Sports Performance Research Institute New Zealand, AUT University, Auckland, New Zealand.
School of Sport, Manukau Institute of Technology, Auckland, New Zealand.
J Sports Sci. 2021 Jun;39(12):1402-1409. doi: 10.1080/02640414.2021.1876312. Epub 2021 Jan 22.
This study examined whether an inertial measurement unit (IMU), in combination with machine learning, could accurately predict two indirect measures of bowling intensity through ball release speed (BRS) and perceived intensity zone (PIZ). One IMU was attached to the thoracic back of 44 fast bowlers. Each participant bowled 36 deliveries at two different PIZ zones (Zone 1 = 24 deliveries at 70% to 85% of maximum perceived bowling effort; Zone 2 = 12 deliveries at 100% of maximum perceived bowling effort) in a random order. IMU data (sampling rate = 250 Hz) were downsampled to 125 Hz, 50 Hz, and 25 Hz to determine if model accuracy was affected by the sampling frequency. Data were analysed using four machine learning models. A two-way repeated-measures ANOVA was used to compare the mean absolute error (MAE) and accuracy scores (separately) across the four models and four sampling frequencies. Gradient boosting models were shown to be the most consistent at measuring BRS (MAE = 3.61 km/h) and PIZ (F-score = 88%) across all sampling frequencies. This method could be used to measure BRS and PIZ which may contribute to a better understanding of overall bowling load which may help to reduce injuries.
这项研究旨在探讨惯性测量单元（IMU）与机器学习相结合，是否能够通过球速（BRS）和感知强度区（PIZ）这两个间接指标准确预测投球强度。研究中，将一个 IMU 附着在 44 名快速投球手的背部。每位参与者以两种不同的 PIZ 区（Zone 1=以 70%至 85%最大感知投球强度的 24 次投球；Zone 2=以 100%最大感知投球强度的 12 次投球）随机顺序进行 36 次投球。IMU 数据（采样率为 250Hz）以 125Hz、50Hz 和 25Hz 进行下采样，以确定模型准确性是否受采样频率影响。研究使用四个机器学习模型对数据进行分析。使用双向重复测量方差分析比较四个模型和四个采样频率的平均绝对误差（MAE）和准确率得分。梯度提升模型在测量 BRS（MAE=3.61km/h）和 PIZ（F 得分=88%）方面表现最为一致，在所有采样频率下均具有一致性。这种方法可用于测量 BRS 和 PIZ，这可能有助于更好地理解整体投球负荷，从而有助于减少受伤。
