EMBCDataset#

class myoverse.datasets.defaults.EMBCDataset(emg_data_path, ground_truth_data_path, save_path, emg_data={}, ground_truth_data={}, tasks_to_use=('Change Me',), debug_level=0, silence_zarr_warnings=False)[source]#

Official dataset maker for the EMBC paper [1].

Parameters:

  • emg_data_path (Path) – The path to the pickle file containing the EMG data. This should be a dictionary with the keys as the tasks in tasks_to_use and the values as the EMG data. The EMG data should be of shape (320, samples).

  • ground_truth_data_path (Path) – The path to the pickle file containing the ground truth data. This should be a dictionary with the keys as the tasks in tasks_to_use and the values as the ground truth data. The ground truth data should be of shape (21, 3, samples).

  • save_path (Path) – The path to save the dataset to. This should be a zarr file.

  • emg_data (dict[str, np.ndarray], optional) – Optional dictionary containing EMG data if not loading from a file.

  • ground_truth_data (dict[str, np.ndarray], optional) – Optional dictionary containing ground truth data if not loading from a file.

  • tasks_to_use (Sequence[str], optional) – The tasks to use.

  • debug_level (int, optional) – Debug level (0-2). Default is 0 (no debugging).

  • silence_zarr_warnings (bool, optional) – Whether to silence all Zarr-related warnings. Default is False.

create_dataset()[source]#

Creates the dataset.

References

[1] Sîmpetru, R.C., Osswald, M., Braun, D.I., Souza de Oliveira, D., Cakici, A.L., Del Vecchio, A., 2022. Accurate Continuous Prediction of 14 Degrees of Freedom of the Hand from Myoelectrical Signals through Convolutive Deep Learning, in: Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) pp. 702–706. https://doi.org/10/gq2f47

Methods

__init__(emg_data_path, ...[, emg_data, ...])

create_dataset()