PDF(Pubmed)
Abstract：
Skiing technique and performance improvements are crucial for athletes and enthusiasts alike. This study presents SnowMotion, a digital human motion training assistance platform that addresses the key challenges of reliability, real-time analysis, usability, and cost in current motion monitoring techniques for skiing. SnowMotion utilizes wearable sensors fixed at five key positions on the skier\'s body to achieve high-precision kinematic data monitoring. The monitored data are processed and analyzed in real time through the SnowMotion app, generating a panoramic digital human image and reproducing the skiing motion. Validation tests demonstrated high motion capture accuracy (cc > 0.95) and reliability compared to the Vicon system, with a mean error of 5.033 and a root-mean-square error of less than 12.50 for typical skiing movements. SnowMotion provides new ideas for technical advancement and training innovation in alpine skiing, enabling coaches and athletes to analyze movement details, identify deficiencies, and develop targeted training plans. The system is expected to contribute to popularization, training, and competition in alpine skiing, injecting new vitality into this challenging sport.
摘要：
滑雪技术和性能的提高对运动员和爱好者都至关重要。这项研究提出了SnowMotion，数字人体运动训练辅助平台，解决了可靠性的关键挑战，实时分析,可用性，以及当前滑雪运动监测技术的成本。SnowMotion利用固定在滑雪者身体五个关键位置的可穿戴传感器来实现高精度的运动学数据监测。通过SnowMotion应用程序对监控的数据进行实时处理和分析，生成全景数字人体图像并再现滑雪运动。验证测试表明，与Vicon系统相比，运动捕捉精度高（cc>0.95），可靠性高，对于典型的滑雪动作，平均误差为5.033，均方根误差小于12.50。SnowMotion为高山滑雪技术进步和训练创新提供了新思路,使教练和运动员能够分析运动细节，识别缺陷，制定有针对性的培训计划。该系统有望有助于普及，培训,和高山滑雪比赛，为这项具有挑战性的运动注入新的活力。
