Department of Economics, Engineering, Society and Business Organization (DEIM), University of Tuscia, 01100 Viterbo, Italy.
Department of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy.
Sensors (Basel). 2019 Mar 25;19(6):1461. doi: 10.3390/s19061461.
The validity of results in race walking is often questioned due to subjective decisions in the detection of faults. This study aims to compare machine-learning algorithms fed with data gathered from inertial sensors placed on lower-limb segments to define the best-performing classifiers for the automatic detection of illegal steps. Eight race walkers were enrolled and linear accelerations and angular velocities related to pelvis, thighs, shanks, and feet were acquired by seven inertial sensors. The experimental protocol consisted of two repetitions of three laps of 250 m, one performed with regular race walking, one with loss-of-contact faults, and one with knee-bent faults. The performance of 108 classifiers was evaluated in terms of accuracy, recall, precision, F1-score, and goodness index. Generally, linear accelerations revealed themselves as more characteristic with respect to the angular velocities. Among classifiers, those based on the support vector machine (SVM) were the most accurate. In particular, the quadratic SVM fed with shank linear accelerations was the best-performing classifier, with an F1-score and a goodness index equal to 0.89 and 0.11, respectively. The results open the possibility of using a wearable device for automatic detection of faults in race walking competition.
由于在检测犯规时存在主观判断，因此径赛走步项目的比赛结果的有效性经常受到质疑。本研究旨在比较基于放置在下肢各部位的惯性传感器收集的数据的机器学习算法，以确定用于自动检测违规步伐的最佳分类器。 招募了 8 名径赛走步运动员，通过 7 个惯性传感器获取与骨盆、大腿、小腿和脚部相关的线性加速度和角速度。 实验方案包括两次重复的三个 250 米的圈数，一次是正常的径赛走步，一次是失去接触的犯规，一次是膝盖弯曲的犯规。 108 个分类器的性能通过准确性、召回率、精度、F1 分数和良好指数进行评估。 通常，线性加速度相对于角速度表现出更强的特征。 在分类器中，基于支持向量机（SVM）的分类器最准确。 特别是，基于小腿线性加速度的二次 SVM 是表现最佳的分类器，其 F1 分数和良好指数分别为 0.89 和 0.11。 这些结果为使用可穿戴设备自动检测径赛走步比赛中的犯规行为提供了可能性。
