GREF Dataset: On the benefit of FMG and EMG sensor fusion for gesture recognition using cross-subject validation
| datacite.relation.references | https://doi.org/10.1109/TNSRE.2025.3543649 | |
| dc.contributor.author | Rohr, Maurice | |
| dc.contributor.author | Haidamous, Jad | |
| dc.contributor.author | Schäfer, Niklas | |
| dc.contributor.author | Schaumann, Stephan | |
| dc.contributor.author | Latsch, Bastian | |
| dc.contributor.author | Kupnik, Mario | |
| dc.contributor.author | Hoog Antink, Christoph | |
| dc.date.accessioned | 2025-04-02T13:57:15Z | |
| dc.date.available | 2025-04-02T13:57:15Z | |
| dc.date.created | 2024 | |
| dc.date.issued | 2025-04-02 | |
| dc.description | The Gesture Recognition using EMG and FMG TU Darmstadt (GREFTUD) dataset contains labeled recordings of electromyography (EMG) and forcemyography recordings of 13 healthy subjects, each of whom performed 66 distinct hand movements. The movements were labeled using a high speed camera and the start and end time (in ms) of each movement were noted manually. The dataset was presented in our paper "On the Benefit of FMG and EMG Sensor Fusion for Gesture Recognition Using Cross-Subject Validation". The file "dataset.hdf5" contains the complete dataset, including the manual labels, but no video data. An example on how to use the dataset can be found in "hdf5_example.ipynb". | de_DE |
| dc.description.version | 1.0 | de_DE |
| dc.identifier.uri | https://tudatalib.ulb.tu-darmstadt.de/handle/tudatalib/4528 | |
| dc.identifier.uri | https://doi.org/10.48328/tudatalib-1707 | |
| dc.language.iso | en | de_DE |
| dc.rights.license | CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0) | |
| dc.subject | Electromyography (EMG) | de_DE |
| dc.subject | Ferroelectrets | de_DE |
| dc.subject | Force myography (FMG) | de_DE |
| dc.subject | Gesture recognition | de_DE |
| dc.subject | Sensor fusion | de_DE |
| dc.subject.classification | 4.41-06 | |
| dc.subject.ddc | 621.3 | |
| dc.title | GREF Dataset: On the benefit of FMG and EMG sensor fusion for gesture recognition using cross-subject validation | de_DE |
| dc.type | Dataset | de_DE |
| dc.type | Software | de_DE |
| dcterms.accessRights | openAccess | |
| person.identifier.orcid | 0000-0002-6053-6558 | |
| person.identifier.orcid | 0009-0005-9768-4916 | |
| person.identifier.orcid | 0000-0001-5433-0273 | |
| person.identifier.orcid | 0009-0001-2705-9833 | |
| person.identifier.orcid | 0000-0001-9929-5694 | |
| person.identifier.orcid | 0000-0003-2287-4481 | |
| person.identifier.orcid | 0000-0001-7948-8181 | |
| tuda.project | DFG | GRK2761 | TP_Kupnik_GRK_2761 | |
| tuda.unit | TUDa |
Files
Original bundle
1 - 4 of 4
| Name | Description | Size | Format | |
|---|---|---|---|---|
| README.md | Instructions and further information | 2.92 KB | Unknown data format | |
| requirements.txt | Required python packages | 34 B | Plain Text | |
| hdf5_example.ipynb | Example code | 5.83 MB | Unknown data format | |
| dataset.hdf5 | Structured dataset in HDF5 format | 658.16 MB |