Marutani Yoshihiro, Konda Shoji, Ogasawara Issei, Yamasaki Keita, Yokoyama Teruki, Maeshima Etsuko, Nakata Ken
Graduate School of Sport and Exercise Sciences, Osaka University of Health and Sport Sciences, Kumatori, Osaka, Japan.
Department of Health and Sport Sciences, Graduate School of Medicine, Osaka University, Toyonaka, Osaka, Japan.
Front Physiol. 2023 Mar 24;14:1161182. doi: 10.3389/fphys.2023.1161182. eCollection 2023.
With the widespread use of wearable sensors, various methods to evaluate external physical loads using acceleration signals measured by inertial sensors in sporting activities have been proposed. Acceleration-derived external physical loads have been evaluated as a simple indicator, such as the mean or cumulative values of the target interval. However, such a conventional simplified indicator may not adequately represent the features of the external physical load in sporting activities involving various movement intensities. Therefore, we propose a method to evaluate the external physical load of tennis player based on the histogram of acceleration-derived signal obtained from wearable inertial sensors. Twenty-eight matches of 14 male collegiate players and 55 matches of 55 male middle-aged players wore sportswear-type wearable sensors during official tennis matches. The norm of the three-dimensional acceleration signal measured using the wearable sensor was smoothed, and the rest period (less than 0.3 G of at least 5 s) was excluded. Because the histogram of the processed acceleration signal showed a bimodal distribution, for example, high- and low-intensity peaks, a Gaussian mixture model was fitted to the histogram, and the model parameters were obtained to characterize the bimodal distribution of the acceleration signal for each player. Among the obtained Gaussian mixture model parameters, the linear discrimination analysis revealed that the mean and standard deviation of the high-intensity side acceleration value accurately classified collegiate and middle-aged players with 93% accuracy; however, the conventional method (only the overall mean) showed less accurate classification results (63%). The mean and standard deviation of the high-intensity side extracted by the Gaussian mixture modeling is found to be the effective parameter representing the external physical load of tennis players. The histogram-based feature extraction of the acceleration-derived signal that exhibit multimodal distribution may provide a novel insight into monitoring external physical load in other sporting activities.
随着可穿戴传感器的广泛应用，人们提出了各种利用惯性传感器在体育活动中测量的加速度信号来评估外部身体负荷的方法。加速度衍生的外部身体负荷已被评估为一个简单指标，例如目标区间的平均值或累计值。然而，这种传统的简化指标可能无法充分代表涉及各种运动强度的体育活动中外部身体负荷的特征。因此，我们提出了一种基于从可穿戴惯性传感器获得的加速度衍生信号的直方图来评估网球运动员外部身体负荷的方法。14名男性大学生球员的28场比赛以及55名男性中年球员的55场比赛在正式网球比赛期间穿着运动服式可穿戴传感器。对使用可穿戴传感器测量的三维加速度信号的范数进行平滑处理，并排除休息期（至少5秒内小于0.3G）。由于处理后的加速度信号的直方图显示出双峰分布，例如高强度和低强度峰值，因此将高斯混合模型拟合到直方图上，并获得模型参数以表征每个球员加速度信号的双峰分布。在获得的高斯混合模型参数中，线性判别分析表明，高强度侧加速度值的平均值和标准差以93%的准确率准确地对大学生球员和中年球员进行了分类；然而，传统方法（仅总体平均值）显示出较低的准确分类结果（63%）。发现通过高斯混合建模提取的高强度侧的平均值和标准差是代表网球运动员外部身体负荷的有效参数。对呈现多峰分布的加速度衍生信号进行基于直方图的特征提取，可能为监测其他体育活动中的外部身体负荷提供新的见解。
