Multivariate time series for testing -- RacketSports dataset

The original data was retrieved from http://www.timeseriesclassification.com/description.php?Dataset=RacketSports

Original data description:
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
both the gyroscope and accelerometer to an android phone (One Plus 56). The phone
wrote these values to an Attribute-Relation File Format (arff) file using an app developed
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
either a forehand/backhand in squash or a clear/smash in badminton.
The data was collected as part of an undergraduate project by Phillip Perks in 2017/18.

Pre-processing
Data processing was done as described in: https://github.com/NLeSC/mcfly-tutorial/blob/master/utils/tutorial_racketsports.py
The original data was split into train and test set. Here the data was loaded and further divided into train, test, validation sets.
To keep it simple we here simply divided the original test part into test and validation.
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:
http://www.timeseriesclassification.com/description.php?Dataset=RacketSports
(The data was collected as part of an undergraduate project by Phillip Perks in 2017/18.)