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首页> 外文会议>Conference on BioMEMS and Nanotechnology >Feature Extraction of Performance Variables in Elite Half-Pipe Snowboarding Using Body Mounted Inertial Sensors
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Feature Extraction of Performance Variables in Elite Half-Pipe Snowboarding Using Body Mounted Inertial Sensors

机译：使用车身安装惯性传感器精英半管滑雪板性能变量的特征提取

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Recent analysis of elite-level half-pipe snowboard competition has revealed a number of sport specific key performance variables (KPV's) that correlate well to score. Information on these variables is difficult to acquire and analyse, relying on collection and labour intensive manual post processing of video data. This paper presents the use of inertial sensors as a user-friendly alternative and subsequently implements signal processing routines to ultimately provide automated, sport specific feedback to coaches and athletes. The author has recently shown that the key performance variables (KPV's) of total air-time (TAT) and average degree of rotation (ADR) achieved during elite half-pipe snowboarding competition show strong correlation with an athlete's subjectively judged score. Utilising Micro-Electrochemical System (MEMS) sensors (tri-axial accelerometers) this paper demonstrates that air-time (AT) achieved during half-pipe snowboarding can be detected and calculated accurately using basic signal processing techniques. Characterisation of the variations in aerial acrobatic manoeuvres and the associated calculation of exact degree of rotation (DR) achieved is a likely extension of this research. The technique developed used a two-pass method to detect locations of half-pipe snowboard runs using power density in the frequency domain and subsequently utilises a threshold based search algorithm in the time domain to calculate air-times associated with individual aerial acrobatic manoeuvres. This technique correctly identified the air-times of 100 percent of aerial acrobatic manoeuvres within each half-pipe snowboarding run (n = 92 aerial acrobatic manoeuvres from 4 subjects) and displayed a very strong correlation with a video based reference standard for air-time calculation (r = 0.78 ± 0.08; p value < 0.0001; SEE = 0.08 ×/÷1.16; mean bias = -0.03 ± 0.02s) (value ± or ×/÷95% CL).
机译：最近对精英半管滑雪板竞赛的分析揭示了许多运动特定的关键性能变量（KPV），以得分很好。有关这些变量的信息难以获取和分析，依赖于集合和劳动密集型手册后的视频数据处理。本文介绍了惯性传感器作为用户友好的替代方案，随后实现信号处理程序，最终提供自动化，体育特定反馈与教练和运动员。作者最近表明，在精英半管滑雪板上竞争期间实现的总空气时间（TAT）和平均旋转程度（ADR）的关键性能变量（ADR）表现出与运动员的主观判断得分强烈相关。利用微电化学系统（MEMS）传感器（三轴加速度计）本文表明，可以使用基本信号处理技术准确地检测和计算半管滑雪板期间实现的空气时间（AT）。鸟瞰杂技演习的变化以及所实现精确旋转程度（DR）的相关计算的表征是该研究的可能扩展。该技术使用了一种双通方法来使用频域中的功率密度检测半管滑雪板的位置，随后在时域中使用基于阈值的搜索算法来计算与各个鸟瞰杂技动作相关的空气时间。该技术正确地确定了每个半管滑雪板（N = 92个来自4个受试者的空中杂技演奏）内的鸟类杂技动作的烟气时序，并与空气时间计算的基于视频的参考标准显示出非常强烈的相关性（r = 0.78±0.08; p值<0.0001;见= 0.08×/÷1.16;平均偏见= -0.03±0.02s）（值±或×/÷95％CL）。

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