it to improve their technique, and coaches can draw conclusions about the workload of the athletes. This allows for better training management to decrease the risk of injury. The algorithm could also be integrated into consumer products to improve the skiing experience for recreational athletes, for example, through playful feedback in gamification applications.

The algorithm is now set to be extended to other winter sports such as snowboarding and ski jumping. A transfer to other sports is also conceivable.

The research was funded by the federal ministries BMK and BMDW and the federal states of Salzburg, Upper Austria, and Tyrol as part of the COMET research program in the project “Digital Motion in Sport, Fitness and Wellbeing” (DiMo).

About the project:

• Digital Motion in Sports, Fitness & Well-being
• DiMo-NEXT – Next Level of Digital Motion in Sports, Fitness and Well-being

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