A leap into the future: Towards an augmented reality learning environment in ski-jumping

Keywords: wearable, sensor, tinyML



Professional sports are fiercely competitive. In ski jumping, for example, even small changes in take-off and flight can make a decisive difference between victory and defeat (Elfmark et al., 2022). Within the short time of a jump, athletes must learn to solve complex motor control problems while being exposed to harsh environmental conditions, e.g., wind, snow, and low temperatures. The actual take-off occurs within the blink of an eye (~300 ms) and an aerodynamically favourable and stable flight position should be attained immediately. Fine control of the centre of gravity in the in-run favours high speeds to generate optimum momentum during take-off (Müller, 2008). In flight, athletes can voluntarily influence aerodynamics by changing their body position. However, non-optimal flight positions occur unintentionally or due to incorrect behaviour. Furthermore, as a non-cyclical sport, ski jumping suffers from low repetition rates, which impairs the effectiveness of training. Thus, increasing the learning rate for each jump is a key success factor. Biofeedback methods have been shown to accelerate motor learning in athletes (Mulder & Hulstijn, 1985). Current sensor technologies in ski jumping do not meet the requirements for a truly wearable system, which must be energy-efficient, unobtrusive and barely noticeable (so as not to interfere with natural movement behaviour and jumping technique) and, in particular, must be equipped with a wireless link (for real-time data analysis, e.g. on the trainer tower; Schulthess et al., 2023).


The proposed system consists of two multi-sensor nodes: One node is hidden in a modified ski jumping boot, integrating three force-sensing resistor sensors to measure the pressure distribution on the foot soles of ski jumpers. The second sensor node is located in the ski goggles and contains RGB LEDs that provide visual biofeedback in the peripheral vision.


We have calculated the total power consumption of our systems to be 2.52 mW, meeting requirements for multi-day operation between battery recharges. Our on-device body position classification model achieves an accuracy of 92.7% in recognising body positions from data recorded in the laboratory.


This is the first truly wearable training system in ski jumping, offering professional athletes a new augmented experience, aimed at accelerating motor learning. In addition, the real-time data transmission of biomechanically relevant characteristics facilitates the work of the training team and could in the future enable more informative and entertaining television broadcasts.


Elfmark, O., Ettema, G., & Gilgien, M. (2022). Assessment of the steady glide phase in ski jumping. Journal of Biomechanics, 139, 111139. https://doi.org/10.1016/j.jbiomech.2022.111139

Mulder, T., & Hulstijn, W. (1985). Sensory feedback in the learning of a novel motor task. Journal of Motor Behavior, 17(1), 110–128. https://doi.org/10.1080/00222895.1985.10735340

Müller, W. (2008). Performance factors in ski jumping. In H. Nørstrud (Ed.), Sport Aerodynamics (pp. 139– 160). Springer. https://doi.org/10.1007/978-3-211-89297-8_8

Schulthess, L., Ingolfsson, T. M., Nölke, M., Magno, M., Benini, L., & Leitner, C. (2023). Skilog: A smart sensor system for performance analysis and biofeedback in ski jumping. https://doi.org/10.48550/arXiv.2309.14455

How to Cite
Schulthess, L., Ingolfsson, T., Huber, S., Nölke, M., Magno, M., Benini, L., & Leitner, C. (2024). A leap into the future: Towards an augmented reality learning environment in ski-jumping. Current Issues in Sport Science (CISS), 9(2), 069. https://doi.org/10.36950/2024.2ciss069