Deves Mathieu, Sauret Christophe, Alberca Ilona, Honnorat Lorian, Poulet Yoann, Hays Arnaud, Faupin Arnaud
Laboratoire Jeunesse Activité Physique et Sportive-Santé (J-AP2S), Université de Toulon, 83130 La Garde, France.
Fédération Française de Tennis (FFT), 75016 Paris, France.
Methods Protoc. 2024 Oct 18;7(5):84. doi: 10.3390/mps7050084.
Monitoring player mobility in wheelchair sports is crucial for helping coaches understand activity dynamics and optimize training programs. However, the lack of data from monitoring tools, combined with a lack of standardized processing approaches and ineffective data presentation, limits their usability outside of research teams. To address these issues, this study aimed to propose a simple and efficient algorithm for identifying locomotor tasks (static, forward/backward propulsion, pivot/tight/wide rotation) during wheelchair movements, utilizing kinematic data from standard wheelchair mobility tests.
Each participant's wheelchair was equipped with inertial measurement units-two on the wheel axes and one on the frame. A total of 36 wheelchair tennis and badminton players completed at least one of three proposed tests: the star test, the figure-of-eight test, and the forward/backward test. Locomotor tasks were identified using a five-step procedure involving data reduction, symbolic approximation, and logical pattern searching.
This method successfully identified 99% of locomotor tasks for the star test, 95% for the figure-of-eight test, and 100% for the forward/backward test.
The proposed method offers a valuable tool for the simple and clear identification and representation of locomotor tasks over extended periods. Future research should focus on applying this method to wheelchair court sports matches and daily life scenarios.
监测轮椅运动中的运动员移动性对于帮助教练了解活动动态并优化训练计划至关重要。然而，监测工具缺乏数据，再加上缺乏标准化的处理方法和无效的数据呈现方式，限制了它们在研究团队之外的可用性。为了解决这些问题，本研究旨在提出一种简单有效的算法，用于利用标准轮椅移动性测试的运动学数据识别轮椅运动过程中的运动任务（静态、向前/向后推进、枢轴/紧密/宽幅旋转）。
为每位参与者的轮椅配备惯性测量单元——两个安装在轮轴上，一个安装在车架上。共有36名轮椅网球和羽毛球运动员完成了三项提议测试中的至少一项：星形测试、8字形测试和向前/向后测试。使用包括数据简化、符号近似和逻辑模式搜索在内的五步程序识别运动任务。
该方法成功识别出星形测试中99%的运动任务、8字形测试中95%的运动任务以及向前/向后测试中100%的运动任务。
所提出的方法为长时间简单清晰地识别和呈现运动任务提供了一个有价值的工具。未来的研究应侧重于将该方法应用于轮椅场地运动比赛和日常生活场景。
