Ground Truths and Predictions
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Format This dataset contains the ground truths and predictions of all deep learning models from Sîmpetru et al (<sub>https://doi.org/10.1101/2022.07.29.502064</sub>). Fig. 2-4 The data is formatted as follows: The data used for <em>Fig. 2</em> is stored under <strong>3D_points_{random, testset}</strong> depending on whether the AI was tested with the random or the test set. The data used for <em>Fig. 3</em> is stored under <strong>angle_{random, test set}</strong> depending on whether the AI was tested with the random or the test set. The data used for <em>Fig. 4</em> is stored under <strong>force</strong>. Each of these folders contains the predicted and expected data for each of the 13 subjects stored as <strong>Subject{1, ..., 13}_{predicted, expected}.pkl</strong>. Fig. 5 The data for Fig. 5 is stored under <strong>comparison</strong>. <br> To distinguish between the EMG forms used, we use: 5 Hz low-pass filtered as <strong>3D_points_5</strong> 5 Hz low-pass filtered and rectified as <strong>3D_points_5_rectified</strong> 20 Hz low-pass filtered as <strong>3D_points_20</strong> 20 Hz low-pass filtered and rectified as <strong>3D_points_20_rectified</strong> raw as <strong>3D_points_raw</strong> raw and rectified as <strong>3D_points_raw_rectified</strong> raw and 20 Hz low-pass filtered combined as <strong>3D_points_raw20</strong> <br> Each of these folders contains the predicted and expected data for each of the 4 subjects, stored as <strong>Subject{1, ..., 4}_{predicted, expected}.pkl</strong>.<br> General file format The saved <strong>.pkl</strong> files are tables where the columns represent the joint labels (<em>Fig. 1B</em>) and the rows represent the bin number. <br> Each cell of the table contains a series of 3 values representing the x, y and z coordinates of the respective joint label at a given time.<br> <br> Loading The stored files are <em>pandas</em> (high-level python library for tables manipulation) dataframes stored using <em>pickle</em> (python object serialisation library).<br> <br> To read such a file, we give the following minimal example:<br> <br> import pandas as pd<br> <br> dataframe = pd.read_pickle("PATH_TO_FILE.pkl")<br> <br> # display head (top 5 rows) of the dataframe<br> print(dataframe.head())<br>
提供机构：
Sîmpetru, Raul
创建时间：
2022-08-12
