Data

This module has three data-related functions: (1) Datasets, (2) Offline Data Handling, and (3) Online Data Handling. The dataset functionality has built-in support for downloading and exploring a variety of validated datasets. The OfflineDataHandler is responsible for parsing through previously collected (offline) datasets and preparing data to be passed through the pipeline. Finally, the OnlineDataHandler is responsible for accumulating, storing, and processing real-time data.

Datasets

Several validated datasets consisting of different gestures and recording technology are included in this library. These datasets can be used for exploring the library’s capabilities and for future research. When using the packaged datasets for research purposes, we ask that you reference the original dataset contribution and not just this toolkit (the original dataset contributions might not be obvious since they are included for download with this toolkit).

Classification

OneSubjectMyoDataset

Dataset Description: Simple one subject dataset.

Attribute	Description
Num Subjects:	1
Num Reps:	12 Reps (i.e., 6 Trials x 2 Reps)
Classes:	0 - Hand Open 1 - Hand Close 2 - No Movement 3 - Wrist Extension 4 - Wrist Flexion
Device:	Myo
Sampling Rates:	200 Hz
Auto Download:	True

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list()['OneSubjectMyo']()
odh = dataset.prepare_data()

Dataset Location https://github.com/LibEMG/OneSubjectEMaGerDataset

References:

@ARTICLE{libemg,
  author={Eddy, Ethan and Campbell, Evan and Phinyomark, Angkoon and Bateman, Scott and Scheme, Erik},
  journal={IEEE Access},
  title={LibEMG: An Open Source Library to Facilitate the Exploration of Myoelectric Control},
  year={2023},
  volume={11},
  number={},
  pages={87380-87397},
  keywords={Electromyography;Prosthetics;Libraries;Human computer interaction;Feature extraction;Muscles;Control systems;Gesture recognition;Open source software;EMG;electromyography;toolkit;library;myoelectric control;gesture recognition},
  doi={10.1109/ACCESS.2023.3304544}}

---

DS1: 3DC

Dataset Description: A relatively simple within session baseline.

Attribute	Description
Num Subjects:	22
Num Reps:	4 Training, 4 Testing
Classes:	0 - No Motion 1 - Radial Deviaton 2 - Wrist Flexion 3 - Ulnar Deviaton 4 - Wrist Extension 5 - Supination 6 - Pronation 7 - Power Grip 8- Open Hand 9 - Chuck Grip 10 - Pinch Grip
Device:	Delsys
Sampling Rates:	1000 Hz
Auto Download:	True

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list()['3DC']()
odh = dataset.prepare_data()

Dataset Location https://github.com/LibEMG/3DCDataset

References:

@article{cote2019low,
  title={A low-cost, wireless, 3-D-printed custom armband for sEMG hand gesture recognition},
  author={C{\^o}t{\'e}-Allard, Ulysse and Gagnon-Turcotte, Gabriel and Laviolette, Fran{\c{c}}ois and Gosselin, Benoit},
  journal={Sensors},
  volume={19},
  number={12},
  pages={2811},
  year={2019},
  publisher={MDPI}
}

DS2: ContinuousTransitions

Dataset Description: The testing set in this dataset has continuous transitions between classes, providing a more realistic offline evaluation standard for myoelectric control.

Attribute	Description
Num Subjects:	43
Num Reps:	6 Training (Ramp), 42 Transitions (All combinations of Transitions) x 6 Reps
Classes:	0 - No Motion 1 - Wrist Flexion 2 - Wrist Extension 3 - Wrist Pronation 4 - Wrist Supination 5 - Hand Close 6 - Hand Open
Device:	Delsys
Sampling Rates:	2000 Hz
Auto Download:	False

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list()['ContinuousTransitions']()
odh = dataset.prepare_data()

Dataset Location https://unbcloud-my.sharepoint.com/:f:/g/personal/ecampbe2_unb_ca/EjgjhM9ZHJxOglKoAf062ngBf4wFj2Mn2bORKY1-aMYGRw?e=WkZNwI

References:

@ARTICLE{transitions,
  author={Raghu, Shriram Tallam Puranam and MacIsaac, Dawn and Scheme, Erik},
  journal={IEEE Journal of Biomedical and Health Informatics},
  title={Decision-Change Informed Rejection Improves Robustness in Pattern Recognition-Based Myoelectric Control},
  year={2023},
  volume={27},
  number={12},
  pages={6051-6061},
  doi={10.1109/JBHI.2023.3316599}}

DS3: Contraction Intensity

Dataset Description: A contraction intensity dataset.

Attribute	Description
Num Subjects:	10
Num Reps:	4 Ramp Reps (Train), 4 Reps x 20%, 30%, 40%, 50%, 60%, 70%, 80%, MVC (Test)
Classes:	0 - No Motion 1 - Wrist Flexion 2 - Wrist Extension 3 - Wrist Pronation 4 - Wrist Supination 5 - Chuck Grip 6 - Hand Open
Device:	BE328 by Liberating Technologies, Inc
Sampling Rates:	1000 Hz
Auto Download:	True

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list()['ContractionIntensity']()
odh = dataset.prepare_data()

Dataset Location https://github.com/LibEMG/ContractionIntensity

References:

@article{contraction_intensity,
  title={Training strategies for mitigating the effect of proportional control on classification in pattern recognition--based myoelectric control},
  author={Scheme, Erik and Englehart, Kevin},
  journal={JPO: Journal of Prosthetics and Orthotics},
  volume={25},
  number={2},
  pages={76--83},
  year={2013},
  publisher={LWW}
}

DS4: Electrode Shift

Dataset Description: An electrode shift confounding factors dataset.

Attribute	Description
Num Subjects:	21
Num Reps:	5 Train (Before Shift), 8 Test (After Shift)
Classes:	0 - Close 1 - Open 2 - Rest 3 - Flexion 4 - Extension
Device:	Myo Armband
Sampling Rates:	200 Hz
Auto Download:	True

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list()['ElectrodeShift']()
odh = dataset.prepare_data()

Dataset Location https://github.com/LibEMG/CIILData

References:

@article{ciil_es,
  title={Context-informed incremental learning improves both the performance and resilience of myoelectric control},
  author={Campbell, Evan and Eddy, Ethan and Bateman, Scott and C{\^o}t{\'e}-Allard, Ulysse and Scheme, Erik},
  journal={Journal of NeuroEngineering and Rehabilitation},
  volume={21},
  number={1},
  pages={70},
  year={2024},
  publisher={Springer}
}

DS5: EMGEPN612

Dataset Description: A large 612 user dataset for developing cross-user models.

Attribute	Description
Num Subjects:	612
Num Reps:	50 Reps x 306 Users (Train), 25 Reps x 306 Users (Test) –> Cross User Split
Classes:	0 - No Movement 1 - Hand Close 2 - Flexion 3 - Extension 4 - Hand Open 5 - Pinch
Device:	Myo Armband
Sampling Rates:	200 Hz
Auto Download:	True

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list()['EMGEPN612']() # User Dependent
dataset = get_dataset_list(cross_user=True)['EMGEPN612']() # User Independent
odh = dataset.prepare_data()

Dataset Location https://unbcloud-my.sharepoint.com/:u:/g/personal/ecampbe2_unb_ca/EWf3sEvRxg9HuAmGoBG2vYkBLyFv6UrPYGwAISPDW9dBXw?e=vjCA14

References:

@article{epn,
  title={EMG-EPN-612 Dataset. 2020},
  author={Benalc{\'a}zar, M and Barona, L and Valdivieso, L and Aguas, X and Zea, J},
  journal={DOI: https://doi. org/10.5281/zenodo},
  volume={4027874},
  year={2020}
}

DS6: FORSEMG

Dataset Description: Twelve gestures elicited in three forearm orientations (neutral, pronation, and supination).

Attribute	Description
Num Subjects:	19
Num Reps:	5 Train, 10 Test (2 Forearm Orientations x 5 Reps)
Classes:	0 - Thump Up 1 - Index 2 - Right Angle 3 - Peace 4 - Index Little 5 - Thumb Little 6 - Hand Close 7 - Hand Open 8 - Wrist Flexion 9 - Wrist Extension 10 - Ulnar Deviation 11 - Radial Deviation
Device:	Experimental Device
Sampling Rates:	985 Hz
Auto Download:	False

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list()['FORS-EMG']()
odh = dataset.prepare_data()

Dataset Location https://www.kaggle.com/datasets/ummerummanchaity/fors-emg-a-novel-semg-dataset

References:

@article{fors_emg,
  title={FORS-EMG: A Novel sEMG Dataset for Hand Gesture Recognition Across Multiple Forearm Orientations},
  author={Rumman, Umme and Ferdousi, Arifa and Hossain, Md Sazzad and Islam, Md Johirul and Ahmad, Shamim and Reaz, Mamun Bin Ibne and Islam, Md Rezaul},
  journal={arXiv preprint arXiv:2409.07484},
  year={2024}
}

DS7: Limb Position (Fougner)

Dataset Description: A limb position dataset (with 5 static limb positions).

Attribute	Description
Num Subjects:	12
Num Reps:	10 Reps (Train), 10 Reps x 4 Positions
Classes:	0 - Wrist Flexion 1 - Wrist Extension 2 - Pronation 3 - Supination 4 - Hand Open 5 - Power Grip 6 - Pinch Grip 7 - Rest
Device:	BE328 by Liberating Technologies, Inc.
Sampling Rates:	1000 Hz
Auto Download:	True

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list()['FougnerLP']()
odh = dataset.prepare_data()

Dataset Location https://github.com/LibEMG/LimbPosition

References:

@article{fougner_lp,
  title={Resolving the limb position effect in myoelectric pattern recognition},
  author={Fougner, Anders and Scheme, Erik and Chan, Adrian DC and Englehart, Kevin and Stavdahl, {\O}yvind},
  journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
  volume={19},
  number={6},
  pages={644--651},
  year={2011},
  publisher={IEEE}
}

DS8: GRABMyo

Dataset Description: A large cross-session dataset including 17 gestures elicited across 3 separate sessions.

Attribute	Description
Num Subjects:	43
Num Reps:	7 Train, 14 Test (2 Separate Days x 7 Reps) –> Cross Day Split
Classes:	0 - Lateral Prehension 1 - Thumb Adduction 2 - Thumb and Little Finger Opposition 3 - Thumb and Index Finger Opposition 4 - Thumb and Index Finger Extension 5 - Thumb and Little Finger Extension 6 - Index and Middle Finger Extension 7 - Little Finger Extension 8 - Index Finger Extension 9 - Thumb Finger Extension 10 - Wrist Extension 11 - Wrist Flexion 12 - Forearm Supination 13 - Forearm Pronation 14 - Hand Open 15 - Hand Close 16 - Rest
Device:	EMGUSB2+ device (OT Bioelletronica, Italy)
Sampling Rates:	2048 Hz
Auto Download:	False

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list()['GRABMyoBaseline']() # Baseline
dataset = get_dataset_list()['GRABMyoCrossDay']() # CrossDay
odh = dataset.prepare_data()

Dataset Location https://physionet.org/content/grabmyo/1.0.2/

References:

@article{grabmyo,
  title={Multi-day dataset of forearm and wrist electromyogram for hand gesture recognition and biometrics},
  author={Pradhan, Ashirbad and He, Jiayuan and Jiang, Ning},
  journal={Scientific data},
  volume={9},
  number={1},
  pages={733},
  year={2022},
  publisher={Nature Publishing Group UK London}
}

DS9: HyserPR

Dataset Description: High Density Hyser pattern recognition (PR) dataset. Includes dynamic and maintenance tasks for 34 hand gestures.

Attribute	Description
Num Subjects:	18
Num Reps:	1 Train, 1 Test (Consisting of dynamic and maintanance tasks)
Classes:	1 - Thumb Extension 2 - Index Finger Extension 3 - Middle Finger Extension 4 - Ring Finger Extension 5 - Little Finger Extension 6 - Wrist Flexion 7 - Wrist Extension 8 - Wrist Radial 9 - Wrist Ulnar 10 - Wrist Pronation 11 - Wrist Supination 12 - Extension of Thumb and Index Fingers 13 - Extension of Index and Middle Fingers 14 - Wrist Flexion Combined with Hand Close 15 - Wrist Extension Combined with Hand Close 16 - Wrist Radial Combined with Hand Close 17 - Wrist Ulnar Combined with Hand Close 18 - Wrist Pronation Combined with Hand Close 19 - Wrist Supination Combined with Hand Close 20 - Wrist Flexion Combined with Hand Open 21 - Wrist Extension Combined with Hand Open 22 - Wrist Radial Combined with Hand Open 23 - Wrist Ulnar Combined with Hand Open 24 - Wrist Pronation Combined with Hand Open 25 - Wrist Supination Combined with Hand Open 26 - Extension of Thumb, Index and Middle Fingers 27 - Extension of Index, Middle and Ring Fingers 28 - Extension of Middle, Ring and Little Fingers 29 - Extension of Index, Middle, Ring and Little Fingers 30 - Hand Close 31 - Hand Open 32 - Thumb and Index Fingers Pinch 33 - Thumb, Index and Middle Fingers Pinch 34 - Thumb and Middle Fingers Pinch
Device:	OT Bioelettronica Quattrocento
Sampling Rates:	2048 Hz
Auto Download:	False

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list()['HyserPR']()
odh = dataset.prepare_data()

Dataset Location https://www.physionet.org/content/hd-semg/2.0.0/

References:

@ARTICLE{hyser,
  author={Jiang, Xinyu and Liu, Xiangyu and Fan, Jiahao and Ye, Xinming and Dai, Chenyun and Clancy, Edward A. and Akay, Metin and Chen, Wei},
  journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
  title={Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings},
  year={2021},
  volume={29},
  number={},
  pages={1035-1046},
  doi={10.1109/TNSRE.2021.3082551}}

DS10: Single-User Multi-Day (KaufmannMD)

Dataset Description: A single subject, multi-day (120 days) collection.

Attribute	Description
Num Subjects:	1
Num Reps:	1 rep per day, 120 days total. 60/60 train-test split
Classes:	0 - No Motion 1 - Wrist Extension 2 - Wrist Flexion 3 - Wrist Adduction 4 - Wrist Abduction 5 - Wrist Supination 6 - Wrist Pronation 7 - Hand Open 8 - Hand Closed 9 - Key Grip 10 - Index Point
Device:	MindMedia
Sampling Rates:	2048 Hz
Auto Download:	True

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list()['KaufmannMD']()
odh = dataset.prepare_data()

Dataset Location https://github.com/LibEMG/MultiDay

References:

@INPROCEEDINGS{kaufmann,
  author={Kaufmann, Paul and Englehart, Kevin and Platzner, Marco},
  booktitle={2010 Annual International Conference of the IEEE Engineering in Medicine and Biology},
  title={Fluctuating emg signals: Investigating long-term effects of pattern matching algorithms},
  year={2010},
  volume={},
  number={},
  pages={6357-6360},
  doi={10.1109/IEMBS.2010.5627288}}

DS11: Minimal Training Data

Dataset Description: The goal of this Myo dataset is to explore how well models perform when they have a limited amount of training data (1s per class).

Attribute	Description
Num Subjects:	11
Num Reps:	1 Train, 15 Test
Classes:	0 - Close 1 - Open 2 - Rest 3 - Flexion 4 - Extension
Device:	Myo Armband
Sampling Rates:	200 Hz
Auto Download:	True

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list()['MinimalTrainingData']()
odh = dataset.prepare_data()

Dataset Location https://github.com/LibEMG/CIILData

References:

@inproceedings{ciil_md,
  title={Leveraging task-specific context to improve unsupervised adaptation for myoelectric control},
  author={Eddy, Ethan and Campbell, Evan and Bateman, Scott and Scheme, Erik},
  booktitle={2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
  pages={4661--4666},
  year={2023},
  organization={IEEE}
}

DS12: NinaProDB2

Dataset Description: The Ninapro DB2 is a dataset that can be used to test how algorithms perform for large gesture sets. The dataset contains 6 repetitions of 50 motion classes (plus optional rest) that were recorded using 12 Delsys Trigno electrodes around the forearm.

Attribute	Description
Num Subjects:	40
Num Reps:	6
Classes:	50 Nina Pro DB2
Device:	Delsys
Sampling Rates:	2000 Hz
Auto Download:	False

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list()['NinaProDB2']()
odh = dataset.prepare_data()

Dataset Location Note, this dataset will not be automatically downloaded. To download this dataset, please see Nina DB2. Simply download the ZIPs and place them in a folder and LibEMG will handle the rest. All credit for this dataset should be given to the original authors.

References:

@article{db2,
  title={Electromyography data for non-invasive naturally-controlled robotic hand prostheses},
  author={Atzori, Manfredo and Gijsberts, Arjan and Castellini, Claudio and Caputo, Barbara and Hager, Anne-Gabrielle Mittaz and Elsig, Simone and Giatsidis, Giorgio and Bassetto, Franco and M{\"u}ller, Henning},
  journal={Scientific data},
  volume={1},
  number={1},
  pages={1--13},
  year={2014},
  publisher={Nature Publishing Group}
}

---

DS13: Limb Position (Radmand)

Dataset Description: A large limb position dataset (with 16 static limb positions).

Attribute	Description
Num Subjects:	10
Num Reps:	4 Reps (Train), 4 Reps x 15 Positions
Classes:	Mapping is Uncertain
Device:	DelsysTrigno
Sampling Rates:	1000 Hz
Auto Download:	True

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list()['RadmandLP']()
odh = dataset.prepare_data()

Dataset Location https://github.com/LibEMG/LimbPosition

References:

@INPROCEEDINGS{radmand_lp,
  author={Radmand, A. and Scheme, E. and Englehart, K.},
  booktitle={2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
  title={A characterization of the effect of limb position on EMG features to guide the development of effective prosthetic control schemes},
  year={2014},
  volume={},
  number={},
  pages={662-667},
  keywords={},
  doi={10.1109/EMBC.2014.6943678}}

DS14: Pre/Post TMR

Dataset Description: 6 subjects, 8 reps, 24 motions, pre/post intervention.

Attribute	Description
Num Subjects:	6
Num Reps:	8 reps per motion (pre/post intervention)
Classes:	0 - Hand Open 1 - Key Grip 2 - Power Grip 3 - Fine Pinch Opened 4 - Fine Pinch Closed 5 - Tripod Opened 6 - Tripod Closed 7 - Tool 8 - Hook 9 - Index Point 10 - Thumb Flexion 11 - Thumb Extension 12 - Thumb Abduction 13 - Thumb Adduction 14 - Index Flexion 15 - Ring Flexion 16 - Pinky Flexion 17 - Wrist Supination 18 - Wrist Pronation 19 - Wrist Flexion 20 - Wrist Extension 21 - Radial Deviation 22 - Ulnar Deviation 23 - No Motion
Device:	Ag/AgCl
Sampling Rates:	1000 Hz
Auto Download:	True

Using the Dataset:

from libemg.datasets import *
dataset_pre = get_dataset_list()['TMR_Pre']()
dataset_post = get_dataset_list()['TMR_Post']()
odh = dataset_pre.prepare_data()

Dataset Location https://github.com/LibEMG/TMR_ShirleyRyanAbilityLab

References:

@article{tmr,
  title={Myoelectric prosthesis hand grasp control following targeted muscle reinnervation in individuals with transradial amputation},
  author={Simon, Ann M and Turner, Kristi L and Miller, Laura A and Dumanian, Gregory A and Potter, Benjamin K and Beachler, Mark D and Hargrove, Levi J and Kuiken, Todd A},
  journal={PloS one},
  volume={18},
  number={1},
  pages={e0280210},
  year={2023},
  publisher={Public Library of Science San Francisco, CA USA}
}

DS15: Weakly Supervised

Dataset Description: A weakly supervised environment with sparse supervised calibration.

Attribute	Description
Num Subjects:	16
Num Reps:	30 min weakly supervised, 1 rep calibration, 14 reps test
Classes:	0 - Close 1 - Open 2 - Rest 3 - Flexion 4 - Extension
Device:	OyMotion gForcePro+ EMG Armband
Sampling Rates:	1000 Hz
Auto Download:	True

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list('WEAKLYSUPERVISED')['WeaklySupervised']()
odh = dataset.prepare_data()

Dataset Location https://github.com/LibEMG/WS_CIIL

References:

@ARTICLE{10802919,
  author={Campbell, Evan and Eddy, Ethan and Isabel, Xavier and Bateman, Scott and Gosselin, Benoit and Côté-Allard, Ulysse and Scheme, Erik},
  journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
  title={Screen Guided Training Does Not Capture Goal-Oriented Behaviors: Learning Myoelectric Control Mappings From Scratch Using Context Informed Incremental Learning},
  year={2025},
  volume={33},
  number={},
  pages={332-342},
  keywords={Training;Electromyography;Games;Data models;Calibration;Adaptation models;Context modeling;Predictive models;Pipelines;Wrist;Electromyography (EMG);deep learning;incremental learning;gesture recognition;zero-shot},
  doi={10.1109/TNSRE.2024.3518059}}

Regression

OneSubjectEmaGEr

Dataset Description: Simple one subject regression dataset.

Attribute	Description
Num Subjects:	1
Num Reps:	5 Reps
Classes:	0: Hand Close (-) / Hand Open (+) Pronation (-) / Supination (+)
Device:	EmaGEr
Sampling Rates:	1010 Hz
Auto Download:	True

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list('REGRESSION')['OneSubjectEMaGer']()
odh = dataset.prepare_data()

Dataset Location https://github.com/LibEMG/OneSubjectEMaGerDataset

References:

@ARTICLE{libemg,
  author={Eddy, Ethan and Campbell, Evan and Phinyomark, Angkoon and Bateman, Scott and Scheme, Erik},
  journal={IEEE Access},
  title={LibEMG: An Open Source Library to Facilitate the Exploration of Myoelectric Control},
  year={2023},
  volume={11},
  number={},
  pages={87380-87397},
  doi={10.1109/ACCESS.2023.3304544}}

DS16: EMG2POSE

Dataset Description: A large dataset from ctrl-labs (Meta) for joint angle estimation. Note that not all subjects have all stages.

Attribute	Description
Num Subjects:	193
Num Reps:	N/A
Classes:	FingerPinches1 - AllFingerPinchesThumbSwipeThumbRotate Object1 - CoffeePanicPete Counting1 - CountingUpDownFaceSideAway Counting2 - CountingUpDownFingerWigglingSpreading DoorknobFingerGraspFistGrab - DoorknobFingerGraspFistGrab Throwing - FastPongFronthandBackhandThrowing Abduction - FingerAbductionSeries FingerFreeform - FingerFreeform FingerPinches2 - FingerPinchesSingleFingerPinchesMultiple HandHandInteractions - FingerTouchPalmClapmrburns Wiggling1 - FingerWigglingSpreading Punch - GraspPunchCloseFar Gesture1 - HandClawGraspFlicks StaticHands - HandDeskSeparateClaspedChest FingerPinches3 - HandOverHandAllFingerPinchesThumbSwipeThumbRotate Wiggling2 - HandOverHandCountingUpDownFingerWigglingSpreading Unconstrained - unconstrained Gesture2 - HookEmHornsOKScissors FingerPinches4 - IndexPinchesMiddlePinchesThumbswipes Pointing - IndividualFingerPointingSnap Freestyle1 - OneHandedFreeStyle Object2 - PlayBlocksChess Draw - PokeDrawPinchRotateclosefar Poke - PokePinchCloseFar Gesture3 - ShakaVulcanPeace ThumbsSwipes - ThumbsSwipesWholeHand ThumbRotations - ThumbsUpDownThumbRotationsCWCCWP Freestyle2 - TwoHandedFreeStyle WristFlex - WristFlexionAbduction
Device:	Ctrl Labs Armband
Sampling Rates:	2000 Hz
Auto Download:	False

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list('REGRESSION')['EMG2POSE']() # Within USer
dataset = get_dataset_list('REGRESSION', cross_user=True)['EMG2POSE']() # Cross User
odh = dataset.prepare_data()

Dataset Location https://fb-ctrl-oss.s3.amazonaws.com/emg2pose/emg2pose_dataset.tar

References:

@inproceedings{salteremg2pose,
  title={emg2pose: A Large and Diverse Benchmark for Surface Electromyographic Hand Pose Estimation},
  author={Salter, Sasha and Warren, Richard and Schlager, Collin and Spurr, Adrian and Han, Shangchen and Bhasin, Rohin and Cai, Yujun and Walkington, Peter and Bolarinwa, Anuoluwapo and Wang, Robert and others},
  booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track}
}

DS17: Hyser1DOF

Dataset Description: Hyser 1 DOF dataset. Includes within-DOF finger movements. Ground truth finger forces are recorded for use in finger force regression.

Attribute	Description
Num Subjects:	20
Num Reps:	3
Classes:	1 - Thumb 2 - Index 3 - Middle 4 - Ring 5 - Little
Device:	OT Bioelettronica Quattrocento
Sampling Rates:	2048 Hz
Auto Download:	False

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list('REGRESSION')['Hyser1DOF']()
odh = dataset.prepare_data()

Dataset Location https://www.physionet.org/content/hd-semg/2.0.0/

References:

@ARTICLE{hyser,
  author={Jiang, Xinyu and Liu, Xiangyu and Fan, Jiahao and Ye, Xinming and Dai, Chenyun and Clancy, Edward A. and Akay, Metin and Chen, Wei},
  journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
  title={Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings},
  year={2021},
  volume={29},
  number={},
  pages={1035-1046},
  doi={10.1109/TNSRE.2021.3082551}}

DS18: HyserNDOF

Dataset Description: Hyser N DOF dataset. Includes combined finger movements. Ground truth finger forces are recorded for use in finger force regression.

Attribute	Description
Num Subjects:	20
Num Reps:	2
Classes:	1 - Thumb + Index 2 - Thumb + Middle 3 - Thumb + Ring 4 - Thumb + Little 5 - Index + Middle 6 - Thumb + Index + Middle 7 - Index + Middle + Ring 8 - Middle + Ring + Little 9 - Index + Middle + Ring + Little 10 - All Fingers 11 - Thumb + Index (Opposing) 12 - Thumb + Middle (Opposing) 13 - Thumb + Ring (Opposing) 14 - Thumb + Little (Opposing) 15 - Index + Middle (Opposing)
Device:	OT Bioelettronica Quattrocento
Sampling Rates:	2048 Hz
Auto Download:	False

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list('REGRESSION')['HyserNDOF']()
odh = dataset.prepare_data()

Dataset Location https://www.physionet.org/content/hd-semg/2.0.0/

References:

@ARTICLE{hyser,
  author={Jiang, Xinyu and Liu, Xiangyu and Fan, Jiahao and Ye, Xinming and Dai, Chenyun and Clancy, Edward A. and Akay, Metin and Chen, Wei},
  journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
  title={Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings},
  year={2021},
  volume={29},
  number={},
  pages={1035-1046},
  doi={10.1109/TNSRE.2021.3082551}}

DS19: HyserRandom

Dataset Description: Hyser random dataset. Includes random motions performed by users. Ground truth finger forces are recorded for use in finger force regression.

Attribute	Description
Num Subjects:	19
Num Reps:	5
Classes:	Random
Device:	OT Bioelettronica Quattrocento
Sampling Rates:	2048 Hz
Auto Download:	False

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list('REGRESSION')['HyserRandom']()
odh = dataset.prepare_data()

Dataset Location https://www.physionet.org/content/hd-semg/2.0.0/

References:

@ARTICLE{hyser,
  author={Jiang, Xinyu and Liu, Xiangyu and Fan, Jiahao and Ye, Xinming and Dai, Chenyun and Clancy, Edward A. and Akay, Metin and Chen, Wei},
  journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering},
  title={Open Access Dataset, Toolbox and Benchmark Processing Results of High-Density Surface Electromyogram Recordings},
  year={2021},
  volume={29},
  number={},
  pages={1035-1046},
  doi={10.1109/TNSRE.2021.3082551}}

DS20: NinaProDB8

Attribute	Description
Num Subjects:	12
Num Reps:	20 Training, 2 Testing
Classes:	9 NinaProDB8
Device:	Delsys
Sampling Rates:	1111 Hz
Auto Download:	False

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list('REGRESSION')['NinaProDB8']()
odh = dataset.prepare_data()

Dataset Location Note, this dataset will not be automatically downloaded. To download this dataset, please see Nina DB8. Simply download the ZIPs and place them in a folder and LibEMG will handle the rest. All credit for this dataset should be given to the original authors.

References:

@article{db8,
  title={Effect of user practice on prosthetic finger control with an intuitive myoelectric decoder},
  author={Krasoulis, Agamemnon and Vijayakumar, Sethu and Nazarpour, Kianoush},
  journal={Frontiers in neuroscience},
  volume={13},
  pages={891},
  year={2019},
  publisher={Frontiers Media SA}
}

DS21: UserCompliance

Dataset Description: Regression dataset used for investigation into user compliance during mimic training.

Attribute	Description
Num Subjects:	6
Num Reps:	5
Classes:	0 - Hand Close (-) / Hand Open (+) 1 - Pronation (-) / Supination (+)
Device:	EMaGer
Sampling Rates:	1010 Hz
Auto Download:	True

Using the Dataset:

from libemg.datasets import *
dataset = get_dataset_list('REGRESSION')['UserCompliance']()
odh = dataset.prepare_data()

Dataset Location https://github.com/LibEMG/UserComplianceDataset

References:

@inproceedings{morrell2024exploring,
  title={Exploring user compliance in the training of regression-based myoelectric control},
  author={Morrell, Christian and Campbell, Evan and Scheme, Erik},
  booktitle={Myoelectric Controls Symposium},
  year={2024}
}

Offline Data Handler

One overhead for most EMG projects is interfacing with a particular dataset since they often have different folder and file structures. LibEMG provides a means to quickly interface datasets so you can focus on using them with minimal setup time. Assuming the files in the dataset are well formatted (i.e., they include all metadata such as rep, class, and subject) and are either .csv or .txt files, the OfflineDataHandler does all accumulation and processing. To do this, LibEMG relies on regular expressions to define a dataset’s file and folder structure. These expressions can be used to create a dictionary that is passed to the OfflineDataHandler. Once the data handler has collected all the files that satisfy the regexes, the dataset can be sliced using the metadata tags (e.g., by rep, subjects, classes, etc.). After extracting the data it is ready to be passed through the rest of the pipeline. The following code snippet exemplifies how to process a dataset with testing/training, rep, and class metadata. In this case the file format is: dataset/train/R_1_C_1_EMG.csv where R is the rep and C is the class.

Example Code

from libemg.data_handler import OfflineDataHandler, RegexFilter
dataset_folder = 'dataset'
regex_filters = [
    RegexFilter(left_bound = "dataset/", right_bound="/", values = sets_values, description='sets'),
    RegexFilter(left_bound = "_C_", right_bound="_EMG.csv", values = classes_values, description='classes'),
    RegexFilter(left_bound = "R_", right_bound="_C_", values = reps_values, description='reps')
]
odh = OfflineDataHandler()
odh.get_data(folder_location=dataset_folder, regex_filters=regex_filters, delimiter=",")

# Extract training data:
train_odh = odh.isolate_data(key="sets", values=[0])
train_windows, train_metadata = train_odh.parse_windows(50,25)

# Extract features
fe = FeatureExtractor()
feature_list = fe.get_feature_list()
training_features = fe.extract_features(feature_list, train_windows)

Online Data Handler

One complication when using EMG devices is the lack of standardization, meaning that interfacing with hardware is a new undertaking for each device. A goal of LibEMG is to abstract these differences and enable a hardware-agnostic framework. Therefore, this module acts as a middle layer for processing real-time data streaming from any device. In this architecture - exemplified in Figure 1 – live data streaming is performed by using a shared memory buffer as the core. This shared memory buffer is created by the device streamer, where a process is spawned that continuously populates the buffer with samples. Other modules can gain access to the shared memory buffer using the shared memory items that the streamer returned, allowing for cross-process, low-latency, non-blocking access to the data of interest. We provide an OnlineDataHandler object that is a generic object for attaching to the shared memory buffer with added some utilities.

An example of the online data streaming workflow is provided below:

# start the hardware streamer, receive a process handle and shared memory descriptors
streamer_process, shared_memory_items = libemg.streamers.myo_streamer()
# start an online data handler to attach to the shared memory buffer
odh = libemg.data_handler.OnlineDataHandler(shared_memory_items=shared_memory_items)
# start logging the data to a file
odh.log_to_file()
# start visualizing the data
odh.visualization()

Figure 1: OnlineDataHandler Architecture

For more information on the default streamers and creating your own, please reference the Supported Hardware section.