Motion-Print Control Task Movements (38 subjects)

This dataset is composed of 4-Dimensional time series files, representing the movements of all 38 participants during a novel control task. In the ‘5D_Data_Extractor.py’ file this can be set up to 6-Dimension, by the ‘fields_included’ variable. Two folders are included, one ready for preprocessing (‘subjects raw’) and the other already preprocessed ‘subjects preprocessed’.In the 'subjects raw' folder there is 1 file per subject, with all 15 runs of the task contained. Each run is separated by a line of all zeros. The raw data is sampled at 100 HZ, meaning 100 samples per second. Given this high sampling rate aggregation is done in preprocessing, to produce the files in the 'subjects preprocessed' folder. Depending on the ‘agg_rate’ given in the ‘5D_Data_Extractor.py’ file, each run at 100 HZ is sampled down by combining every ‘agg_rate’ data points (such as 100, 50 or 20) into one. In the 'subjects preprocessed' folder there are 2 files per subject, one 'TRAIN' and one 'TEST', which have been aggregated and split from the 'subjects raw' folder. These were made using ‘agg_rate’ of 100 (converting them to 1 HZ), ‘fields_included’ of ‘Ys’ and ‘Zs’, and ‘Dists’ and ‘test_runvals’ of 13, 14, 15. This means that of the 15 total runs, those three runs composed the TEST for each subject. The 'TRAIN' files are therefore larger, containing the subjects' movements on runs 1-12 of the task. The 'TRAIN' files were used to fit machine learning models for each subject, and the 'TEST' files were used to validate these models.

本数据集由四维时间序列文件组成，记录了38名参与者在一项新颖的控制任务中的运动轨迹。在'5D_Data_Extractor.py'文件中，通过设置'fields_included'变量，可将维度扩展至六维。数据集包含两个文件夹：一个为待预处理的文件夹（'subjects raw'），另一个为已预处理的文件夹（'subjects preprocessed'）。在'subjects raw'文件夹中，每位参与者对应一个文件，包含该任务的所有15次运行，每次运行之间以全零行分隔。原始数据以100赫兹的采样率进行采集，即每秒100个样本。鉴于高采样率，预处理阶段进行了数据聚合，以生成'subjects preprocessed'文件夹中的文件。根据'5D_Data_Extractor.py'文件中给出的'agg_rate'值，将100赫兹的每次运行中的数据点（如100、50或20）合并为一个样本。在'subjects preprocessed'文件夹中，每位参与者对应两个文件，分别为'TRAIN'和'TEST'文件，它们已从'subjects raw'文件夹中聚合并分割。这些文件是通过设置'agg_rate'为100（将其转换为1赫兹）、'fields_included'为'Ys'和'Zs'，以及'Dists'和'test_runvals'为13、14、15来制作的。这意味着在总共15次运行中，这三个运行组成了每位参与者的测试集。因此，'TRAIN'文件包含的运行次数更多，涵盖了从第1次到第12次运行的任务，这些文件被用于为每位参与者拟合机器学习模型，而'TEST'文件则用于验证这些模型。