Multivariate time series for testing -- RacketSports dataset

The original data was retrieved from http://www.timeseriesclassification.com/description.php?Dataset=RacketSports Original data description:<br> The data was created by university students plyaing badminton or squash whilst wearing a smart watch (Sony Smart watch 35). The watch relayed the x-y-z coordinates for<br> both the gyroscope and accelerometer to an android phone (One Plus 56). The phone<br> wrote these values to an Attribute-Relation File Format (arff) file using an app developed<br> by a UEA computer science masters student. The problem is to identify which sport and which stroke the players are making. The data was collected at a rate of 10 HZ over 3 seconds whilst the player played<br> either a forehand/backhand in squash or a clear/smash in badminton.<br> The data was collected as part of an undergraduate project by Phillip Perks in 2017/18. Pre-processing<br> Data processing was done as described in: https://github.com/NLeSC/mcfly-tutorial/blob/master/utils/tutorial_racketsports.py<br> The original data was split into train and test set. Here the data was loaded and further divided into train, test, validation sets.<br> To keep it simple we here simply divided the original test part into test and validation.<br> The resulting data was stored as numpy .npy files. The zip file contains three sets of time series data (X_train, X_test, X_valid) and the respective labels (y_train, y_test, y_valid). Reference:<br> http://www.timeseriesclassification.com/description.php?Dataset=RacketSports<br> (The data was collected as part of an undergraduate project by Phillip Perks in 2017/18.)