Effectiveness of Temporal Gait Event Detection Methods in Detecting Ice-Skating Temporal Events

A. Khandan, Jason P. Carey, H. Rouhani
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Abstract

Objective assessment of an ice skater’s motions during training sessions and matches helps coaches to monitor the player performance continuously. The initial step to developing a technology to assess the performance is to detect temporal skating events precisely. Our objective is to investigate the effectiveness of gait event detection methods in detecting skating events using Inertial Measurement Units (IMU). Four highly–cited gait event detection methods using inertial sensors in literature were adopted to detect Skate Strike (SS) and Blades-Off (BO) in skating. Aminian et al. (2002) suggested an algorithmic method based on wavelet analysis to detect heel strikes and toe-offs during gait from the angular velocity of lower limbs. Salarian et al. (2004) and Mariani et al. (2010) respectively, proposed that two negative peaks of the angular velocity of the lower limb and foot are associated with heel strike and toe-off. Mariani et al. (2013) evaluated 24 feature extraction methods to obtain the gait events and suggested that the maximum and minimum of the absolute value of the foot acceleration signal ( ‖ A ‖ ) can detect heel strike and toe-off in gait better than other signal features. These studies did not elaborate on the definition of the sensors frame or a functional calibration procedure to align these frames with anatomical frames of the body segments, and thus we had to add a presumption. To implement these methods in our studies, we presumed that the angular velocity described in these studies was the angular velocity about the anatomical frame of the segment. Therefore, we virtually rotated the IMU frames and aligned them with the anatomical studies above using a functional calibration procedure suggested in Nazarahari et al. (2019). We attached two IMU (Xsens Technologies, NL) on both skates and two on the lower legs of three participants. We also placed two pressure insoles (Pedar system) into their skates, used as a
时间步态事件检测方法在滑冰时间事件检测中的有效性
在训练和比赛中对滑冰运动员动作的客观评估有助于教练持续监控运动员的表现。开发一种评估表现的技术的第一步是精确地检测时间滑冰事件。我们的目的是研究步态事件检测方法在使用惯性测量单元(IMU)检测滑冰事件中的有效性。采用文献中引用率较高的四种基于惯性传感器的步态事件检测方法,对滑冰运动中的滑击(SS)和滑刃(BO)进行检测。Aminian et al.(2002)提出了一种基于小波分析的算法方法,从下肢的角速度检测步态中脚跟撞击和脚趾脱落。Salarian et al.(2004)和Mariani et al.(2010)分别提出下肢和足部角速度的两个负峰值与脚跟撞击和脚趾脱落有关。Mariani et al.(2013)评估了24种特征提取方法来获得步态事件,并提出足部加速度信号绝对值(‖A‖)的最大值和最小值比其他信号特征更能检测出步态中的足跟撞击和脱趾。这些研究没有详细说明传感器框架的定义或将这些框架与身体部分的解剖框架对齐的功能校准程序,因此我们必须添加一个假设。为了在我们的研究中实施这些方法,我们假设这些研究中描述的角速度是关于节段解剖框架的角速度。因此,我们实际上旋转了IMU框架,并使用Nazarahari等人(2019)建议的功能校准程序将其与上述解剖研究对齐。我们在两个冰鞋上安装了两个IMU (Xsens Technologies, NL),并在三个参与者的小腿上安装了两个IMU。我们还在他们的冰鞋里放了两个压力鞋垫(Pedar系统),作为一个
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