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https://doi.org/10.5281/zenodo...
Dataset . 2021
License: CC BY
Data sources: Sygma
ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2021
License: CC BY
Data sources: Datacite
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Preliminary Dataset for the evaluation of a framework designed for dataset creation for monitoring simultaneously the operator and remotely operated vehicle for anomalous command detection

Authors: Elia, Rafaella; Theocharides, Theocharis;

Preliminary Dataset for the evaluation of a framework designed for dataset creation for monitoring simultaneously the operator and remotely operated vehicle for anomalous command detection

Abstract

Preliminary Dataset for the evaluation of a framework designed for dataset creation for monitoring simultaneously the human and remotely operated vehicle for anomalous command detection General Information: This dataset consists of data from 6 subjects during a mission with an unmanned aerial vehicle (UAV). Data fusion includes data from the human operator and the UAV following a predefined course. Our goal is the classification of abnormal/involuntary movements of the operator given on the controller of the UAV. Movements were annotated manually based on the videos collected. This dataset was constructed for a preliminary analysis, prior to constructing a larger dataset and the design of the framework for creating such datasets. Data monitored from the human-operator are the: - heart rate (HR, Polar HR Sensor OH1 was used for signal collection)- surface electromyography signals (sEMG) from the right hand only (MYO Armband was used for signals collection). Data from the inertial measurement unit (IMU) of the UAV consists of: - Accelerometer x, y, z- Gyroscope x, y, z- Magnetometer x, y, z- Altitude- Relative height The procedure followed for the data acquisition is described in: Elia, R., Plastiras, G., Pettemeridou, E., Savva, A., & Theocharides, T. (2022). A real-world data collection framework for a fused dataset creation for joint human and remotely operated vehicle monitoring and anomalous command detection. CAAI Transactions on Intelligence Technology, 7(3), 432-445. Dataset In total there are three items that should be downloaded: (1) the zip folder 'Dataset.zip' containing the data, (2) the text file 'Dataset Description.txt' with all the necessary information for each folder, and (3) the text file 'Readme.txt' which contains the same information as those included in this section. Citing the Dataset The dataset is released under a Creative Commons Attribution license, so please cite the dataset if it is used in your work in any form. Published academic papers should use the academic paper citation for our paper. Personal works, such as projects or blog posts, should provide a URL to this Zenodo page, though a reference to our paper would also be appreciated. Academic paper citation: Elia, R., Plastiras, G., Pettemeridou, E., Savva, A., & Theocharides, T. (2022). A real-world data collection framework for a fused dataset creation for joint human and remotely operated vehicle monitoring and anomalous command detection. CAAI Transactions on Intelligence Technology, 7(3), 432-445. Personal use citation: Include a link to this Zenodo page - 10.5281/zenodo.10001011 ACKNOWLEDGMENT This research is funded by the European Union s Horizon 2020 research and innovation program under grant agreement No. 739551 (KIOS CoE) and from the Republic of Cyprus through the Directorate General for European Programs, Coordination and Development. Contact Information If you would like further information about the dataset, or if you experience any issues downloading files, please contact us at relia001@ucy.ac.cy.

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Keywords

flexibility of urban, spatial application, Biophysical data, livability

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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