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

Lukas Schulthess, T. Ingolfsson, Serin Huber, Marc Nölke, Michele Magno, Luca Benini, Christoph Leitner
{"title":"A leap into the future: Towards an augmented reality learning environment in ski-jumping","authors":"Lukas Schulthess, T. Ingolfsson, Serin Huber, Marc Nölke, Michele Magno, Luca Benini, Christoph Leitner","doi":"10.36950/2024.2ciss069","DOIUrl":null,"url":null,"abstract":"Introduction \nProfessional 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). \nMethods \nThe 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. \nResults \nWe 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. \nDiscussion/Conclusion \nThis 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. \nReferences \nElfmark, 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 \nMulder, 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 \nMüller, 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 \nSchulthess, 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","PeriodicalId":415194,"journal":{"name":"Current Issues in Sport Science (CISS)","volume":"118 4","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Issues in Sport Science (CISS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36950/2024.2ciss069","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Introduction 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). Methods 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. Results 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. Discussion/Conclusion 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. References 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
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
飞跃未来:在跳台滑雪中建立增强现实学习环境
导言 职业运动竞争激烈。例如,在跳台滑雪运动中,即使是起飞和飞行中的微小变化也会对胜负产生决定性的影响(Elfmark 等人,2022 年)。运动员必须在短时间内学会解决复杂的运动控制问题,同时还要面对恶劣的环境条件,如风、雪和低温。实际起飞发生在眨眼之间(约 300 毫秒),应立即达到有利于空气动力学的稳定飞行位置。跑动中对重心的精细控制有利于在起飞过程中以高速产生最佳动力(Müller,2008 年)。在飞行过程中,运动员可以通过改变身体姿势来主动影响空气动力学。然而,非最佳飞行姿势会在无意中或由于不正确的行为而出现。此外,作为一项非周期性运动,跳台滑雪的重复率低,影响了训练效果。因此,提高每次跳跃的学习率是成功的关键因素。生物反馈方法已被证明可加速运动员的运动学习(Mulder 和 Hulstijn,1985 年)。目前跳台滑雪领域的传感器技术并不符合真正可穿戴系统的要求,该系统必须节能、不显眼、不易察觉(以免干扰自然运动行为和跳跃技术),尤其是必须配备无线连接(用于实时数据分析,例如在训练塔上;Schulthess 等人,2023 年)。方法 拟议的系统由两个多传感器节点组成:一个节点隐藏在改装过的跳台滑雪靴中,集成了三个力敏电阻传感器,用于测量跳台滑雪运动员脚底的压力分布。第二个传感器节点位于滑雪镜中,包含 RGB LED,可在外围视野中提供视觉生物反馈。结果 我们计算出系统的总功耗为 2.52 mW,满足了电池充电后可运行多天的要求。我们的设备体位分类模型从实验室记录的数据中识别体位的准确率达到 92.7%。讨论/结论 这是跳台滑雪领域第一个真正意义上的可穿戴训练系统,为专业运动员提供了全新的增强体验,旨在加速运动学习。此外,生物力学相关特征的实时数据传输也为训练团队的工作提供了便利,并在未来使电视转播更具信息性和娱乐性。参考文献 Elfmark, O., Ettema, G., & Gilgien, M. (2022)。跳台滑雪中稳定滑行阶段的评估。https://doi.org/10.1016/j.jbiomech.2022.111139 Mulder, T., & Hulstijn, W. (1985)。新运动任务学习中的感觉反馈。运动行为杂志》,17(1),110-128。 https://doi.org/10.1080/00222895.1985.10735340 Müller, W. (2008)。跳台滑雪的性能因素。见 H. Nørstrud(编辑),《运动空气动力学》(第 139-160 页)。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:用于跳台滑雪成绩分析和生物反馈的智能传感器系统。https://doi.org/10.48550/arXiv.2309.14455。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
From supercrip to techno supercrip Associations between daily movement behaviors, sleep, and affect in older adults: An ecological momentary assessment study Position statement regarding the current standing of exercise therapy in Austria (Positionspapier zur Situation der Trainingstherapie in Österreich) The Perceived Instrumental Effects of Maltreatment in Sport (PIEMS) scale: Translation, (cross-)validation, and short-form development of the German version Who’s better? Adaptive comparative judgment of dance performances
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1