Wearable Sensors for Activity Recognition in Ultimate Frisbee Using Convolutional Neural Networks and Transfer Learning

A Comparative Study of the Use of Stratified Cross-Validation and Distribution-Balanced Stratified Cross-Validation in Imbalanced Learning

Nowadays, the solution to many practical problems relies on machine learning tools. However, compiling the appropriate training data set for real-world classification problems is challenging because collecting the right amount of data for each class is often difficult or even impossible. In such cases, we can easily face the problem of imbalanced learning. There are many methods in the literature for solving the imbalanced learning problem, so it has become a serious question how to compare the performance of the imbalanced learning methods. Inadequate validation techniques can provide misleading results (e.g., due to data shift), which leads to the development of methods designed for imbalanced data sets, such as stratified cross-validation (SCV) and distribution optimally balanced SCV (DOB-SCV). Previous studies have shown that higher classification performance scores (AUC) can be achieved on imbalanced data sets using DOB-SCV instead of SCV. We investigated the effect of the oversamplers on this difference. The study was conducted on 420 data sets, involving several sampling methods and the DTree, kNN, SVM, and MLP classifiers. We point out that DOB-SCV often provides a little higher F1 and AUC values for classification combined with sampling. However, the results also prove that the selection of the sampler-classifier pair is more important for the classification performance than the choice between the DOB-SCV and the SCV techniques.

xLength: Predicting Expected Ski Jump Length Shortly after Take-Off Using Deep Learning

With tracking systems becoming more widespread in sports research and regular training and competitions, more data are available for sports analytics and performance prediction. We analyzed 2523 ski jumps from 205 athletes on five venues. For every jump, the dataset includes the 3D trajectory, 3D velocity, skis' orientation, and metadata such as wind, starting gate, and ski jumping hill data. Using this dataset, we aimed to predict the expected jump length (xLength) inspired by the expected goals metric in soccer (xG). We evaluate the performance of a fully connected neural network, a convolutional neural network (CNN), a long short-term memory (LSTM), and a ResNet architecture to estimate the xLength. For the prediction of the jump length one second after take-off, we achieve a mean absolute error (MAE) of 5.3 m for the generalization to new athletes and an MAE of 5.9 m for the generalization to new ski jumping hills using ResNet architectures. Additionally, we investigated the influence of the input time after the take-off on the predictions' accuracy. As expected, the MAE becomes smaller with longer inputs. Due to the real-time transmission of the sensor's data, xLength can be updated during the flight phase and used in live TV broadcasting. xLength could also be used as an analysis tool for experts to quantify the quality of the take-off and flight phases.

Leakage Prediction in Machine Learning Models When Using Data from Sports Wearable Sensors

One of the major problems in machine learning is data leakage, which can be directly related to adversarial type attacks, raising serious concerns about the validity and reliability of artificial intelligence. Data leakage occurs when the independent variables used to teach the machine learning algorithm include either the dependent variable itself or a variable that contains clear information that the model is trying to predict. This data leakage results in unreliable and poor predictive results after the development and use of the model. It prevents the model from generalizing, which is required in a machine learning problem and thus causes false assumptions about its performance. To have a solid and generalized forecasting model, which will be able to produce remarkable forecasting results, we must pay great attention to detecting and preventing data leakage. This study presents an innovative system of leakage prediction in machine learning models, which is based on Bayesian inference to produce a thorough approach to calculating the reverse probability of unseen variables in order to make statistical conclusions about the relevant correlated variables and to calculate accordingly a lower limit on the marginal likelihood of the observed variables being derived from some coupling method. The main notion is that a higher marginal probability for a set of variables suggests a better fit of the data and thus a greater likelihood of a data leak in the model. The methodology is evaluated in a specialized dataset derived from sports wearable sensors.