Hu Xinyao, Liang Fenjie, Fang Zhimeng, Qu Xingda, Zhao Zhong, Ren Zhanbing, Cai Wenfei
Institute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen City, Guangdong Province, China.
Department of Physical Education, Shenzhen University, Shenzhen City, Guangdong Province, China.
Sports Biomech. 2024 Nov;23(11):2006-2020. doi: 10.1080/14763141.2021.1990384. Epub 2021 Oct 21.
The Ollie movement is about the most dangerous fundamental skateboarding skill. This study proposed a peak heuristic algorithm to detect the key temporal events of the Ollie movement during skateboarding using IMUs. The proposed algorithm was used to detect four key temporal events including take-off (TO), peak flight height (HP), front wheel landing (FL), and back wheel landing (RL). Based on these temporal events, three temporal phases including ascending, descending, and flight were identified. The results showed that our proposed method could help accurately identify these key temporal events and phases. Knowledge of the temporal information about the Ollie movement could provide a basis for quantitative assessment of riders' performance and injury risks. Practically, this proposed algorithm can benefit the outdoor injury risk monitoring of the skateboarding movement.
豚跳动作是最危险的基本滑板技能之一。本研究提出了一种峰值启发式算法，用于利用惯性测量单元（IMU）检测滑板运动中豚跳动作的关键时间事件。所提出的算法用于检测四个关键时间事件，包括起跳（TO）、飞行高度峰值（HP）、前轮落地（FL）和后轮落地（RL）。基于这些时间事件，确定了包括上升、下降和飞行在内的三个时间阶段。结果表明，我们提出的方法有助于准确识别这些关键时间事件和阶段。关于豚跳动作的时间信息知识可为定量评估骑手的表现和受伤风险提供依据。实际上，这种提出的算法可有益于滑板运动的户外受伤风险监测。
