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首页> 外文会议>Conference on BioMEMS and Nanotechnology; 20071205-07; Canberra(AU) >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：使用人体安装惯性传感器的精英半管滑雪中性能变量的特征提取

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摘要

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：对精英级别的半管滑雪比赛的最新分析显示，许多运动特定的关键性能变量（KPV）与得分密切相关。这些变量的信息很难获得和分析，这取决于视频数据的收集和劳动密集型手动后期处理。本文介绍了惯性传感器的使用，它是一种用户友好的替代方法，并随后实现了信号处理例程，以最终向教练和运动员提供自动化的，针对运动的反馈。作者最近表明，在精英半管滑雪比赛中获得的总飞行时间（TAT）和平均旋转度（ADR）的关键性能变量（KPV）与运动员的主观判断得分密切相关。本文利用微电化学系统（MEMS）传感器（三轴加速度计）证明，使用基本的信号处理技术可以准确地检测和计算半管滑雪期间获得的空中时间（AT）。空中杂技动作变化的特征以及相关的精确旋转度（DR）的计算是该研究的可能扩展。开发的技术使用了两遍方法来检测频域中功率密度的半管滑雪板的位置，随后在时域中使用基于阈值的搜索算法来计算与单个空中杂技动作相关的空中时间。这项技术正确地识别了每个半管滑雪运动中100％的空中杂技动作的飞行时间（n =来自4个对象的92项空中杂技动作），并且与基于视频的空中时间参考标准显示出非常强的相关性（r = 0.78±0.08; p值<0.0001; SEE = 0.08×/÷1.16;平均偏差= -0.03±0.02s）（值±或×/÷95％CL）。

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