Mascia Guido, De Lazzari Beatrice, Camomilla Valentina
Department of Movement, Human and Health Science, University of Rome "Foro Italico", Rome, Italy.
Interuniversity Centre of Bioengineering of the Human Neuromusculoskeletal System, University of Rome "Foro Italico", Rome, Italy.
Front Sports Act Living. 2023 Feb 9;5:1112739. doi: 10.3389/fspor.2023.1112739. eCollection 2023.
The peak height reached in a countermovement jump is a well established performance parameter. Its estimate is often entrusted to force platforms or body-worn inertial sensors. To date, smartphones may possibly be used as an alternative for estimating jump height, since they natively embed inertial sensors.
For this purpose, 43 participants performed 4 countermovement jumps (172 in total) on two force platforms (gold standard). While jumping, participants held a smartphone in their hands, whose inertial sensor measures were recorded. After peak height was computed for both instrumentations, twenty-nine features were extracted, related to jump biomechanics and to signal time-frequency characteristics, as potential descriptors of soft tissues or involuntary arm swing artifacts. A training set (129 jumps - 75%) was created by randomly selecting elements from the initial dataset, the remaining ones being assigned to the test set (43 jumps - 25%). On the training set only, a Lasso regularization was applied to reduce the number of features, avoiding possible multicollinearity. A multi-layer perceptron with one hidden layer was trained for estimating the jump height from the reduced feature set. Hyperparameters optimization was performed on the multi-layer perceptron using a grid search approach with 5-fold cross validation. The best model was chosen according to the minimum negative mean absolute error.
The multi-layer perceptron greatly improved the accuracy (4 cm) and precision (4 cm) of the estimates on the test set with respect to the raw smartphone measures estimates (18 and 16 cm, respectively). Permutation feature importance was performed on the trained model in order to establish the influence that each feature had on the outcome. The peak acceleration and the braking phase duration resulted the most influential features in the final model. Despite not being accurate enough, the height computed through raw smartphone measures was still among the most influential features.
The study, implementing a smartphone-based method for jump height estimates, paves the way to method release to a broader audience, pursuing a democratization attempt.
反向助跑纵跳所达到的峰值高度是一个公认的运动表现参数。其估算通常借助于测力平台或穿戴式惯性传感器。迄今为止，智能手机或许可用作估算跳跃高度的替代工具，因为其本身就内置了惯性传感器。
为此，43名参与者在两个测力平台（黄金标准）上进行了4次反向助跑纵跳（总共172次）。跳跃过程中，参与者手持一部智能手机，记录其惯性传感器的测量数据。在为两种测量仪器计算出峰值高度后，提取了29个与跳跃生物力学和信号时频特征相关的特征，作为软组织或非自主手臂摆动伪影的潜在描述符。通过从初始数据集中随机选择元素创建了一个训练集（129次跳跃 - 75%），其余的分配给测试集（43次跳跃 - 25%）。仅在训练集上应用套索正则化来减少特征数量，避免可能的多重共线性。训练了一个具有一个隐藏层的多层感知器，用于从减少后的特征集中估算跳跃高度。使用具有5折交叉验证的网格搜索方法对多层感知器进行超参数优化。根据最小负平均绝对误差选择最佳模型。
与原始智能手机测量估算值（分别为18厘米和16厘米）相比，多层感知器极大地提高了测试集估算值的准确性（4厘米）和精度（4厘米）。对训练好的模型进行排列特征重要性分析，以确定每个特征对结果的影响。峰值加速度和制动阶段持续时间是最终模型中最具影响力的特征。尽管通过原始智能手机测量计算出的高度不够准确，但它仍然是最具影响力的特征之一。
该研究实施了一种基于智能手机的跳跃高度估算方法，为向更广泛的受众发布该方法铺平了道路，是一次追求普及化的尝试。
