Reflecting on the latest Sports Engineering Research on Winter Sports
Every four years, the Winter Olympics is an opportunity to watch athletes in awe, as they navigate their way over snow and ice to Olympic glory. All winter sports share a common requirement for the equipment used to support a participant’s performance, safety or enjoyment whether they be recreational or elite. The Sports Engineering journal has been active in ensuring that the equipment and technology used in winter sports is grounded in research. As such, we have published three topical collections on winter sports research the latest recently published ahead of the 2022 Olympic Games [1,2, 3]. As we approach the 2022 games, it is a chance to reflect on the key themes from the latest collection [3] and consider what the future might hold for winter sports research.
Wearable sensors and instrumented equipment were popular approaches in the most recent topical collection. These types of sensors or instrumentation provide an opportunity to capture unique insights into athlete techniques or equipment performance, in what would otherwise be challenging environments. Applications included, capturing knee angles of downhill skiers, computing the pole angle of cross-country skiers, quantifying the mechanical behaviour of downhill skis and classifying an athlete’s impairment in para winter sport. The full potential of such sensors has not yet been fully realised but there are exciting prospects, such as those proposed by the authors Hermann et al. [4] on creating a mechatronic ski binding system which adapts a bindings release setting according to the current risk of knee injury.
Artificial intelligence has been applied to many fields and machine learning algorithms are popular approaches. One paper proposed the use of machine learning techniques for predicting push-off forces in speed skating which was again a potential alternative way to capture data in challenging winter sports environments [5]. As an Associate Editor of Sports Engineering, we anticipate growth in the application of machine learning approaches to Sports Engineering research, but also recognise the need to ensure that the methods used are rigorous and well defined. Modelling approaches also proved popular in the winter sports engineering research. The modelling helped to explain real-world situations, such as sideslipping in alpine skiing, quantifying the magnitude of impacts of ice hockey helmets and lower limb-ski boot pressures related to ergonomics and comfort. Fontanella et al. [6]’s paper on modelling of lower limb- ski boot pressures went on to win the Sports Engineering’s journal best paper of the year award for 2021. Their use of advanced experimental techniques as input parameters for detailed computational models provided new insights into these ski boot pressures that could be used to help inform the design of more comfortable and ergonomic boots in the future. A prospect which I’m sure would be a welcome relief for any skiers reading this blog!
The varied approaches included in this collection highlights the potential for collaborations between these approaches and people in the field, (i.e., companies developing wearable sensors and engineers instrumenting the equipment) which will help us to ensure that future winter sport technology and innovations have been rigorously tested and validated.
References
- Federolf, P., Strangwood, M. Special issue on winter sports. Sports Eng 16, 195–196 (2013). https://doi.org/10.1007/s12283-013-0142-y
- Allen, T., Sandbakk, Ø. & Lindinger, S.J. Winter sports special issue. Sports Eng 20, 243–244 (2017). https://doi.org/10.1007/s12283-017-0256-8
- Mears, A.C., Pearsall, D.J., Scher, I.S. et al. Winter sports topical collection. Sports Eng 24, 27 (2021). https://doi.org/10.1007/s12283-021-00364-z
- Hermann, A., Ostarhild, J., Mirabito, Y. et al. Stretchable piezoresistive vs. capacitive silicon sensors integrated into ski base layer pants for measuring the knee flexion angle. Sports Eng 23, 22 (2020). https://doi.org/10.1007/s12283-020-00336-9
- Krumm, D., Kuske, N., Neubert, M. et al. Determining push-off forces in speed skating imitation drills. Sports Eng 24, 25 (2021). https://doi.org/10.1007/s12283-021-00362-1
- Fontanella, C.G., Arduino, A., Toniolo, I. et al. Computational methods for the investigation of ski boots ergonomics. Sports Eng 24, 15 (2021). https://doi.org/10.1007/s12283-021-00352-3
Dr Aimée Mears is a Senior Lecturer at the Sports Technology Institute, Loughborough University, UK. Prior to the lectureship role, she received her PhD in sports technology and biomechanics at Loughborough University in 2013. She then carried out a post-doctoral fellow role at the University of Calgary. Her research interests are on the human-equipment interactions specifically using biomechanics and data analytics to understand these interactions in areas such as pregnancy and post-pregnancy exercise and golf. She has been Associate Editor of the Sports Engineering journal since 2015 and helped to curate the latest Winters Sports Topical Collection.